![](\"https://images.prismic.io/encord/1c077c02-5274-459a-b088-8cc8cd46759c_05.gif?auto=compress,format\")
Frequently asked questions
"},"theme":"light"},"items":[{"heading":{"text":"What does the interview process look like?","html":"What does the interview process look like?
"},"description":{"text":"It varies by role and team, but the general shape is: recruiter screen, hiring manager conversation, a skills-based interview or take-home, and a final panel. We try to keep the process to four or five stages total. Your recruiter will walk you through the specific steps at the start of your process so there are no surprises.","html":"It varies by role and team, but the general shape is: recruiter screen, hiring manager conversation, a skills-based interview or take-home, and a final panel. We try to keep the process to four or five stages total. Your recruiter will walk you through the specific steps at the start of your process so there are no surprises.
"}},{"heading":{"text":"Can I reapply if I've been rejected before?","html":"Can I reapply if I've been rejected before?
"},"description":{"text":"Absolutely! Timing matters, and so does role fit. If you were strong but not the right match for a specific position, we encourage you to apply again when a better-aligned role opens up. We keep notes and context from previous conversations, so you won't be starting from scratch. As a general guideline, we recommend waiting 6 months to 1 year before reapplying. The exact window depends on the feedback you received and the skillsets required for the role.","html":"Absolutely! Timing matters, and so does role fit. If you were strong but not the right match for a specific position, we encourage you to apply again when a better-aligned role opens up. We keep notes and context from previous conversations, so you won't be starting from scratch. As a general guideline, we recommend waiting 6 months to 1 year before reapplying. The exact window depends on the feedback you received and the skillsets required for the role.
"}},{"heading":{"text":"Do you have internship or early-career programs?","html":"Do you have internship or early-career programs?
"},"description":{"text":"We don't have a formal internship program at this stage, but we do hire ambitious early-career candidates across functions. On the GTM side, our Commercial Associate program is designed as a launchpad into sales or solutions. On the technical side, we hire early-career engineers and ML practitioners who want to work at the frontier of AI infrastructure. If you're excited about what we're building and are earlier in your journey, reach out!","html":"We don't have a formal internship program at this stage, but we do hire ambitious early-career candidates across functions. On the GTM side, our Commercial Associate program is designed as a launchpad into sales or solutions. On the technical side, we hire early-career engineers and ML practitioners who want to work at the frontier of AI infrastructure. If you're excited about what we're building and are earlier in your journey, reach out!
"}},{"heading":{"text":"Do you sponsor visas?","html":"Do you sponsor visas?
"},"description":{"text":"We do consider visa sponsorship on a case-by-case basis. It depends on the role, level, and location. This is something we confirm early in the process, typically at the recruiter screen stage, so there's no ambiguity later. If sponsorship is a factor for you, just let your recruiter know upfront.","html":"We do consider visa sponsorship on a case-by-case basis. It depends on the role, level, and location. This is something we confirm early in the process, typically at the recruiter screen stage, so there's no ambiguity later. If sponsorship is a factor for you, just let your recruiter know upfront.
"}}]}]},"uid":"careers","_previewable":"ac5KshEAACAAuLZM"},"allPrismicBlog":{"edges":[{"node":{"uid":"kings-college-london-customer-interview","data":{"author":{"document":{"id":"7eacb635-8381-5dfe-bd83-7f2340c17caf","uid":"ulrik-stig-hansen","data":{"full_name":{"text":"Ulrik Stig Hansen"}}}},"content":{"html":"King’s College Hospital’s Director for Gastroenterology and Endoscopy Dr. Bu Hayee is– to put it mildly– a busy man. As a practising physician and assistant professor, he has a number of competing interests: academic, strategic, research, and clinical.
In addition to supervising students and treating patients, he works closely with NHS England on their digital pathways and transformations, and he has a deep interest in how emerging technologies such as artificial intelligence and computer vision can increase the efficiency of patient care.
In 2020, Dr. Hayee assisted in supervising a collaboration between King’s College London and Encord– the platform for data-centric computer vision– which aimed to increase the speed at which doctors could annotate polyps in colonoscopy videos (training data for computer vision models detecting precancerous polyps).
Polyp detection in action
We recently sat down with Dr. Bu Hayee to hear more about his thoughts on the role of deep learning in medical diagnosis, his collaboration with Encord, and the changing landscape of medical AI.
The following interview has been edited for clarity and length.
Dr. Hayee: It’s very new. We only began to see the practical application of medical AI in my speciality about three years ago. Endoscopy lends itself extremely well to computer vision because it's a very image oriented speciality. The diagnosis and evaluation of patients and the conditions they might have relies on image analysis, so computer vision is gaining a lot of traction in the field.
Although I have had a longstanding interest in machine learning and how it can be leveraged to provide better patient care, unfortunately, until very recently, these kinds of machine learning projects have been slow to deliver because training a model requires millions and millions of data points.
Preparing that data requires laborious image annotation and time-intensive quality control. These barriers make it difficult to move a model out of the research stage and into production. These roadblocks also mean that this technology has mostly been the domain of large industries as opposed to other, smaller actors that have been dissuaded from pursuing deep learning because of the challenges associated with training data curation and model development.
Dr. Hayee: It’s mental torture. Annotation is simply one of the most painful tasks that we have to perform in image based research.
Labelling data is time consuming, and it requires the attention of someone who understands the subject well enough to annotate the images. Reliable and highly accurate annotations are the bedrock of the machine learning process, so labelling has to be done properly. One option is to outsource the images to a company that provides annotation services, but since these annotators have no background in medicine, clinicians must educate them about the data which is in itself a labour intensive effort. Alternatively, a hospital can pay a highly trained medical professional with two postgraduate degrees to sit in front of a screen and click a mouse for hours, which is not an efficient or cost effective use of time.
Dr. Hayee: The built-in tools allowed us to rapidly annotate large numbers of data points. Before, using clinicians to annotate precancerous polyp videos had prohibitively high costs because of the large number of datasets needed to train the model. With Encord, we annotated the data over six times faster than using traditional methods.
That’s a fantastic result because at those rates we can still deploy highly qualified medical professionals to annotate data, but we don’t take up a huge amount of their time. Much of the task shifts to quality assurance, which is much quicker than annotating, because Encord’s micromodels automatically generated 97 percent of the labels. Rather than acting as annotators, we become the humans-in-the-loop who review the machine’s output, in this case labels, for accuracy.
Compared to other label assistance tools that we’ve worked with, Encord’s user experience is much smoother. The interface is intuitive, so it requires very little training to become a sort of “expert” at using all the functionality.
That’s what you want, right? The mark of a great product is in many ways its useability. For instance, you plug in your smartphone, it works, and you don’t need much training to operate it. That’s the kind of UX we’ve come to expect in the 21st century. When it comes to implementing medical AI, we’ll need that kind of experience because doctors aren’t programmers. We’re end users, and we don’t want to have to learn to code to use the product effectively. We want a product that compliments our medical expertise and enhances our medical practice without requiring us to become computer science experts.
![](\"https://images.prismic.io/encord/28482384-d060-455b-860e-814e8652628d_06.png?auto=compress,format\")
Encord's tools in action
Dr. Hayee: In short, time– time for both patients and clinicians. We’ll be able to give patients real-time evolutions of what we’re seeing on the screen. We’ll be able to detect abnormalities visually with greater reliability and greater speed rather than having to wait for a microscopic confirmation. When it comes to patient care, physicians will have more time to dedicate to a care plan because we won’t have to depend on the current pathways that we use for diagnosis, such as waiting for a histology on a biopsy we’ve taken to come back from the lab, before we make a care management plan.
AI will be incredibly useful in decision support. It’s not a decision replacement. The machine won’t replace the doctor and diagnose the patient itself, but it will provide a second pair of eyes, so to speak, for an already educated clinician. The machine can confirm the doctor's diagnosis, increasing the physician’s confidence so that they can make a management plan and treat the patient straight away. Alternatively, if the machine detects something different from what the doctor had thought, the doctor can take a bit more care, be a bit more cautious, and investigate a bit more thoroughly before making a diagnosis and devising a management plan. Both of those scenarios are positive in my book.
Dr. Hayee: I think there’s some confusion around the technology’s role. Even calling this technology AI unnecessarily raises some hackles because a lot of people associate AI with The Terminator and iRobot– two of the worst movies to have brought AI into the common consciousness because they perpetuate such a negative, unrealistic view of the technology and what it does.
When I talk to people about AI, I say: ‘Listen, these aren’t thinking machines or all-seeing eyes. They aren’t even as smart as a three-year-old child putting blocks into a hole. They’re not going to replace doctors or nurses.’
Medical professionals will become comfortable working daily with AI. In time, computer vision will become what our Texas Instrument calculators are to us now. It’ll make us better and faster at our jobs. I can do simple addition without my calculator, but the calculator augments what I do and improves my performance. That’s what AI is going to do for healthcare. The clinician will always be the person who decides, but the AI will give us additional information to make that decision, similar to how we make decisions about treatment based on blood test results.
Dr. Hayee: One would hope so.
Not catching a disease before it’s too late– diagnosing a cancer at a late stage when an early diagnosis could have saved a life– those are the kinds of things that keep physicians up at night. They’re horrendous, and unfortunately, most of us have personal experiences of them. Physicians constantly wonder how they can keep such things from happening.
Right now, we don’t have enough proof to say with certainty that AI will be able to do that, but if you look logically at what AI purports to do, then hopefully it will only be a matter of time before we can definitively prove that AI is saving lives
In healthcare, one of the biggest strengths and biggest weaknesses is the large amount of data that we generate. If we knew how to tap into it, I’m sure it could help us solve many of these problems. It could be a strategic asset that enables us to help our patients rather than just numbers on a page
Products like Encord are going to help us do that because they’re allowing us to unlock the potential of that data faster and in a more democratising way. Not having to be a highly trained programmer to get started with an AI project or proposal opens all kinds of opportunities for innovation and new ideas from new actors. That’s got to be the way forward. If we're going to make progress, we can’t forever concentrate these efforts in big silos and big companies. We need to have more grassroots involvement, and products like Encord are going to help make that participation possible.
","text":"King’s College Hospital’s Director for Gastroenterology and Endoscopy Dr. Bu Hayee is– to put it mildly– a busy man. As a practising physician and assistant professor, he has a number of competing interests: academic, strategic, research, and clinical. In addition to supervising students and treating patients, he works closely with NHS England on their digital pathways and transformations, and he has a deep interest in how emerging technologies such as artificial intelligence and computer vision can increase the efficiency of patient care. In 2020, Dr. Hayee assisted in supervising a collaboration between King’s College London and Encord– the platform for data-centric computer vision– which aimed to increase the speed at which doctors could annotate polyps in colonoscopy videos (training data for computer vision models detecting precancerous polyps). Polyp detection in action We recently sat down with Dr. Bu Hayee to hear more about his thoughts on the role of deep learning in medical diagnosis, his collaboration with Encord, and the changing landscape of medical AI. The following interview has been edited for clarity and length. How has medical AI evolved over the course of your career? Dr. Hayee: It’s very new. We only began to see the practical application of medical AI in my speciality about three years ago. Endoscopy lends itself extremely well to computer vision because it's a very image oriented speciality. The diagnosis and evaluation of patients and the conditions they might have relies on image analysis, so computer vision is gaining a lot of traction in the field. Although I have had a longstanding interest in machine learning and how it can be leveraged to provide better patient care, unfortunately, until very recently, these kinds of machine learning projects have been slow to deliver because training a model requires millions and millions of data points. Preparing that data requires laborious image annotation and time-intensive quality control. These barriers make it difficult to move a model out of the research stage and into production. These roadblocks also mean that this technology has mostly been the domain of large industries as opposed to other, smaller actors that have been dissuaded from pursuing deep learning because of the challenges associated with training data curation and model development. You mentioned data annotation as a laborious process. Could you describe what it’s like to annotate these images manually? Dr. Hayee: It’s mental torture. Annotation is simply one of the most painful tasks that we have to perform in image based research. Labelling data is time consuming, and it requires the attention of someone who understands the subject well enough to annotate the images. Reliable and highly accurate annotations are the bedrock of the machine learning process, so labelling has to be done properly. One option is to outsource the images to a company that provides annotation services, but since these annotators have no background in medicine, clinicians must educate them about the data which is in itself a labour intensive effort. Alternatively, a hospital can pay a highly trained medical professional with two postgraduate degrees to sit in front of a screen and click a mouse for hours, which is not an efficient or cost effective use of time. How did Encord’s tools change the image annotation experience for you? Dr. Hayee: The built-in tools allowed us to rapidly annotate large numbers of data points. Before, using clinicians to annotate precancerous polyp videos had prohibitively high costs because of the large number of datasets needed to train the model. With Encord, we annotated the data over six times faster than using traditional methods. That’s a fantastic result because at those rates we can still deploy highly qualified medical professionals to annotate data, but we don’t take up a huge amount of their time. Much of the task shifts to quality assurance, which is much quicker than annotating, because Encord’s micromodels automatically generated 97 percent of the labels. Rather than acting as annotators, we become the humans-in-the-loop who review the machine’s output, in this case labels, for accuracy. Compared to other label assistance tools that we’ve worked with, Encord’s user experience is much smoother. The interface is intuitive, so it requires very little training to become a sort of “expert” at using all the functionality. That’s what you want, right? The mark of a great product is in many ways its useability. For instance, you plug in your smartphone, it works, and you don’t need much training to operate it. That’s the kind of UX we’ve come to expect in the 21st century. When it comes to implementing medical AI, we’ll need that kind of experience because doctors aren’t programmers. We’re end users, and we don’t want to have to learn to code to use the product effectively. We want a product that compliments our medical expertise and enhances our medical practice without requiring us to become computer science experts. Encord's tools in action What’s the greatest benefit that AI in general, and computer vision in particular, can bring to your field? Dr. Hayee: In short, time– time for both patients and clinicians. We’ll be able to give patients real-time evolutions of what we’re seeing on the screen. We’ll be able to detect abnormalities visually with greater reliability and greater speed rather than having to wait for a microscopic confirmation. When it comes to patient care, physicians will have more time to dedicate to a care plan because we won’t have to depend on the current pathways that we use for diagnosis, such as waiting for a histology on a biopsy we’ve taken to come back from the lab, before we make a care management plan. AI will be incredibly useful in decision support. It’s not a decision replacement. The machine won’t replace the doctor and diagnose the patient itself, but it will provide a second pair of eyes, so to speak, for an already educated clinician. The machine can confirm the doctor's diagnosis, increasing the physician’s confidence so that they can make a management plan and treat the patient straight away. Alternatively, if the machine detects something different from what the doctor had thought, the doctor can take a bit more care, be a bit more cautious, and investigate a bit more thoroughly before making a diagnosis and devising a management plan. Both of those scenarios are positive in my book. How do you think patients and medical professionals view adoption of AI for medical treatment? Dr. Hayee: I think there’s some confusion around the technology’s role. Even calling this technology AI unnecessarily raises some hackles because a lot of people associate AI with The Terminator and iRobot– two of the worst movies to have brought AI into the common consciousness because they perpetuate such a negative, unrealistic view of the technology and what it does. When I talk to people about AI, I say: ‘Listen, these aren’t thinking machines or all-seeing eyes. They aren’t even as smart as a three-year-old child putting blocks into a hole. They’re not going to replace doctors or nurses.’ Medical professionals will become comfortable working daily with AI. In time, computer vision will become what our Texas Instrument calculators are to us now. It’ll make us better and faster at our jobs. I can do simple addition without my calculator, but the calculator augments what I do and improves my performance. That’s what AI is going to do for healthcare. The clinician will always be the person who decides, but the AI will give us additional information to make that decision, similar to how we make decisions about treatment based on blood test results. You mentioned earlier that time was the greatest benefit that AI could provide to patients. Can you see AI eventually making a profound difference in early diagnosis for diseases? Dr. Hayee: One would hope so. Not catching a disease before it’s too late– diagnosing a cancer at a late stage when an early diagnosis could have saved a life– those are the kinds of things that keep physicians up at night. They’re horrendous, and unfortunately, most of us have personal experiences of them. Physicians constantly wonder how they can keep such things from happening. Right now, we don’t have enough proof to say with certainty that AI will be able to do that, but if you look logically at what AI purports to do, then hopefully it will only be a matter of time before we can definitively prove that AI is saving lives In healthcare, one of the biggest strengths and biggest weaknesses is the large amount of data that we generate. If we knew how to tap into it, I’m sure it could help us solve many of these problems. It could be a strategic asset that enables us to help our patients rather than just numbers on a page Products like Encord are going to help us do that because they’re allowing us to unlock the potential of that data faster and in a more democratising way. Not having to be a highly trained programmer to get started with an AI project or proposal opens all kinds of opportunities for innovation and new ideas from new actors. That’s got to be the way forward. If we're going to make progress, we can’t forever concentrate these efforts in big silos and big companies. We need to have more grassroots involvement, and products like Encord are going to help make that participation possible. 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King's College Uses Encord to Automatically Generate 97% of Labels"},"read_time":7,"sub_image":{"alt":"kings college london","url":"https://images.prismic.io/encord/aBydIidWJ-7kRv-C_Logo_kingscollegelondon_white_2.png?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/aBydIidWJ-7kRv-C_Logo_kingscollegelondon_white_2.png?auto=format%2Ccompress&fit=max&w=2&h=10&fm=webp 2w,\nhttps://images.prismic.io/encord/aBydIidWJ-7kRv-C_Logo_kingscollegelondon_white_2.png?auto=format%2Ccompress&fit=max&w=4&h=20&fm=webp 4w,\nhttps://images.prismic.io/encord/aBydIidWJ-7kRv-C_Logo_kingscollegelondon_white_2.png?auto=format%2Ccompress&fit=max&w=8&h=40&fm=webp 8w,\nhttps://images.prismic.io/encord/aBydIidWJ-7kRv-C_Logo_kingscollegelondon_white_2.png?auto=format%2Ccompress&fit=max&w=16&h=80&fm=webp 16w","sizes":"(min-width: 8px) 8px, 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Stig Hansen"}}}},"content":{"html":"{{light_callout_start}} Employing clinicians to label medical data is extremely expensive. Speed and accuracy are paramount. By leveraging Encord, the most senior (and thus, most expensive) clinician at KCL saw a 16x improvement in labeling efficiency, cutting model development time from 1 year to 2 months. {{light_callout_end}}
In a published study, “Novel artificial intelligence-driven software significantly shortens the time required for annotation in computer vision projects,” researchers at King’s College London annotated endoscopy videos of polyps 6.4x faster on Encord’s platform.
For researchers in the field, the accurate labeling of data remains “painstaking, cost-inefficient, [and] time-consuming.” Employing clinicians to label videos of pre-cancerous polyps is excessively expensive and thus inhibits the creation of large datasets.
The study compared Encord, an enterprise-grade solution, with CVAT, an open-source tool, to analyze the speed and accuracy of labeling on each platform. Using a sample of polyp videos from the Hyper-Kvasir dataset, annotators leveraged the functionality offered on each platform.
On the Encord platform, annotators employed embedded intelligence features, including object tracking algorithms and functionality to train CNNs to annotate the data. After labeling a small number of frames, annotators built a micro-model that predicted the annotations for the remaining frames. With CVAT, the annotators drew bounding boxes & propagated them across frames using linear interpolation of box coordinates.
![](\"https://images.prismic.io/encord/631f36d0-083c-411d-9dc1-cfd173c6baba_caseStudy%402x.png?auto=compress,format\")
{{light_callout_start}} “With the model assistance, we found a much higher increase in efficiency within Encord simply because most labels were produced by a trained model and did not require correction.” {{light_callout_end}}
The increased efficiency and accuracy decreases the time investment required by clinicians to annotate data and frees up their time for more productive activities.
","text":"{{light_callout_start}} Employing clinicians to label medical data is extremely expensive. Speed and accuracy are paramount. By leveraging Encord, the most senior (and thus, most expensive) clinician at KCL saw a 16x improvement in labeling efficiency, cutting model development time from 1 year to 2 months. {{light_callout_end}} In a published study, “Novel artificial intelligence-driven software significantly shortens the time required for annotation in computer vision projects,” researchers at King’s College London annotated endoscopy videos of polyps 6.4x faster on Encord’s platform. For researchers in the field, the accurate labeling of data remains “painstaking, cost-inefficient, [and] time-consuming.” Employing clinicians to label videos of pre-cancerous polyps is excessively expensive and thus inhibits the creation of large datasets. Encord vs. CVAT The study compared Encord, an enterprise-grade solution, with CVAT, an open-source tool, to analyze the speed and accuracy of labeling on each platform. Using a sample of polyp videos from the Hyper-Kvasir dataset, annotators leveraged the functionality offered on each platform. On the Encord platform, annotators employed embedded intelligence features, including object tracking algorithms and functionality to train CNNs to annotate the data. After labeling a small number of frames, annotators built a micro-model that predicted the annotations for the remaining frames. With CVAT, the annotators drew bounding boxes & propagated them across frames using linear interpolation of box coordinates. {{light_callout_start}} “With the model assistance, we found a much higher increase in efficiency within Encord simply because most labels were produced by a trained model and did not require correction.” {{light_callout_end}} The increased efficiency and accuracy decreases the time investment required by clinicians to annotate data and frees up their time for more productive activities. 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Stig Hansen"}}}},"content":{"html":"RapidAI develops AI that helps medical professionals identify and provide treatment for stroke victims. The company works with multiple annotators to label vast datasets of medical imaging. When managing the annotation and review process for large projects became unwieldy, the company turned to Encord’s platform for a seamless, centralized annotation experience.
For decades, doctors believed that only patients who sought care within a few hours of suffering a stroke would benefit from treatment. However, Dr. Greg Albers and Dr. Roland Bammer thought that with better technology, the window for care could be extended and more people with cerebrovascular disorders could be saved.
When diagnosing stroke patients, doctors use perfusion and diffusion imaging. However, perfusion imaging is a complicated process: obtaining the results is time-consuming, and perfusion scans provide a lot of information that doctors must compile and analyze quickly. If doctors had more immediate access to a summary image that provided meaningful information, they could quickly identify patients who would benefit from care, extending the time frame for treatment.
In 2012, Albers and Bammer founded RapidAI. Building on the technology they’d developed at Stanford University; the company provides brain imaging software tools designed to help doctors extract greater meaning from patient data. By creating high-quality images from diffusion and perfusion data, RapidAI’s technology assists neurovascular and vascular teams in making faster, more accurate diagnoses and treatment decisions.
In support of the founding mission, RapidAI has analyzed more than 7 million scans. The company is the exclusive advanced neuroimaging partner for the World Stroke Organization, and today, more than 2,000 hospitals in 100-plus countries use the Rapid platform to diagnose cerebrovascular disorders, such as strokes and aneurysms.
To train a model, RapidAI’s machine learning teams need thousands of labeled CT, CTA, MR, and MRA images. Each training project consists of about 500 cases, with between 30 and 200 CT image slices per case. If the case contains CTA scans, that number is closer to 1000 slices.
RapidAI’s annotation team consists of many highly trained annotators – with several board-certified diagnostic radiologists and subspecialists acting as reviewers. Initially, open-source labeling tools were used. As the project grew in complexity and size, the need for a robust management system and a seamless export process became essential so the company began to look for an online platform that could help them manage the annotation and review process more efficiently.
RapidAI tried out several online annotation vendors when looking for an online platform to manage and label training data.
{{light_callout_start}} “We went through the process of trying out each platform– uploading a test case and labeling a particular pathology,” says Dr. Ryan Mason, a neuroradiologist overseeing annotations at RapidAI. “Encord made it very easy to centrally keep track of annotations, including who had made them and who had reviewed them. It also had this great interpolation tool which was especially useful for the projects we were working on.” {{light_callout_end}}
For particular diseases, the shape of the pathology changes substantially in the peripheral slices of a case. In the middle slices, however, it maintains a uniform shape. With Encord’s interpolation tool, when RapidAI’s annotators reach the middle slices, they only have to label one out of every five images. Encord’s DICOM annotation tool captures the shape of the pathology, interpolating the label on the slices the annotators skipped. In a case with 100 slices, annotators only have to label about 30 images.
Encord’s reviewer function also helped RapidAI improve the efficiency of its annotation process. Using this feature, neuroradiologists can leave notes for annotators, correcting mistakes and providing guidance for improvement. They can also reject a case and label it directly. That way, annotators can then compare their original work against a reviewer’s to identify and learn from their makes. This feature is especially useful because as the team is gathering the labeled data they need, they are simultaneously teaching annotators to improve their labeling skills.
With Encord’s interpolation tool, Rapid AI’s team has increased the speed at which they can label data resulting in as much as a 30-minute reduction in annotation time per case.
Encord and RapidAI’s team communicate directly on Slack, and should RapidAI encounter a bug or issue, Encord responds immediately and fixes it within a few hours. Encord’s team has also worked with them to customize the product to meet their annotation needs.
{{light_callout_start}} “One of my favorite things about using Encord’s platform is how involved their team is with their customers,” says Mason. “For instance, when we started this project, I told them we liked to use Wacom tablets for drawing our annotations. Encord’s platform wasn’t set up for that, but their team asked to hop on a call so they could learn more. Within about two days, they released an update that made their platform compatible with the pads.” {{light_callout_end}}
Rapid AI is in the process of expanding its technology to additional disease states, such as pulmonary embolisms. As they expand, the company will continue to build out a pipeline of projects that each require annotating thousands of MRI and CT scans. By working with a centralized platform that effectively manages the annotation process and speeds up labeling, the company can increase the speed at which its AI enters production and, ultimately, saves lives.
{{medical_CTA}}
","text":"RapidAI develops AI that helps medical professionals identify and provide treatment for stroke victims. The company works with multiple annotators to label vast datasets of medical imaging. When managing the annotation and review process for large projects became unwieldy, the company turned to Encord’s platform for a seamless, centralized annotation experience. Introducing Customer: RapidAI For decades, doctors believed that only patients who sought care within a few hours of suffering a stroke would benefit from treatment. However, Dr. Greg Albers and Dr. Roland Bammer thought that with better technology, the window for care could be extended and more people with cerebrovascular disorders could be saved. When diagnosing stroke patients, doctors use perfusion and diffusion imaging. However, perfusion imaging is a complicated process: obtaining the results is time-consuming, and perfusion scans provide a lot of information that doctors must compile and analyze quickly. If doctors had more immediate access to a summary image that provided meaningful information, they could quickly identify patients who would benefit from care, extending the time frame for treatment. In 2012, Albers and Bammer founded RapidAI. Building on the technology they’d developed at Stanford University; the company provides brain imaging software tools designed to help doctors extract greater meaning from patient data. By creating high-quality images from diffusion and perfusion data, RapidAI’s technology assists neurovascular and vascular teams in making faster, more accurate diagnoses and treatment decisions. In support of the founding mission, RapidAI has analyzed more than 7 million scans. The company is the exclusive advanced neuroimaging partner for the World Stroke Organization, and today, more than 2,000 hospitals in 100-plus countries use the Rapid platform to diagnose cerebrovascular disorders, such as strokes and aneurysms. Problem: Managing Annotations for High-Volume Data Projects To train a model, RapidAI’s machine learning teams need thousands of labeled CT, CTA, MR, and MRA images. Each training project consists of about 500 cases, with between 30 and 200 CT image slices per case. If the case contains CTA scans, that number is closer to 1000 slices. RapidAI’s annotation team consists of many highly trained annotators – with several board-certified diagnostic radiologists and subspecialists acting as reviewers. Initially, open-source labeling tools were used. As the project grew in complexity and size, the need for a robust management system and a seamless export process became essential so the company began to look for an online platform that could help them manage the annotation and review process more efficiently. Solution: A Centralized Platform with Annotation Review and Interpolation Features RapidAI tried out several online annotation vendors when looking for an online platform to manage and label training data. {{light_callout_start}} “We went through the process of trying out each platform– uploading a test case and labeling a particular pathology,” says Dr. Ryan Mason, a neuroradiologist overseeing annotations at RapidAI. “Encord made it very easy to centrally keep track of annotations, including who had made them and who had reviewed them. It also had this great interpolation tool which was especially useful for the projects we were working on.” {{light_callout_end}} For particular diseases, the shape of the pathology changes substantially in the peripheral slices of a case. In the middle slices, however, it maintains a uniform shape. With Encord’s interpolation tool, when RapidAI’s annotators reach the middle slices, they only have to label one out of every five images. Encord’s DICOM annotation tool captures the shape of the pathology, interpolating the label on the slices the annotators skipped. In a case with 100 slices, annotators only have to label about 30 images. Encord’s reviewer function also helped RapidAI improve the efficiency of its annotation process. Using this feature, neuroradiologists can leave notes for annotators, correcting mistakes and providing guidance for improvement. They can also reject a case and label it directly. That way, annotators can then compare their original work against a reviewer’s to identify and learn from their makes. This feature is especially useful because as the team is gathering the labeled data they need, they are simultaneously teaching annotators to improve their labeling skills. Results: More Efficient Data Labeling Processes With Encord’s interpolation tool, Rapid AI’s team has increased the speed at which they can label data resulting in as much as a 30-minute reduction in annotation time per case. Encord and RapidAI’s team communicate directly on Slack, and should RapidAI encounter a bug or issue, Encord responds immediately and fixes it within a few hours. Encord’s team has also worked with them to customize the product to meet their annotation needs. {{light_callout_start}} “One of my favorite things about using Encord’s platform is how involved their team is with their customers,” says Mason. “For instance, when we started this project, I told them we liked to use Wacom tablets for drawing our annotations. Encord’s platform wasn’t set up for that, but their team asked to hop on a call so they could learn more. Within about two days, they released an update that made their platform compatible with the pads.” {{light_callout_end}} Rapid AI is in the process of expanding its technology to additional disease states, such as pulmonary embolisms. As they expand, the company will continue to build out a pipeline of projects that each require annotating thousands of MRI and CT scans. By working with a centralized platform that effectively manages the annotation process and speeds up labeling, the company can increase the speed at which its AI enters production and, ultimately, saves lives. {{medical_CTA}} "},"body":[{}],"image":{"alt":"Brain scans","url":"https://images.prismic.io/encord/b32482c5-8b6b-425d-8bd9-246caf7384a0_pexels-cottonbro-studio-5723883-min.png?auto=compress%2Cformat&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/b32482c5-8b6b-425d-8bd9-246caf7384a0_pexels-cottonbro-studio-5723883-min.png?auto=compress%2Cformat&fit=max&w=146&h=81&fm=webp 146w,\nhttps://images.prismic.io/encord/b32482c5-8b6b-425d-8bd9-246caf7384a0_pexels-cottonbro-studio-5723883-min.png?auto=compress%2Cformat&fit=max&w=292&h=162&fm=webp 292w,\nhttps://images.prismic.io/encord/b32482c5-8b6b-425d-8bd9-246caf7384a0_pexels-cottonbro-studio-5723883-min.png?auto=compress%2Cformat&fit=max&w=583&h=324&fm=webp 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Stig Hansen"}}}},"content":{"html":"Founded in 2014, Tractable uses AI technology to assist insurance providers in performing visual assessments of damaged assets. The company designs customizable machine learning models to serve insurance providers around the globe. As the complexity of data annotation projects grew, Tractable turned to Encord for a user-friendly platform that allowed for complex ontologies, annotator monitoring, and quality assurance.
By analyzing customer images, Tractable quickly facilitates accurate damage appraisal, making the recovery from accidents 10 times faster and enabling people to move through the claims process quickly and efficiently after they’ve had an automobile accident. Tractable also offers products for property assessment, providing the same high-quality experience for incidents that involve property damage and thereby helping people to recover faster after a disaster strikes their home.
Beyond accident recovery, Tractable’s technology can assist people throughout the entire lifecycle of owning a car or property. With Tractable, they can perform visual inspections before selling an asset, determine which parts of an asset to salvage or replace, obtain a condition report on a leased asset, and more.
By using Tractable, insurance providers can free up employee time for high-value tasks, improve customer experiences, accelerate repairs, and increase recycling– all of which has a positive impact on people and the world they live in.
When building their first models for image classification, Tractable’s team designed and used internal tools for data annotation. However, as Tractable grew their product offerings, the annotation tasks became more complicated, and the team needed a training data platform that supported segmentation. Building one in-house would be costly and time consuming.
Tractable’s team tried a few third-party platforms, but as the projects scaled and grew in sophistication– moving towards more pixel-level annotations with more complex ontologies– they found that many of these platforms had limitations, especially when it came to quality assurance (QA) functionality.
As Tractable began building its property assessment models, the remote annotation team began to grow rapidly and the need for improved data annotation workflows became more urgent. Ensuring the quality of both the data and its labels became increasingly important as annotation tasks grew in complexity and size.
{{light_callout_start}} “As our ambition grew, we realized that the functionality of many of the tools we were using was quite limited,” says Camilla Gilchrist, Head of Operations at Tractable. “We had a lot of steps in our annotation workflows, with many different pipelines, and the platforms couldn’t handle that complexity. We also had a bottleneck around quality assurance. With image segmentation, you can’t do a quick agreement rate analysis and doing a manual quality assurance check on each piece of data isn’t feasible. We needed a far more efficient way to assess quality.” {{light_callout_end}}
Tractable needed a training data platform that could incorporate the feedback of expert decision makers in the annotation process and provide QA functionality.
When annotating data for its models, Tractable needs to take into account expertise beyond visual observations.
{{light_callout_start}} “Think about how motor engineers assess damage on a vehicle,” explains Camilla. “They don’t just look at the car: they open and close the doors, feel the body work, and much more. As much as possible, we incorporate that expertise into our models, which requires determining specific annotation criteria that’s often quite complex. We need an accessible training data platform that allows annotators to break these complex labeling tasks into smaller parts and provides quality assurance and annotation review functionality. Otherwise, annotation tasks quickly become overwhelming, and the risk of mistakes increases.” {{light_callout_end}}
The higher the granularity of an annotation task, the greater the amount of detailed features that must be labeled in each piece of data. This level of detail increases the need for annotator monitoring and quality assurance.
Unlike Tractable’s legacy training data platforms, Encord built out the QA functionality that the company needed, enabling Tractable to unlock the power of using granular annotation techniques at scale.
At the same time, Encord’s user-friendly platform provided a level of control that allowed Tractable’s operations professionals to build out ontologies quickly.
The other platforms that Tractable had used allowed for custom mixtures of segmentation and frame-level annotation, but they required users to implement these customizations programmatically. SageMaker, for instance, allows for users to create custom UIs and ontologies, but to do so Tractable’s engineering and research teams had to put in a lot of time and effort into delivering the tooling the company needed.
With Encord’s QA and annotation review features, Tractable has been able to train annotators efficiently, monitoring their performance and providing feedback along the way. Their remote annotation team has grown to over 40 people.
{{light_callout_start}} “Managing QA workflows efficiently and having an overview of the entire annotation process has been so important for our success. The more manual that QA process is, the more the amount of work explodes as the annotation workforce scales,” explains Camilla. “It’s always about balancing speed and quality. A lot of platforms prioritize speed over quality or quality over speed. Encord speeds up annotation while still allowing for strong quality control.” {{light_callout_end}}
Encord’s API also works with S3 servers as a primary integration, so Tractable can keep customer data on its own servers. Because Tractable operates globally, it has to adhere to different data governance rules depending on the region in which a customer is located. The other tools that Tractable tried didn’t prioritize S3 integrations, so when the API broke, Tractable had to wait for and nudge those platforms to fix it– a less-than-ideal situation for a global company.
{{light_callout_start}} “Encord’s team has been very hands-on in delivering what we need to succeed,” says Camilla. “A lot of companies promise that level of commitment, but Encord actually delivers it. We receive excellent support. They build out new features quickly based on our feedback. With other platforms, we’ve had to troubleshoot problems on our own– even to the extent that we basically needed to build our own offline tooling to process the data we labeled using them! Encord has worked with us to find a solution for every challenge." {{light_callout_end}}
","text":"Founded in 2014, Tractable uses AI technology to assist insurance providers in performing visual assessments of damaged assets. The company designs customizable machine learning models to serve insurance providers around the globe. As the complexity of data annotation projects grew, Tractable turned to Encord for a user-friendly platform that allowed for complex ontologies, annotator monitoring, and quality assurance. Introducing Customer: Tractable By analyzing customer images, Tractable quickly facilitates accurate damage appraisal, making the recovery from accidents 10 times faster and enabling people to move through the claims process quickly and efficiently after they’ve had an automobile accident. Tractable also offers products for property assessment, providing the same high-quality experience for incidents that involve property damage and thereby helping people to recover faster after a disaster strikes their home. Beyond accident recovery, Tractable’s technology can assist people throughout the entire lifecycle of owning a car or property. With Tractable, they can perform visual inspections before selling an asset, determine which parts of an asset to salvage or replace, obtain a condition report on a leased asset, and more. By using Tractable, insurance providers can free up employee time for high-value tasks, improve customer experiences, accelerate repairs, and increase recycling– all of which has a positive impact on people and the world they live in. Problem: Performing Quality Assurance When Scaling Up Projects with Complex Ontologies When building their first models for image classification, Tractable’s team designed and used internal tools for data annotation. However, as Tractable grew their product offerings, the annotation tasks became more complicated, and the team needed a training data platform that supported segmentation. Building one in-house would be costly and time consuming. Tractable’s team tried a few third-party platforms, but as the projects scaled and grew in sophistication– moving towards more pixel-level annotations with more complex ontologies– they found that many of these platforms had limitations, especially when it came to quality assurance (QA) functionality. As Tractable began building its property assessment models, the remote annotation team began to grow rapidly and the need for improved data annotation workflows became more urgent. Ensuring the quality of both the data and its labels became increasingly important as annotation tasks grew in complexity and size. {{light_callout_start}} “As our ambition grew, we realized that the functionality of many of the tools we were using was quite limited,” says Camilla Gilchrist, Head of Operations at Tractable. “We had a lot of steps in our annotation workflows, with many different pipelines, and the platforms couldn’t handle that complexity. We also had a bottleneck around quality assurance. With image segmentation, you can’t do a quick agreement rate analysis and doing a manual quality assurance check on each piece of data isn’t feasible. We needed a far more efficient way to assess quality.” {{light_callout_end}} Tractable needed a training data platform that could incorporate the feedback of expert decision makers in the annotation process and provide QA functionality. Solution: A User-Friendly Training Data Platform with Quality Assurance and Annotator Review Functionality When annotating data for its models, Tractable needs to take into account expertise beyond visual observations. {{light_callout_start}} “Think about how motor engineers assess damage on a vehicle,” explains Camilla. “They don’t just look at the car: they open and close the doors, feel the body work, and much more. As much as possible, we incorporate that expertise into our models, which requires determining specific annotation criteria that’s often quite complex. We need an accessible training data platform that allows annotators to break these complex labeling tasks into smaller parts and provides quality assurance and annotation review functionality. Otherwise, annotation tasks quickly become overwhelming, and the risk of mistakes increases.” {{light_callout_end}} The higher the granularity of an annotation task, the greater the amount of detailed features that must be labeled in each piece of data. This level of detail increases the need for annotator monitoring and quality assurance. Unlike Tractable’s legacy training data platforms, Encord built out the QA functionality that the company needed, enabling Tractable to unlock the power of using granular annotation techniques at scale. At the same time, Encord’s user-friendly platform provided a level of control that allowed Tractable’s operations professionals to build out ontologies quickly. The other platforms that Tractable had used allowed for custom mixtures of segmentation and frame-level annotation, but they required users to implement these customizations programmatically. SageMaker, for instance, allows for users to create custom UIs and ontologies, but to do so Tractable’s engineering and research teams had to put in a lot of time and effort into delivering the tooling the company needed. Results: Improved Quality Control, Data Governance, and Annotator Training With Encord’s QA and annotation review features, Tractable has been able to train annotators efficiently, monitoring their performance and providing feedback along the way. Their remote annotation team has grown to over 40 people. {{light_callout_start}} “Managing QA workflows efficiently and having an overview of the entire annotation process has been so important for our success. The more manual that QA process is, the more the amount of work explodes as the annotation workforce scales,” explains Camilla. “It’s always about balancing speed and quality. A lot of platforms prioritize speed over quality or quality over speed. Encord speeds up annotation while still allowing for strong quality control.” {{light_callout_end}} Encord’s API also works with S3 servers as a primary integration, so Tractable can keep customer data on its own servers. Because Tractable operates globally, it has to adhere to different data governance rules depending on the region in which a customer is located. The other tools that Tractable tried didn’t prioritize S3 integrations, so when the API broke, Tractable had to wait for and nudge those platforms to fix it– a less-than-ideal situation for a global company. {{light_callout_start}} “Encord’s team has been very hands-on in delivering what we need to succeed,” says Camilla. “A lot of companies promise that level of commitment, but Encord actually delivers it. We receive excellent support. They build out new features quickly based on our feedback. With other platforms, we’ve had to troubleshoot problems on our own– even to the extent that we basically needed to build our own offline tooling to process the data we labeled using them! Encord has worked with us to find a solution for every challenge.\" {{light_callout_end}}"},"body":[{}],"image":{"alt":"Broken front tail light on car","url":"https://images.prismic.io/encord/7DFN-9ibSozdWWe-_updated-cover_tractable_1.avif?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/7DFN-9ibSozdWWe-_updated-cover_tractable_1.avif?auto=format%2Ccompress&fit=max&w=146&h=81&fm=webp 146w,\nhttps://images.prismic.io/encord/7DFN-9ibSozdWWe-_updated-cover_tractable_1.avif?auto=format%2Ccompress&fit=max&w=292&h=162&fm=webp 292w,\nhttps://images.prismic.io/encord/7DFN-9ibSozdWWe-_updated-cover_tractable_1.avif?auto=format%2Ccompress&fit=max&w=583&h=324&fm=webp 583w,\nhttps://images.prismic.io/encord/7DFN-9ibSozdWWe-_updated-cover_tractable_1.avif?auto=format%2Ccompress&fit=max&w=1166&h=648&fm=webp 1166w","sizes":"(min-width: 583px) 583px, 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Tractable Trained 40+ Annotators with Encord's QA & annotation review "},"read_time":3,"sub_image":{"alt":"Tractable","url":"https://images.prismic.io/encord/aBxwLydWJ-7kRvfB_Logo_tractable_white.png?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/aBxwLydWJ-7kRvfB_Logo_tractable_white.png?auto=format%2Ccompress&fit=max&w=2&h=10&fm=webp 2w,\nhttps://images.prismic.io/encord/aBxwLydWJ-7kRvfB_Logo_tractable_white.png?auto=format%2Ccompress&fit=max&w=4&h=20&fm=webp 4w,\nhttps://images.prismic.io/encord/aBxwLydWJ-7kRvfB_Logo_tractable_white.png?auto=format%2Ccompress&fit=max&w=8&h=40&fm=webp 8w,\nhttps://images.prismic.io/encord/aBxwLydWJ-7kRvfB_Logo_tractable_white.png?auto=format%2Ccompress&fit=max&w=16&h=80&fm=webp 16w","sizes":"(min-width: 8px) 8px, 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Stig Hansen"}}}},"content":{"html":"The Far Out Thinking Company, a UK based environmental R&D company that uses AI to combat pollinator decline, needed an accessible, affordable annotation tool to enable all members of their team to label images at speed. Using Encord’s platform, the team quickly annotated 4500 images of the European Hornet, Asian Hornet, and the Common Wasp and built a computer model to help distinguish between these different species and to confirm and detect the presence of Asian Hornets, an invasive species to the UK.
In late 2018, Pollenize, a U.K.-based pollinator conservation community interest company, launched its R&D arm The Far Out Thinking Company. The Far Out Thinking Company develops technology and AI solutions that help protect pollinators and preserve biodiversity.
Pollinators, and in particular bees, play a crucial role in the viability of our plant and animal ecosystems. Bees are responsible for pollinating a large percentage of crop species. According to the Food and Agriculture Organization of the United Nations, bees pollinate around 75% of the world's food crops.
However, bees are disappearing at a terrifying rate: 40 percent of insect pollinator species are facing extinction, with eight species of bees having been declared endangered.
Working in tandem with Pollenize, The Far Out Thinking Company is developing technology that helps to better understand pollinator populations and their resourcing needs. By devising devices such as AI beehives, hornet identification boxes, and moth detectors, the Far Out Thinking Company helps determine gaps in flora, pinpoint resource-stressed areas, inform public rewilding strategies, identify invasive predators, and more.
In 2003, the Asian Hornet arrived in Europe. An invasive species, the hornet has now spread throughout Europe, taking up the resources of natural pollinators and decimating the honey bee population. Asian hornets attack honey bees outside of beehives, a behavior known as “hawking,” biting off their heads and abdomens and taking their thoraxes back to their nests to feed their larva.
Killing individual Asian Hornets won’t solve the problem. Local authorities need to destroy the nests, each of which can house from several hundred to several thousand individual hornets.
Asian hornets haven’t yet naturalized in the U.K.; however, every year, they make their way into the country via shipping crates and the caravans of returning holidaymakers.
Asian Hornets create their nests in April, and in October each nest produces an average of 14 reproductive individuals, so nests must be destroyed before October. Otherwise, the Asian Hornets will multiply rapidly.
Since 2016, nine nests have been destroyed throughout the U.K.. However, local residents aren’t familiar with the look of this invasive species, and locating and destroying nests without a system for identifying hornets creates many challenges.
The Far Out Thinking Company developed the Asian Hornet ID Device. It uses computer vision to distinguish between local pollinators and Asian Hornets.
With a network of these devices, port cities and areas of entry into the U.K. would no longer have to rely on individuals to identify Asian Hornets. Instead, the devices will provide an automated early detection system and prevent the hornets from naturalizing in the U.K.
The Asian Hornet ID Device is situated in a box that contains a small reservoir of hornet attractant. After an Asian Hornet lands on the attractant, a camera located above the attractant observes it, and the computer vision model uses object detection to determine whether it is a common wasp, Asian Hornet, or European Hornet.
If the AI system detects an Asian Hornet, it sends an alert to the Far Out Thinking Company, which contacts the authorities who put a radio tag on it. They then track the hornet back to its nest and destroy it.
To build its computer vision model, The Far Out Thinking Company needed access to a data annotation tool that enabled multiple team members to label hundreds of images of pollinators at the same time.
Only one member of the team has a computer science and computer vision background. The other members of the team found the open-source annotation tools alienating. Without a foundation in computer science or a background in data labeling, they couldn’t easily use the tools to assist in annotation despite having valuable expertise in entomology.
An Encord employee familiar with the company’s work reached out, asking if they’d like to demo the product.
{{light_callout_start}} “The platform was so intuitive. It provides a lot of different levels of control and team management. Everyone on the team could label and review images easily,” says Mathew Elmes, Director of Pollenize and The Far Out Thinking Company. “We could also do computation both on the cloud and locally on our deep learning machine. All those factors made it really advantageous for us, and I saw the opportunity to save a lot of time and costs by using Encord to speed up our model development.” {{light_callout_end}}
Although Encord’s platform was initially designed for video annotation, the Encord team helped The Far Out Thinking Company string together a number of still images so that they could use the product to meet their specific annotation needs.
{{light_callout_start}} “After they helped us put the images together, we pressed a couple of buttons, and the platform made a model for us,” says Elmes. “It was so easy.” {{light_callout_end}}
The Far Out Thinking Company’s team culled its training data iNaturalists, a crowdsourced image database. All of the images have been second certified to research grades. Using Encord, two members of the AI Hornet Device team annotated 1500 images of each pollinator class (4500 total) in just three hours.
{{light_callout_start}} “We built separate pipelines for the three sets of images and labeled them accordingly. Then we trained and tested this small model. We got really good results, and it took more time to pull the images off the internet than it did to label them and build the model,” says Elmes. {{light_callout_end}}
The Far Out Thinking Company has installed the first two of its Asian Hornet ID Devices on the Isle of Scilly, off the coast of Cornwall. They’re now working with Associated British Ports to build and install a device in Plymouth Port.
The University of Plymouth has also helped them build an image scraper to speed up the process of gathering images, and the company is now building a larger model for its Asian Hornet ID Devices, training it on 1500 images of each pollinator class.
{{light_callout_start}} “With Encord, myself and other team members can label the data ourselves. We have numerous projects that we want to achieve this year, and I think having an easy to use platform where we can all work together is going to help us move them forward quickly and successfully,” says Elmes. {{light_callout_end}}
Beyond the Asian Hornet ID Device, The Far Out Thinking Company is using Encord in three other projects – one to determine the plant biodiversity in local fields, one to identify local moth diversity, and one to decode the honey bees’ waggle dances. All of these projects will help local pollinators survive by providing valuable information about the resourcing and health of the environment.
","text":"The Far Out Thinking Company, a UK based environmental R&D company that uses AI to combat pollinator decline, needed an accessible, affordable annotation tool to enable all members of their team to label images at speed. Using Encord’s platform, the team quickly annotated 4500 images of the European Hornet, Asian Hornet, and the Common Wasp and built a computer model to help distinguish between these different species and to confirm and detect the presence of Asian Hornets, an invasive species to the UK. Introducing Customer: Pollenize In late 2018, Pollenize, a U.K.-based pollinator conservation community interest company, launched its R&D arm The Far Out Thinking Company. The Far Out Thinking Company develops technology and AI solutions that help protect pollinators and preserve biodiversity. Pollinators, and in particular bees, play a crucial role in the viability of our plant and animal ecosystems. Bees are responsible for pollinating a large percentage of crop species. According to the Food and Agriculture Organization of the United Nations, bees pollinate around 75% of the world's food crops. However, bees are disappearing at a terrifying rate: 40 percent of insect pollinator species are facing extinction, with eight species of bees having been declared endangered. Working in tandem with Pollenize, The Far Out Thinking Company is developing technology that helps to better understand pollinator populations and their resourcing needs. By devising devices such as AI beehives, hornet identification boxes, and moth detectors, the Far Out Thinking Company helps determine gaps in flora, pinpoint resource-stressed areas, inform public rewilding strategies, identify invasive predators, and more. Problem: An Invasive Species Threatens the U.K.’s Honey Bee Population In 2003, the Asian Hornet arrived in Europe. An invasive species, the hornet has now spread throughout Europe, taking up the resources of natural pollinators and decimating the honey bee population. Asian hornets attack honey bees outside of beehives, a behavior known as “hawking,” biting off their heads and abdomens and taking their thoraxes back to their nests to feed their larva. Killing individual Asian Hornets won’t solve the problem. Local authorities need to destroy the nests, each of which can house from several hundred to several thousand individual hornets. Asian hornets haven’t yet naturalized in the U.K.; however, every year, they make their way into the country via shipping crates and the caravans of returning holidaymakers. Asian Hornets create their nests in April, and in October each nest produces an average of 14 reproductive individuals, so nests must be destroyed before October. Otherwise, the Asian Hornets will multiply rapidly. Since 2016, nine nests have been destroyed throughout the U.K.. However, local residents aren’t familiar with the look of this invasive species, and locating and destroying nests without a system for identifying hornets creates many challenges. Solution: Training Computer Vision Models to Identify Invasive Species and Protect Local Pollinators The Far Out Thinking Company developed the Asian Hornet ID Device. It uses computer vision to distinguish between local pollinators and Asian Hornets. With a network of these devices, port cities and areas of entry into the U.K. would no longer have to rely on individuals to identify Asian Hornets. Instead, the devices will provide an automated early detection system and prevent the hornets from naturalizing in the U.K. The Asian Hornet ID Device is situated in a box that contains a small reservoir of hornet attractant. After an Asian Hornet lands on the attractant, a camera located above the attractant observes it, and the computer vision model uses object detection to determine whether it is a common wasp, Asian Hornet, or European Hornet. If the AI system detects an Asian Hornet, it sends an alert to the Far Out Thinking Company, which contacts the authorities who put a radio tag on it. They then track the hornet back to its nest and destroy it. To build its computer vision model, The Far Out Thinking Company needed access to a data annotation tool that enabled multiple team members to label hundreds of images of pollinators at the same time. Only one member of the team has a computer science and computer vision background. The other members of the team found the open-source annotation tools alienating. Without a foundation in computer science or a background in data labeling, they couldn’t easily use the tools to assist in annotation despite having valuable expertise in entomology. An Encord employee familiar with the company’s work reached out, asking if they’d like to demo the product. {{light_callout_start}} “The platform was so intuitive. It provides a lot of different levels of control and team management. Everyone on the team could label and review images easily,” says Mathew Elmes, Director of Pollenize and The Far Out Thinking Company. “We could also do computation both on the cloud and locally on our deep learning machine. All those factors made it really advantageous for us, and I saw the opportunity to save a lot of time and costs by using Encord to speed up our model development.” {{light_callout_end}} Although Encord’s platform was initially designed for video annotation, the Encord team helped The Far Out Thinking Company string together a number of still images so that they could use the product to meet their specific annotation needs. {{light_callout_start}} “After they helped us put the images together, we pressed a couple of buttons, and the platform made a model for us,” says Elmes. “It was so easy.” {{light_callout_end}} Results: Accessible Annotating, New Projects, Pollinator Protection The Far Out Thinking Company’s team culled its training data iNaturalists, a crowdsourced image database. All of the images have been second certified to research grades. Using Encord, two members of the AI Hornet Device team annotated 1500 images of each pollinator class (4500 total) in just three hours. {{light_callout_start}} “We built separate pipelines for the three sets of images and labeled them accordingly. Then we trained and tested this small model. We got really good results, and it took more time to pull the images off the internet than it did to label them and build the model,” says Elmes. {{light_callout_end}} The Far Out Thinking Company has installed the first two of its Asian Hornet ID Devices on the Isle of Scilly, off the coast of Cornwall. They’re now working with Associated British Ports to build and install a device in Plymouth Port. The University of Plymouth has also helped them build an image scraper to speed up the process of gathering images, and the company is now building a larger model for its Asian Hornet ID Devices, training it on 1500 images of each pollinator class. {{light_callout_start}} “With Encord, myself and other team members can label the data ourselves. We have numerous projects that we want to achieve this year, and I think having an easy to use platform where we can all work together is going to help us move them forward quickly and successfully,” says Elmes. {{light_callout_end}} Beyond the Asian Hornet ID Device, The Far Out Thinking Company is using Encord in three other projects – one to determine the plant biodiversity in local fields, one to identify local moth diversity, and one to decode the honey bees’ waggle dances. All of these projects will help local pollinators survive by providing valuable information about the resourcing and health of the environment."},"body":[{}],"image":{"alt":"bee on a yellow flower","url":"https://images.prismic.io/encord/aBxvgSdWJ-7kRvej_pollenize.avif?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/aBxvgSdWJ-7kRvej_pollenize.avif?auto=format%2Ccompress&fit=max&w=146&h=81&fm=webp 146w,\nhttps://images.prismic.io/encord/aBxvgSdWJ-7kRvej_pollenize.avif?auto=format%2Ccompress&fit=max&w=292&h=162&fm=webp 292w,\nhttps://images.prismic.io/encord/aBxvgSdWJ-7kRvej_pollenize.avif?auto=format%2Ccompress&fit=max&w=583&h=324&fm=webp 583w","sizes":"(min-width: 583px) 583px, 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the Honey Bees with Computer Vision"},"read_time":3,"sub_image":{"alt":"pollenize","url":"https://images.prismic.io/encord/aBxvmydWJ-7kRveq_Logo_pollenize_white.png?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/aBxvmydWJ-7kRveq_Logo_pollenize_white.png?auto=format%2Ccompress&fit=max&w=2&h=10&fm=webp 2w,\nhttps://images.prismic.io/encord/aBxvmydWJ-7kRveq_Logo_pollenize_white.png?auto=format%2Ccompress&fit=max&w=4&h=20&fm=webp 4w,\nhttps://images.prismic.io/encord/aBxvmydWJ-7kRveq_Logo_pollenize_white.png?auto=format%2Ccompress&fit=max&w=8&h=40&fm=webp 8w,\nhttps://images.prismic.io/encord/aBxvmydWJ-7kRveq_Logo_pollenize_white.png?auto=format%2Ccompress&fit=max&w=16&h=80&fm=webp 16w","sizes":"(min-width: 8px) 8px, 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100vw"}},"layout":"constrained","placeholder":{"fallback":"data:image/png;base64,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"},"width":8,"height":40}}},"first_publication_date":"2023-04-14T13:59:15+0000","tags":["Customers","Agriculture"]}},{"node":{"uid":"four-growers-customer-story","data":{"author":{"document":{"id":"7eacb635-8381-5dfe-bd83-7f2340c17caf","uid":"ulrik-stig-hansen","data":{"full_name":{"text":"Ulrik Stig Hansen"}}}},"content":{"html":"Automated harvesting and analytics company Four Growers uses Encord’s platform and annotators to help build its training datasets from scratch labeling millions of instances of greenhouses and plants. So far, Encord has halved the time it takes Four Growers to get its datasets in working order.
Four Growers was founded to provide healthy, affordable, local produce by reducing the production costs of greenhouse growers through autonomous harvesting robots.
The GR-100 robot has a robotic arm, calibrated by four stereo cameras that allow it to precisely detect and harvest produce using computer vision, focusing on tomatoes initially. Now in its second iteration, the GR-100 also offers heat mapping and yield forecasting abilities, as well as patented packing technology.
By automating the harvesting process, Four Growers hopes to minimize food waste, increase quality, and contribute to making produce more affordable for everyone.
Currently, commercial greenhouses are expansive and it is impossible for manual pickers to get all of the ripe fruits on time. Over time, this leads to a large quantity of fruit just rotting in the greenhouse.
Hoping to expedite this process, Four Growers set about developing their harvesting and analytics robot. But when it came to training its deep learning models for automated harvesting, Four Growers found that while there is a lot of awareness around growing in greenhouses, there was a distinct lack of publicly available data sets to draw from.
This meant that the company had to build its own datasets from scratch, initially employing a number of freelance contractors to manually annotate all of the data, which consisted mostly of images of greenhouses and tomato plants.
This process was incredibly time-consuming and manual. By having multiple external annotators using various tools, the Four Growers team had to often deal with multiple different annotation platforms at once, consolidating the data into one succinct database at a later stage.
For Mira Murali, Computer Vision Engineer at Four Growers, this manual approach was eating into her schedule every week taking as long as 10 hours to manually review and condense data from multiple annotators that came through in various formats.
However, despite the huge amount of time, it was taking to have the data manually labeled and then verified by the team at Four Growers, it felt like a necessary task given the importance of having clean accurate data to begin with.
“You can tweak your models all you want but if your data at the beginning is wrong, then no matter how good your model is, you won’t get the end resource that you want,” Mira says.
The team at Four Growers needed a data annotation tool that could handle the labeling of millions of object instances at the same time and this is where Encord came into the mix.
Initially, the team was attracted to Encord because not only did the platform offer automatic annotating, but it also allowed Four Growers to work with an in-house team of human annotators. So rather than having different freelance contractors using different systems that had to be later consolidated, this streamlined approach instantly gave the Four Growers team time back in their days to focus on innovating their product and growing the business.
“The fact that Encord was able to offer both an automatic labeling tool as well as a team of annotators that we could use was very attractive. We need that flexibility at this stage in our journey,” says Murali.
Encord Active is another tool beginning to be utilized by the Four Growers team. It is an open-source toolkit that can be used for testing, validating, and evaluating your models. This allows Four Growers to further segment their datasets and build on their predictive model, making the harvesting even more precise.
For Four Growers, a fast-growing and lean business, the biggest win with using Encord has been the time saved. Now, instead of spending hours upon hours dealing with contractors and re-verifying all of that work, the team can simply upload their images to Encord’s platform, wait for a ping to say the annotation work is complete, and focus on their core jobs.
Speaking about the experience with Encord’s platform, Murali says:
“Using Encord has more than halved the time it takes us to provide feedback on imagery. What could have previously taken us 2 hours now takes approximately 30 minutes.”
Four Growers currently works with a number of large commercial greenhouses located in the US, Canada, and the Netherlands. As the company continues to grow its business and expand into more regions and crops, the company plans to continue its journey with Encord, leaning into the data analysis aspect of the platform to further understand how the team can improve its model, further streamline harvesting and help reduce food waste.
","text":"Automated harvesting and analytics company Four Growers uses Encord’s platform and annotators to help build its training datasets from scratch labeling millions of instances of greenhouses and plants. So far, Encord has halved the time it takes Four Growers to get its datasets in working order. Customer: Meet Four Growers Four Growers was founded to provide healthy, affordable, local produce by reducing the production costs of greenhouse growers through autonomous harvesting robots. The GR-100 robot has a robotic arm, calibrated by four stereo cameras that allow it to precisely detect and harvest produce using computer vision, focusing on tomatoes initially. Now in its second iteration, the GR-100 also offers heat mapping and yield forecasting abilities, as well as patented packing technology. By automating the harvesting process, Four Growers hopes to minimize food waste, increase quality, and contribute to making produce more affordable for everyone. Problem: Insufficient Access to Annotated Datasets Hinders Development of Automated Harvesting Models Currently, commercial greenhouses are expansive and it is impossible for manual pickers to get all of the ripe fruits on time. Over time, this leads to a large quantity of fruit just rotting in the greenhouse. Hoping to expedite this process, Four Growers set about developing their harvesting and analytics robot. But when it came to training its deep learning models for automated harvesting, Four Growers found that while there is a lot of awareness around growing in greenhouses, there was a distinct lack of publicly available data sets to draw from. This meant that the company had to build its own datasets from scratch, initially employing a number of freelance contractors to manually annotate all of the data, which consisted mostly of images of greenhouses and tomato plants. This process was incredibly time-consuming and manual. By having multiple external annotators using various tools, the Four Growers team had to often deal with multiple different annotation platforms at once, consolidating the data into one succinct database at a later stage. For Mira Murali, Computer Vision Engineer at Four Growers, this manual approach was eating into her schedule every week taking as long as 10 hours to manually review and condense data from multiple annotators that came through in various formats. However, despite the huge amount of time, it was taking to have the data manually labeled and then verified by the team at Four Growers, it felt like a necessary task given the importance of having clean accurate data to begin with. “You can tweak your models all you want but if your data at the beginning is wrong, then no matter how good your model is, you won’t get the end resource that you want,” Mira says. Solution: Encord Annotate for automated labeling and a team of annotators The team at Four Growers needed a data annotation tool that could handle the labeling of millions of object instances at the same time and this is where Encord came into the mix. Initially, the team was attracted to Encord because not only did the platform offer automatic annotating, but it also allowed Four Growers to work with an in-house team of human annotators. So rather than having different freelance contractors using different systems that had to be later consolidated, this streamlined approach instantly gave the Four Growers team time back in their days to focus on innovating their product and growing the business. “The fact that Encord was able to offer both an automatic labeling tool as well as a team of annotators that we could use was very attractive. We need that flexibility at this stage in our journey,” says Murali. Encord Active is another tool beginning to be utilized by the Four Growers team. It is an open-source toolkit that can be used for testing, validating, and evaluating your models. This allows Four Growers to further segment their datasets and build on their predictive model, making the harvesting even more precise. Results: Significant Time Reduction and Streamlined Annotation Process Achieved with Encord's Platform For Four Growers, a fast-growing and lean business, the biggest win with using Encord has been the time saved. Now, instead of spending hours upon hours dealing with contractors and re-verifying all of that work, the team can simply upload their images to Encord’s platform, wait for a ping to say the annotation work is complete, and focus on their core jobs. Speaking about the experience with Encord’s platform, Murali says: “Using Encord has more than halved the time it takes us to provide feedback on imagery. What could have previously taken us 2 hours now takes approximately 30 minutes.” Four Growers currently works with a number of large commercial greenhouses located in the US, Canada, and the Netherlands. As the company continues to grow its business and expand into more regions and crops, the company plans to continue its journey with Encord, leaning into the data analysis aspect of the platform to further understand how the team can improve its model, further streamline harvesting and help reduce food waste."},"body":[],"image":{"alt":null,"url":"https://images.prismic.io/encord/6c81c5c5-0305-45fc-af05-9c61af026c17_Four+Growers+Stock+Image+%281%29.png?auto=compress%2Cformat&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/6c81c5c5-0305-45fc-af05-9c61af026c17_Four+Growers+Stock+Image+%281%29.png?auto=compress%2Cformat&fit=max&w=146&h=81&fm=webp 146w,\nhttps://images.prismic.io/encord/6c81c5c5-0305-45fc-af05-9c61af026c17_Four+Growers+Stock+Image+%281%29.png?auto=compress%2Cformat&fit=max&w=292&h=162&fm=webp 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Fruit and Vegetable Harvesting & Analytics with Computer Vision"},"read_time":null,"sub_image":{"alt":"four growers","url":"https://images.prismic.io/encord/aBxu9ydWJ-7kRvdc_Logo_fourgrowers_white.png?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/aBxu9ydWJ-7kRvdc_Logo_fourgrowers_white.png?auto=format%2Ccompress&fit=max&w=2&h=10&fm=webp 2w,\nhttps://images.prismic.io/encord/aBxu9ydWJ-7kRvdc_Logo_fourgrowers_white.png?auto=format%2Ccompress&fit=max&w=4&h=20&fm=webp 4w,\nhttps://images.prismic.io/encord/aBxu9ydWJ-7kRvdc_Logo_fourgrowers_white.png?auto=format%2Ccompress&fit=max&w=8&h=40&fm=webp 8w,\nhttps://images.prismic.io/encord/aBxu9ydWJ-7kRvdc_Logo_fourgrowers_white.png?auto=format%2Ccompress&fit=max&w=16&h=80&fm=webp 16w","sizes":"(min-width: 8px) 8px, 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AI","Agriculture"]}},{"node":{"uid":"treeconomy-customer-story","data":{"author":{"document":{"id":"7eacb635-8381-5dfe-bd83-7f2340c17caf","uid":"ulrik-stig-hansen","data":{"full_name":{"text":"Ulrik Stig Hansen"}}}},"content":{"html":"Treeconomy uses Encord to collect reliable, granular data on trees. This is used to more accurately measure the carbon content in forests. More reliable carbon credit data bolsters Treeconomy’s reputation, allows the company to charge 50-150% more for carbon credits, and enables them to redirect more funds to reforestation efforts.
Treeconomy, an Earth Tech company, was created to better incentivize the planting of trees. It does so by accurately quantifying carbon captured and stored by trees (using sophisticated technology), packaging them as carbon offsets, and selling these high-quality carbon offsets to industries. Profits generated benefit landowners and incentivize them to preserve forests and plant trees.
What sets Treeconomy apart from competitors is its use of world-class remote sensing, machine learning, and monitoring tools. This enables Treeconomy to more accurately measure carbon offsets and detect changes to a project location that could alter carbon levels. Doing so helps to assure investors that their nature-based carbon credits are real, that trees are really growing, and that the project is delivering on its impact claims.
Accurately measuring the amount of carbon in a forest is extremely important for carbon offsetting. Errors can lead to greenwashing and a loss of trust in the carbon market. As Treeconomy Co-Founder Robert Godfrey notes “Right now there are a number of companies being called out and caught with bad credits on their books, where projects have maybe grossly overestimated their climate impact.”
Yet measuring carbon in forests is difficult. Traditionally, this was done by meticulously going from tree to tree with a tape measure and making broad extrapolations based on these measurements. The process was time-consuming and error-prone. Monitoring changes in the forest - like growth or deforestation - was similarly laborious.
To address these challenges, Treeconomy explored open-source data and satellite imagery but found they had limitations. For example, satellite imagery still required them to manually count trees, a time-consuming and inefficient process. They created a computer vision algorithm to detect tree crowns from high-resolution drone and satellite data imagery but determined that larger and more relevant data sets were required to produce more accurate results.
Treeconomy turned to Encord as an intuitive and flexible data annotation tool capable of labeling hundreds of images at once. The tool has enabled Treeconomy to train its computer vision algorithm to accurately count the number of trees in a given location.
One of the big advantages of Encord for Treeconomy is its compatibility with Microsoft Azure and ease of use. This enabled the team to seamlessly integrate their existing data sets, thereby saving time and effort.
Using Encord resulted in a positive discovery for Treeconomy: The tool could facilitate their future plans of labeling specific tree species - not just counting trees - and remove the need to hire staff for the task.
Encord has helped Treeconomy gather more accurate data and save time. As Godfrey explains, “Encord helps us to offer a ‘best in class’ capability for counting trees. It has helped us to improve the computer vision algorithm that allows us to delineate individual tree tops. [We were able] to count the entire forest in two minutes as opposed to two weeks.”
By accurately quantifying trees in a forest, Treeconomy has been able to create more reliable carbon offsets. According to Godfrey, this has allowed the company to package and sell these offsets at premiums ranging from 50% to 150% above those of alternative options.
This increased revenue is not only good for the company’s bottom line but is channeled back into the communities, benefiting landowners and supporting reforestation efforts. Ultimately, these actions reinforce Treeconomy's dedication to transforming sustainable land use into an economically rewarding and competitive venture.
{{try_encord}}
","text":"Treeconomy uses Encord to collect reliable, granular data on trees. This is used to more accurately measure the carbon content in forests. More reliable carbon credit data bolsters Treeconomy’s reputation, allows the company to charge 50-150% more for carbon credits, and enables them to redirect more funds to reforestation efforts. Introducing Customer: Treecomony Treeconomy, an Earth Tech company, was created to better incentivize the planting of trees. It does so by accurately quantifying carbon captured and stored by trees (using sophisticated technology), packaging them as carbon offsets, and selling these high-quality carbon offsets to industries. Profits generated benefit landowners and incentivize them to preserve forests and plant trees. What sets Treeconomy apart from competitors is its use of world-class remote sensing, machine learning, and monitoring tools. This enables Treeconomy to more accurately measure carbon offsets and detect changes to a project location that could alter carbon levels. Doing so helps to assure investors that their nature-based carbon credits are real, that trees are really growing, and that the project is delivering on its impact claims. Problem: Time-consuming & error-prone to measure carbon forests Accurately measuring the amount of carbon in a forest is extremely important for carbon offsetting. Errors can lead to greenwashing and a loss of trust in the carbon market. As Treeconomy Co-Founder Robert Godfrey notes “Right now there are a number of companies being called out and caught with bad credits on their books, where projects have maybe grossly overestimated their climate impact.” Yet measuring carbon in forests is difficult. Traditionally, this was done by meticulously going from tree to tree with a tape measure and making broad extrapolations based on these measurements. The process was time-consuming and error-prone. Monitoring changes in the forest - like growth or deforestation - was similarly laborious. To address these challenges, Treeconomy explored open-source data and satellite imagery but found they had limitations. For example, satellite imagery still required them to manually count trees, a time-consuming and inefficient process. They created a computer vision algorithm to detect tree crowns from high-resolution drone and satellite data imagery but determined that larger and more relevant data sets were required to produce more accurate results. Solution: Encord provided labeling to train computer vision mdoel to count trees Treeconomy turned to Encord as an intuitive and flexible data annotation tool capable of labeling hundreds of images at once. The tool has enabled Treeconomy to train its computer vision algorithm to accurately count the number of trees in a given location. One of the big advantages of Encord for Treeconomy is its compatibility with Microsoft Azure and ease of use. This enabled the team to seamlessly integrate their existing data sets, thereby saving time and effort. Using Encord resulted in a positive discovery for Treeconomy: The tool could facilitate their future plans of labeling specific tree species - not just counting trees - and remove the need to hire staff for the task. Results: Sell offsets at premiums, from 50% to 150% above alternative options Encord has helped Treeconomy gather more accurate data and save time. As Godfrey explains, “Encord helps us to offer a ‘best in class’ capability for counting trees. It has helped us to improve the computer vision algorithm that allows us to delineate individual tree tops. [We were able] to count the entire forest in two minutes as opposed to two weeks.” By accurately quantifying trees in a forest, Treeconomy has been able to create more reliable carbon offsets. According to Godfrey, this has allowed the company to package and sell these offsets at premiums ranging from 50% to 150% above those of alternative options. This increased revenue is not only good for the company’s bottom line but is channeled back into the communities, benefiting landowners and supporting reforestation efforts. Ultimately, these actions reinforce Treeconomy's dedication to transforming sustainable land use into an economically rewarding and competitive venture. {{try_encord}} "},"body":[],"image":{"alt":"forest of trees","url":"https://images.prismic.io/encord/aBxtyCdWJ-7kRvcx_treeconomy.avif?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/aBxtyCdWJ-7kRvcx_treeconomy.avif?auto=format%2Ccompress&fit=max&w=146&h=81&fm=webp 146w,\nhttps://images.prismic.io/encord/aBxtyCdWJ-7kRvcx_treeconomy.avif?auto=format%2Ccompress&fit=max&w=292&h=162&fm=webp 292w,\nhttps://images.prismic.io/encord/aBxtyCdWJ-7kRvcx_treeconomy.avif?auto=format%2Ccompress&fit=max&w=583&h=324&fm=webp 583w","sizes":"(min-width: 583px) 583px, 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Measuring Carbon Content in Forests"},"read_time":null,"sub_image":{"alt":"Treeconomy","url":"https://images.prismic.io/encord/aBxt2idWJ-7kRvcz_Logo_treeconomy_white.png?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/aBxt2idWJ-7kRvcz_Logo_treeconomy_white.png?auto=format%2Ccompress&fit=max&w=2&h=10&fm=webp 2w,\nhttps://images.prismic.io/encord/aBxt2idWJ-7kRvcz_Logo_treeconomy_white.png?auto=format%2Ccompress&fit=max&w=4&h=20&fm=webp 4w,\nhttps://images.prismic.io/encord/aBxt2idWJ-7kRvcz_Logo_treeconomy_white.png?auto=format%2Ccompress&fit=max&w=8&h=40&fm=webp 8w,\nhttps://images.prismic.io/encord/aBxt2idWJ-7kRvcz_Logo_treeconomy_white.png?auto=format%2Ccompress&fit=max&w=16&h=80&fm=webp 16w","sizes":"(min-width: 8px) 8px, 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AI "]}},{"node":{"uid":"viz-customer-story","data":{"author":{"document":{"id":"7eacb635-8381-5dfe-bd83-7f2340c17caf","uid":"ulrik-stig-hansen","data":{"full_name":{"text":"Ulrik Stig Hansen"}}}},"content":{"html":"Viz.ai’s revolutionary technology empowers medical teams to save lives through early detection and care coordination. The award-winning health tech company leverages Encord to accelerate processes, improve accuracy of diagnosis, and streamline collaboration among clinical AI teams. Encord’s medical training data platform and services have leveled up Viz.ai’s capabilities. They are now able to analyze data quickly and enter new areas of medical research.
Viz.ai uses artificial intelligence to speed up care and improve patient outcomes. The San Francisco-based health tech company uses its software to ingest patient data and images in real-time and use regulatory-approved AI algorithms to detect disease and notify the appropriate medical team. This allows early detection of large vessel occlusion strokes, aneurysms, pulmonary embolism, and other critical diseases. It alerts doctors and nurses, who can see the images on their phones, saving hours of the time it would otherwise take to get that patient to diagnosis and treatment.
Their slogan “time is brain” speaks to the heart of their mission. Every minute that passes during a stroke, 2 billion brain cells die, so early detection and accurate diagnosis are crucial.
Viz.ai’s success in aiding medical teams to rapidly detect diseases has allowed them to help enhance and preserve the quality of patients’ lives. And, with more success, comes more responsibility. The healthcare tech company has been rapidly growing and is constantly developing more solutions to support clinical teams worldwide. Viz.ai is now expanding into cardiovascular diseases with a recent approval by the FDA of AI-powered software for hypertrophic cardiomyopathy, an often missed but potentially deadly condition.
Early detection of a stroke, for instance, is possible thanks to Viz.ai’s abundance of data. By accessing and comparing scans with their multiple medical databases, the accuracy of their products is top of the range. However, for their teams, this has also translated into a lot of manpower and time consumed.
Previously, the clinical AI teams at Viz.ai relied on an in-house annotation platform to analyze and label medical data obtained from outside resources. “However, it lacked several features to enhance and streamline team collaboration,” explains Maayan Gerbi, Clicinal AI Specialist. “We desired more automated annotation features to track progress amongst teams and lower maintenance.”
It was time to search for a new solution that could support Viz.ai’s ambitious and fast-growing teams.
After considering multiple annotation solutions, it was clear that Encord was the best fit for their needs. What stood out for Viz.ai was Encord’s highly responsive team, top-notch annotation features, as well as the user-friendly interface of the platform.
“Encord’s robust support system has been remarkable. Whenever questions or issues come up, they are always supportive and helpful. This ensures that our workflows remain uninterrupted,” said Maayan. Encord’s helpful and reliable team also facilitated a smooth integration. “The annotation platform is well designed for compatibility and interpretability. We were able to effectively align it with the current systems,” added Data Manager Sarit Meshesha.
From project management, tracking, and monitoring to organizing large volumes of data in a user-friendly manner, Encord’s capabilities empower teams to annotate more speedily and accurately. Viz.ai particularly highlights the interpolation tool. “Encord constantly impresses us with the ability to think outside the box,” says Maayan. “The interpolation tool is a sophisticated, semi-automatic labeling tool that has proven to be a significant time saver for our team by reducing labeling time and improving precision.”
Encord is now an integral part of Viz.ai’s workflow. The platform’s range of innovative features has allowed teams to take on more projects while staying organized. Additionally, the accuracy of the automatic and semi-automatic annotation features means less manual work and higher-quality data. The clinical AI teams can now expedite processes and review medical imaging more quickly.
The collaborative features have also proven beneficial. Viz.ai now experiences more effective communication amongst teams and better project management end-to-end. Maayan explains, “The division of tasks and clear role assignment ensures organized workflows and the ability for team members to consult certain cases with experts along the way.”
Thanks to the benefits that Viz.ai has witnessed with Encord, they now envision the platform as key to their growth and ever-challenging needs. “We plan on leveraging Encord to expand the range of use cases and include more medical conditions in our solutions… We are excited about our potential with Encord and look forward to seeing how we will evolve together in the future.”
","text":"Viz.ai’s revolutionary technology empowers medical teams to save lives through early detection and care coordination. The award-winning health tech company leverages Encord to accelerate processes, improve accuracy of diagnosis, and streamline collaboration among clinical AI teams. Encord’s medical training data platform and services have leveled up Viz.ai’s capabilities. They are now able to analyze data quickly and enter new areas of medical research. Introducing Customer: Viz.ai Viz.ai uses artificial intelligence to speed up care and improve patient outcomes. The San Francisco-based health tech company uses its software to ingest patient data and images in real-time and use regulatory-approved AI algorithms to detect disease and notify the appropriate medical team. This allows early detection of large vessel occlusion strokes, aneurysms, pulmonary embolism, and other critical diseases. It alerts doctors and nurses, who can see the images on their phones, saving hours of the time it would otherwise take to get that patient to diagnosis and treatment. Their slogan “time is brain” speaks to the heart of their mission. Every minute that passes during a stroke, 2 billion brain cells die, so early detection and accurate diagnosis are crucial. Viz.ai’s success in aiding medical teams to rapidly detect diseases has allowed them to help enhance and preserve the quality of patients’ lives. And, with more success, comes more responsibility. The healthcare tech company has been rapidly growing and is constantly developing more solutions to support clinical teams worldwide. Viz.ai is now expanding into cardiovascular diseases with a recent approval by the FDA of AI-powered software for hypertrophic cardiomyopathy, an often missed but potentially deadly condition. Problem: Time-consuming data comparison & need for team collaboration Early detection of a stroke, for instance, is possible thanks to Viz.ai’s abundance of data. By accessing and comparing scans with their multiple medical databases, the accuracy of their products is top of the range. However, for their teams, this has also translated into a lot of manpower and time consumed. Previously, the clinical AI teams at Viz.ai relied on an in-house annotation platform to analyze and label medical data obtained from outside resources. “However, it lacked several features to enhance and streamline team collaboration,” explains Maayan Gerbi, Clicinal AI Specialist. “We desired more automated annotation features to track progress amongst teams and lower maintenance.” It was time to search for a new solution that could support Viz.ai’s ambitious and fast-growing teams. Solution: Encord providing management & tracking for large volumes of data After considering multiple annotation solutions, it was clear that Encord was the best fit for their needs. What stood out for Viz.ai was Encord’s highly responsive team, top-notch annotation features, as well as the user-friendly interface of the platform. “Encord’s robust support system has been remarkable. Whenever questions or issues come up, they are always supportive and helpful. This ensures that our workflows remain uninterrupted,” said Maayan. Encord’s helpful and reliable team also facilitated a smooth integration. “The annotation platform is well designed for compatibility and interpretability. We were able to effectively align it with the current systems,” added Data Manager Sarit Meshesha. From project management, tracking, and monitoring to organizing large volumes of data in a user-friendly manner, Encord’s capabilities empower teams to annotate more speedily and accurately. Viz.ai particularly highlights the interpolation tool. “Encord constantly impresses us with the ability to think outside the box,” says Maayan. “The interpolation tool is a sophisticated, semi-automatic labeling tool that has proven to be a significant time saver for our team by reducing labeling time and improving precision.” Results: Less manual work & higher-quality data Encord is now an integral part of Viz.ai’s workflow. The platform’s range of innovative features has allowed teams to take on more projects while staying organized. Additionally, the accuracy of the automatic and semi-automatic annotation features means less manual work and higher-quality data. The clinical AI teams can now expedite processes and review medical imaging more quickly. The collaborative features have also proven beneficial. Viz.ai now experiences more effective communication amongst teams and better project management end-to-end. Maayan explains, “The division of tasks and clear role assignment ensures organized workflows and the ability for team members to consult certain cases with experts along the way.” Thanks to the benefits that Viz.ai has witnessed with Encord, they now envision the platform as key to their growth and ever-challenging needs. “We plan on leveraging Encord to expand the range of use cases and include more medical conditions in our solutions… We are excited about our potential with Encord and look forward to seeing how we will evolve together in the future.”"},"body":[],"image":{"alt":"medical brain scans being looked at by a doctor","url":"https://images.prismic.io/encord/fed8507c-39b7-4204-aa6b-7b433fbd7ea4_Banner+image+fo+1.png?auto=compress%2Cformat&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/fed8507c-39b7-4204-aa6b-7b433fbd7ea4_Banner+image+fo+1.png?auto=compress%2Cformat&fit=max&w=146&h=81&fm=webp 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Stig Hansen"}}}},"content":{"html":"Automotus is leading the advancement of parking and traffic management solutions through the use of AI and computer vision technology.
The platform serves cities and airports to enhance street safety, sustainability, and efficiency by automating curbside operations, including parking, loading zones, zero-emission areas, bike lanes, and more.
Automotus faced challenges in managing their entire data pipeline for model development, including automating data curation, ensuring efficient AI-assisted labeling, and streamlining model evaluation processes.
After first establishing a continuous annotation pipeline, the team faced a critical question: Among all the collected data, how could they ensure they were labeling the most relevant data? And, how would they identify such data?
The large volumes of de-identified images captured from hundreds of cameras presented an abundance of data. Labeling all of it would not necessarily improve model performance and would be prohibitively expensive.
Upon evaluating other tools, the Automotus team partnered with Encord to manage their entire data pipeline for model development, from automated data curation to AI-assisted labeling to model evaluation.
Here's a look at their entire workflow:
![\"Feature](\"https://images.prismic.io/encord/ZoxfTx5LeNNTw6l7_automotus-diagram.avif?auto=format,compress\")
One of the team’s first priorities was converting the vast amount of de-identified images into labeled training data.
{{Training_data_CTA}}
Leveraging Encord, the team was able to visually inspect, query, and sort their datasets to eliminate undesirable low-quality data in just a few clicks.
This process resulted in a 35% reduction in the datasets' size for annotation. Consequently, the team achieved over a 33% reduction in their labeling costs.
The high model accuracy enabled Automotus to better serve and grow with their customers – presenting more accurate data to their clients: “From the modality distribution that happens at the street-level, to more accurate representations of the dwell times [and a few other metrics that we supply to our clients] – these base models are the ones driving these analytics.”
![\"quote](\"https://images.prismic.io/encord/Zo0OJB5LeNNTw7sD_nobg-testmonial-test.avif?auto=format,compress\")
The team evaluated several other tools on the market but ultimately decided to partner with Encord for several reasons:
The Automotus AI team notes: “For example, a shortcoming with other tools was the quality of the labels: we’d occasionally realize bounding boxes would be tighter or too wide around the objects they were identifying, or objects wouldn’t be classified correctly within frames. Now, we can select the sampling rate of frames that we want to move towards a review process, and share real-time context with annotators so that they can also power our model performance. This human-in-the-loop approach means we can use Encord to help our annotators perform annotations better, which in turn speeds up how quickly we can improve our model performance. We are able to localize objects better and increase accuracy.”
","text":"Introducing Customer: Automotus Automotus is leading the advancement of parking and traffic management solutions through the use of AI and computer vision technology. The platform serves cities and airports to enhance street safety, sustainability, and efficiency by automating curbside operations, including parking, loading zones, zero-emission areas, bike lanes, and more. Problem: Large volume of data, expensive to label Automotus faced challenges in managing their entire data pipeline for model development, including automating data curation, ensuring efficient AI-assisted labeling, and streamlining model evaluation processes. After first establishing a continuous annotation pipeline, the team faced a critical question: Among all the collected data, how could they ensure they were labeling the most relevant data? And, how would they identify such data? The large volumes of de-identified images captured from hundreds of cameras presented an abundance of data. Labeling all of it would not necessarily improve model performance and would be prohibitively expensive. Solution: Use Encord to manage data pipeline Upon evaluating other tools, the Automotus team partnered with Encord to manage their entire data pipeline for model development, from automated data curation to AI-assisted labeling to model evaluation. Here's a look at their entire workflow: One of the team’s first priorities was converting the vast amount of de-identified images into labeled training data. {{Training_data_CTA}} Results: 35% reduction in the datasets' size for annotation Leveraging Encord, the team was able to visually inspect, query, and sort their datasets to eliminate undesirable low-quality data in just a few clicks. This process resulted in a 35% reduction in the datasets' size for annotation. Consequently, the team achieved over a 33% reduction in their labeling costs. The high model accuracy enabled Automotus to better serve and grow with their customers – presenting more accurate data to their clients: “From the modality distribution that happens at the street-level, to more accurate representations of the dwell times [and a few other metrics that we supply to our clients] – these base models are the ones driving these analytics.” How does Encord compare to other tools on the market? The team evaluated several other tools on the market but ultimately decided to partner with Encord for several reasons: Flexible Ontology Structure: Encord facilitated efficient object classification and tracking, allowing for straightforward ontology management and seamless training of both detection and classification models within a single project. Quality Control Capabilities: Encord's human-in-the-loop feedback mechanisms significantly enhanced annotation performance, particularly in identifying small objects within frames. Automated Labeling Features: The ability to label a few frames and use assisted labeling to speed up the process, reducing the need for manual labeling of every frame, was highly advantageous. The Automotus AI team notes: “For example, a shortcoming with other tools was the quality of the labels: we’d occasionally realize bounding boxes would be tighter or too wide around the objects they were identifying, or objects wouldn’t be classified correctly within frames. Now, we can select the sampling rate of frames that we want to move towards a review process, and share real-time context with annotators so that they can also power our model performance. This human-in-the-loop approach means we can use Encord to help our annotators perform annotations better, which in turn speeds up how quickly we can improve our model performance. We are able to localize objects better and increase accuracy.”"},"body":[{}],"image":{"alt":"car, taxi and a bus driving through a city center","url":"https://images.prismic.io/encord/aBxsqydWJ-7kRvcH_automotus.avif?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/aBxsqydWJ-7kRvcH_automotus.avif?auto=format%2Ccompress&fit=max&w=146&h=81&fm=webp 146w,\nhttps://images.prismic.io/encord/aBxsqydWJ-7kRvcH_automotus.avif?auto=format%2Ccompress&fit=max&w=292&h=162&fm=webp 292w,\nhttps://images.prismic.io/encord/aBxsqydWJ-7kRvcH_automotus.avif?auto=format%2Ccompress&fit=max&w=583&h=324&fm=webp 583w","sizes":"(min-width: 583px) 583px, 100vw","type":"image/webp"}],"fallback":{"src":"https://images.prismic.io/encord/aBxsqydWJ-7kRvcH_automotus.avif?auto=format%2Ccompress&fit=max&w=583&h=324","srcSet":"https://images.prismic.io/encord/aBxsqydWJ-7kRvcH_automotus.avif?auto=format%2Ccompress&fit=max&w=146&h=81 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Last-mile Model Performance: Increase mAP by 20% while reducing your dataset size by 35% with intelligent visual data curation"},"read_time":null,"sub_image":{"alt":"automotus","url":"https://images.prismic.io/encord/aBM03PIqRLdaByBk_Logo_automotus_white.png?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/aBM03PIqRLdaByBk_Logo_automotus_white.png?auto=format%2Ccompress&fit=max&w=2&h=10&fm=webp 2w,\nhttps://images.prismic.io/encord/aBM03PIqRLdaByBk_Logo_automotus_white.png?auto=format%2Ccompress&fit=max&w=4&h=20&fm=webp 4w,\nhttps://images.prismic.io/encord/aBM03PIqRLdaByBk_Logo_automotus_white.png?auto=format%2Ccompress&fit=max&w=8&h=40&fm=webp 8w,\nhttps://images.prismic.io/encord/aBM03PIqRLdaByBk_Logo_automotus_white.png?auto=format%2Ccompress&fit=max&w=16&h=80&fm=webp 16w","sizes":"(min-width: 8px) 8px, 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100vw"}},"layout":"constrained","placeholder":{"fallback":"data:image/png;base64,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"},"width":8,"height":40}}},"first_publication_date":"2023-12-14T11:30:40+0000","tags":["Customers","Logistics","Smart cities"]}},{"node":{"uid":"harvard-medical-school-mgh-customer-story","data":{"author":{"document":{"id":"7eacb635-8381-5dfe-bd83-7f2340c17caf","uid":"ulrik-stig-hansen","data":{"full_name":{"text":"Ulrik Stig Hansen"}}}},"content":{"html":"A new paper published in MDPI (Multidisciplinary Digital Publishing Institute) demonstrates how, using the Encord platform, researchers at Harvard Medical School, Massachusetts General Hospital, and Brigham and Women’s Hospital were able to reduce vascular ultrasound annotation time from days to minutes and run automated analyses of their datasets.
Using Encord, the team was able to:
Medical imaging, particularly Arterial Duplex Ultrasound (DUS), plays a crucial role in diagnosing and managing vascular diseases like Popliteal Artery Aneurysms (PAAs). The traditional method of analyzing DUS images relies heavily on manual annotation by skilled medical professionals.
This process is fraught with challenges:
The subjective nature of manual annotations can lead to inconsistent measurements and interpretations due to inter- and intra-observer variability during annotation. This raises concerns about the reliability and reproducibility of the results and could impact the accuracy of diagnoses and treatment plans for patients.
The primary issue in this research paper lies in precisely annotating the inner and outer lumens of the artery in images - a critical step for accurate measurement and subsequent treatment planning.
The study tested the feasibility of the Encord platform to create an automated model that segments the inner and outer lumen within PAA DUS. Using image segmentation to find the largest diameter and thrombus area within PAAs helped standardize DUS measurements that are important for making decisions about surgery.
The researchers collected and prepared (deidentification and extraction) a dataset comprising DUS images of PAAs for upload to Encord before annotating a few images to serve as ground truth for the annotation models using Encord Annotate.
Using Encord Annotate’s automated labeling feature, they could generate segmentation masks for unlabeled images. This reduced the time and effort required for DUS image analysis while minimizing the potential for human error.
They trained three models and validated them with Encord Active on the annotated images (20, 60, and 80 sets). Encord Active enabled the researchers to understand the performance metrics that helped them select the best model for segmenting the inner and outer lumens of the popliteal artery with high precision.
{{light_callout_start}}After training models on image subsets, we tested them within the Encord platform. We selected the desired tests in the analysis tab of the project, and after a runtime period, the platform presented calculations of true positives, false negatives, mAP, IoU, and blur. {{light_callout_end}}
The report referenced Encord’s ability to seamlessly integrate into clinical processes with a user-friendly interface, simple onboarding, and rapid annotation workflows as crucial to the study's success. For healthcare practitioners who use the platform, this improves their diagnostic process without disrupting established procedures.
Where manual annotation could take several minutes per image, the researchers accomplished the task in a fraction of the time using Encord. Their workflow went from relying on RPVI-certified physicians manually annotating DUS images, a process that took several days, to using Encord to annotate a few images, train models, and auto-label unlabeled images in minutes.
This efficiency proves crucial in clinical settings, where timely diagnosis and treatment decisions can significantly impact patient outcomes.
![](\"https://images.prismic.io/encord/e1a9ac18-3923-4508-a32a-6da024c12d14_image1.png?auto=compress,format\")
Figure 1. AI segmentation classifications on duplex ultrasound images. (A) Outer polygon true-positive classification, where the color green indicates a correct segmentation. (B) Outer polygon false-positive classification, where red indicates an incorrect segmentation. (C) Inner polygon true-positive classification, where the color green indicates a correct segmentation. (D) Inner polygon false-positive classification, where red indicates an incorrect segmentation.
The researchers quantitatively assessed the performance of the three models with Encord Active providing analytics on the following metrics:
Encord Active calculated the outer polygon's mAP to be 0.85 for the 20-image model, 0.06 for the 60-image model, and 0 for the 80-image model. The mAP of the inner polygon was 0.23 for the 20-image, 60-image, and 80-image models. The true-positive rate (TPR) for the inner polygon remained at 0.23. See the full results in the table below:
![](\"https://images.prismic.io/encord/8956eb49-0fe7-4f9c-81da-273227cad04e_image2.png?auto=compress,format\")
“With regard to the models for outer and inner polygons, the outer polygon model outperformed the inner polygon model on every metric. The outer polygon demonstrated almost equal precision and recall at 0.85. The mAP for the outer polygon model was 0.85 with a true-positive rate of 0.86, which is comparable to other clinically used high-performing models for US segmentation.”
With Encord Active automatically providing model evaluation analytics, the team instantly discovered the model's strengths and weaknesses. For every model they trained, Active provided breakdowns and graphs on its performance, including the ability to visually explore the regions the model incorrectly segmented vs. the ground truth.
The researchers used Encord Active to explore the model's performance depending on the blur level, allowing them to visually interact with varying levels of blur in their dataset to understand how this impacted model performance.
The paper states, “Intuitively, our analysis found that as the images became blurrier, the model precision declined, and false-negative rates increased... Removing blur from—or augmenting—blur in images can be important for training accurate AI models.”
{{light_callout_start}} In summary, the platform’s intuitive navigation, complemented by tutorials for both model training and analysis, allowed for straightforward operationalization of the model training system among members of the research team. The results were displayed in an understandable format and interpreted within the following discussion. {{light_callout_end}}
The findings have far-reaching consequences for medical imaging and diagnosis. The researchers greatly improved the accuracy, reliability, and efficiency of DUS image analysis by auto-annotating images with Encord Annotate and validating annotation models with Encord Active. This could result in potentially better patient care, treatment planning, and diagnostic procedures.
At Encord, we are committed to continually providing healthcare practitioners and physicians with the data-centric AI platform they need to improve their medical imaging and analysis workflows.
We’re proud of the work the researchers were able to accomplish and how Encord is paving the way for broader applications of AI in various aspects of medical diagnostics.
{{light_callout_start}} 📑 Read the full paper on MDPI (Multidisciplinary Digital Publishing Institute). {{light_callout_end}}
","text":"Introduction A new paper published in MDPI (Multidisciplinary Digital Publishing Institute) demonstrates how, using the Encord platform, researchers at Harvard Medical School, Massachusetts General Hospital, and Brigham and Women’s Hospital were able to reduce vascular ultrasound annotation time from days to minutes and run automated analyses of their datasets. Using Encord, the team was able to: Create their first segmentation models by labeling only a handful of images Cut annotation time through segmentation models by an order of magnitude Visually explore their dataset and identify problematic areas - in their case, the impact of blur on their dataset Evaluate the performance of their segmentation models in the Encord platform \n Problem: DUS Image Annotation is Resource-Intensive and Prone to Human Error Medical imaging, particularly Arterial Duplex Ultrasound (DUS), plays a crucial role in diagnosing and managing vascular diseases like Popliteal Artery Aneurysms (PAAs). The traditional method of analyzing DUS images relies heavily on manual annotation by skilled medical professionals. This process is fraught with challenges: Time-consuming—especially with the growing volume of medical imaging data. Prone to human error. Heavily dependent on expertise and experience - furthering how resource-intensive the process becomes The subjective nature of manual annotations can lead to inconsistent measurements and interpretations due to inter- and intra-observer variability during annotation. This raises concerns about the reliability and reproducibility of the results and could impact the accuracy of diagnoses and treatment plans for patients. The primary issue in this research paper lies in precisely annotating the inner and outer lumens of the artery in images - a critical step for accurate measurement and subsequent treatment planning. Solution: Encord Annotate to Auto-Label DUS Images and Encord Active to Validate Model Performance The study tested the feasibility of the Encord platform to create an automated model that segments the inner and outer lumen within PAA DUS. Using image segmentation to find the largest diameter and thrombus area within PAAs helped standardize DUS measurements that are important for making decisions about surgery. Using Encord Annotate for Automated Annotation The researchers collected and prepared (deidentification and extraction) a dataset comprising DUS images of PAAs for upload to Encord before annotating a few images to serve as ground truth for the annotation models using Encord Annotate. Using Encord Annotate’s automated labeling feature, they could generate segmentation masks for unlabeled images. This reduced the time and effort required for DUS image analysis while minimizing the potential for human error. Using Encord Active to Select the Best-Performing Model They trained three models and validated them with Encord Active on the annotated images (20, 60, and 80 sets). Encord Active enabled the researchers to understand the performance metrics that helped them select the best model for segmenting the inner and outer lumens of the popliteal artery with high precision. {{light_callout_start}}After training models on image subsets, we tested them within the Encord platform. We selected the desired tests in the analysis tab of the project, and after a runtime period, the platform presented calculations of true positives, false negatives, mAP, IoU, and blur. {{light_callout_end}} The report referenced Encord’s ability to seamlessly integrate into clinical processes with a user-friendly interface, simple onboarding, and rapid annotation workflows as crucial to the study's success. For healthcare practitioners who use the platform, this improves their diagnostic process without disrupting established procedures. Results Encord Reduced Annotation Time from Days to Minutes Where manual annotation could take several minutes per image, the researchers accomplished the task in a fraction of the time using Encord. Their workflow went from relying on RPVI-certified physicians manually annotating DUS images, a process that took several days, to using Encord to annotate a few images, train models, and auto-label unlabeled images in minutes. This efficiency proves crucial in clinical settings, where timely diagnosis and treatment decisions can significantly impact patient outcomes. Figure 1. AI segmentation classifications on duplex ultrasound images. (A) Outer polygon true-positive classification, where the color green indicates a correct segmentation. (B) Outer polygon false-positive classification, where red indicates an incorrect segmentation. (C) Inner polygon true-positive classification, where the color green indicates a correct segmentation. (D) Inner polygon false-positive classification, where red indicates an incorrect segmentation. Better Evaluation and Observability of Model Performance with Encord Active The researchers quantitatively assessed the performance of the three models with Encord Active providing analytics on the following metrics: mean Average Precision (mAP). Intersection over Union (IoU). True Positive Rate (TPR). Encord Active calculated the outer polygon's mAP to be 0.85 for the 20-image model, 0.06 for the 60-image model, and 0 for the 80-image model. The mAP of the inner polygon was 0.23 for the 20-image, 60-image, and 80-image models. The true-positive rate (TPR) for the inner polygon remained at 0.23. See the full results in the table below: “With regard to the models for outer and inner polygons, the outer polygon model outperformed the inner polygon model on every metric. The outer polygon demonstrated almost equal precision and recall at 0.85. The mAP for the outer polygon model was 0.85 with a true-positive rate of 0.86, which is comparable to other clinically used high-performing models for US segmentation.” With Encord Active automatically providing model evaluation analytics, the team instantly discovered the model's strengths and weaknesses. For every model they trained, Active provided breakdowns and graphs on its performance, including the ability to visually explore the regions the model incorrectly segmented vs. the ground truth. Encord Active Uncovered Blurry DUS Images that Could Degrade Annotation Model Performance The researchers used Encord Active to explore the model's performance depending on the blur level, allowing them to visually interact with varying levels of blur in their dataset to understand how this impacted model performance. The paper states, “Intuitively, our analysis found that as the images became blurrier, the model precision declined, and false-negative rates increased... Removing blur from—or augmenting—blur in images can be important for training accurate AI models.” Conclusion {{light_callout_start}} In summary, the platform’s intuitive navigation, complemented by tutorials for both model training and analysis, allowed for straightforward operationalization of the model training system among members of the research team. The results were displayed in an understandable format and interpreted within the following discussion. {{light_callout_end}} The findings have far-reaching consequences for medical imaging and diagnosis. The researchers greatly improved the accuracy, reliability, and efficiency of DUS image analysis by auto-annotating images with Encord Annotate and validating annotation models with Encord Active. This could result in potentially better patient care, treatment planning, and diagnostic procedures. At Encord, we are committed to continually providing healthcare practitioners and physicians with the data-centric AI platform they need to improve their medical imaging and analysis workflows. We’re proud of the work the researchers were able to accomplish and how Encord is paving the way for broader applications of AI in various aspects of medical diagnostics. {{light_callout_start}} 📑 Read the full paper on MDPI (Multidisciplinary Digital Publishing Institute). {{light_callout_end}}"},"body":[],"image":{"alt":"doctor annotating medical scans","url":"https://images.prismic.io/encord/aBx8zCdWJ-7kRvnR_mgh.avif?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/aBx8zCdWJ-7kRvnR_mgh.avif?auto=format%2Ccompress&fit=max&w=146&h=81&fm=webp 146w,\nhttps://images.prismic.io/encord/aBx8zCdWJ-7kRvnR_mgh.avif?auto=format%2Ccompress&fit=max&w=292&h=162&fm=webp 292w,\nhttps://images.prismic.io/encord/aBx8zCdWJ-7kRvnR_mgh.avif?auto=format%2Ccompress&fit=max&w=583&h=324&fm=webp 583w","sizes":"(min-width: 583px) 583px, 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Harvard Medical School and MGH Cut Down Annotation Time From Days to Minutes"},"read_time":null,"sub_image":{"alt":"mgh","url":"https://images.prismic.io/encord/aByP0ydWJ-7kRvzk_Logo_MGH_white_3.png?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/aByP0ydWJ-7kRvzk_Logo_MGH_white_3.png?auto=format%2Ccompress&fit=max&w=2&h=10&fm=webp 2w,\nhttps://images.prismic.io/encord/aByP0ydWJ-7kRvzk_Logo_MGH_white_3.png?auto=format%2Ccompress&fit=max&w=4&h=20&fm=webp 4w,\nhttps://images.prismic.io/encord/aByP0ydWJ-7kRvzk_Logo_MGH_white_3.png?auto=format%2Ccompress&fit=max&w=8&h=40&fm=webp 8w,\nhttps://images.prismic.io/encord/aByP0ydWJ-7kRvzk_Logo_MGH_white_3.png?auto=format%2Ccompress&fit=max&w=16&h=80&fm=webp 16w","sizes":"(min-width: 8px) 8px, 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Stig Hansen"}}}},"content":{"html":"CONXAI is building an AI platform for the Architecture, Engineering and Construction industries to contextualise different data and transform them into actionable insights. CONXAI, however, encountered challenges with optimizing datasets, reviewing labels, and managing large volumes of data with their in-house data annotation solution.
This is where Encord came in - CONXAI was looking for an end-to-end solution for data management and curation, annotation, and evaluation.
CONXAI’s goal is to help AEC teams perform better by organizing and making sense of the vast amount of data generated during different stages of construction projects.
They specialize in making data more useful, especially since a lot of project data often goes unused. Their ultimate aim is to help AEC professionals use AI effectively to improve efficiency and tackle challenges in their projects.
We sat down with Markus Kittel, AI Product Development Manager at CONXAI, to discuss his work overseeing the product roadmap, and their exciting plans ahead for the business.
CONXAI's approach involves working with large unstructured datasets, which leads to challenges in effectively managing and curating project data. Their initial reliance on their in-house solution for data annotation proved to be problematic as the volume of data increased.
Like many in-house tools, it was prone to frequent malfunctions, obscured the data it processed, and lacked mechanisms for reviewing annotations. Additionally, scalability was a major concern, as the in-house tool struggled to handle the increasing volume and complexity of project data.
Without a reliable and scalable data management system in place, they faced difficulties in optimizing datasets and analyzing data effectively. As a result, CONXAI recognized the pressing need for a comprehensive solution that could streamline its data curation and management processes, enabling it to unlock the full potential of AI-driven insights within the AEC industry.
CONXAI were also in need of a solution where data security took precedence, enabling data to remain within CONXAI servers and be accessed via an API or SDK.
“With other labeling tools, we needed to integrate another tool for data management and exploration capabilities, but Encord combined the two needs and provided a single comprehensive solution, along with excellent customer care and support,” Markus says.
To address these challenges, CONXAI explored various annotation tools. They were searching for a single platform that could handle data curation and management seamlessly. Encord's Annotate and Encord Active emerged as the ideal solution, offering a comprehensive platform to streamline CONXAI’s operations.
As Markus says “We connect Encord Active with our large dataset and then use metrics to prioritize building a collection of images. This collection is then sent to Encord Annotate for labeling images in preparation for training. And all this without the data leaving our server.”
With the adoption of Encord into the data pipeline, CONXAI witnessed significant improvements in its data management processes. Encord facilitated the transformation of unstructured data into labeled, training-ready, datasets. The intuitive interface of Encord's Annotate tool simplified the annotation process for CONXAI's team, while also providing robust label review capabilities. Moreover, Encord's Active platform allowed CONXAI to efficiently curate and evaluate their datasets.
{{light_callout_start}} “The labeling speed of the annotation team improved to almost 60% compared to when using their in-house tool.” - Markus Kittel{{light_callout_end}}
CONXAI was able to curate over 40k images with Encord Active. They were then able to efficiently evaluate and prioritize these images based on metrics, facilitating streamlined data management and enhanced decision-making processes within their operations.
CONXAI were able to contribute to Encord’s product roadmap by identifying that mapping relationships between labels in their ontology would enhance model performance. The Encord team were able to deploy this functionality, resulting in an improved user experience for CONXAI.
Overall, using Encord led to enhanced robustness, simplified data pipelines, and a remarkable 60% increase in labeling speed compared to CONXAI's previous in-house tool. This demonstrates how adopting an end-to-end platform with annotation, curation, and evaluation capabilities provides the best solution for computer vision teams.
","text":"CONXAI is building an AI platform for the Architecture, Engineering and Construction industries to contextualise different data and transform them into actionable insights. CONXAI, however, encountered challenges with optimizing datasets, reviewing labels, and managing large volumes of data with their in-house data annotation solution. This is where Encord came in - CONXAI was looking for an end-to-end solution for data management and curation, annotation, and evaluation. Introducing Customer: CONXAI CONXAI’s goal is to help AEC teams perform better by organizing and making sense of the vast amount of data generated during different stages of construction projects. They specialize in making data more useful, especially since a lot of project data often goes unused. Their ultimate aim is to help AEC professionals use AI effectively to improve efficiency and tackle challenges in their projects. We sat down with Markus Kittel, AI Product Development Manager at CONXAI, to discuss his work overseeing the product roadmap, and their exciting plans ahead for the business. Problem: Challenges in Data Curation and Management CONXAI's approach involves working with large unstructured datasets, which leads to challenges in effectively managing and curating project data. Their initial reliance on their in-house solution for data annotation proved to be problematic as the volume of data increased. Like many in-house tools, it was prone to frequent malfunctions, obscured the data it processed, and lacked mechanisms for reviewing annotations. Additionally, scalability was a major concern, as the in-house tool struggled to handle the increasing volume and complexity of project data. Without a reliable and scalable data management system in place, they faced difficulties in optimizing datasets and analyzing data effectively. As a result, CONXAI recognized the pressing need for a comprehensive solution that could streamline its data curation and management processes, enabling it to unlock the full potential of AI-driven insights within the AEC industry. CONXAI were also in need of a solution where data security took precedence, enabling data to remain within CONXAI servers and be accessed via an API or SDK. Solution: Encord Provides a Unified Platform for Data Curation and Management “With other labeling tools, we needed to integrate another tool for data management and exploration capabilities, but Encord combined the two needs and provided a single comprehensive solution, along with excellent customer care and support,” Markus says. To address these challenges, CONXAI explored various annotation tools. They were searching for a single platform that could handle data curation and management seamlessly. Encord's Annotate and Encord Active emerged as the ideal solution, offering a comprehensive platform to streamline CONXAI’s operations. As Markus says “We connect Encord Active with our large dataset and then use metrics to prioritize building a collection of images. This collection is then sent to Encord Annotate for labeling images in preparation for training. And all this without the data leaving our server.” Result: 60% Increase in Labeling Speed With the adoption of Encord into the data pipeline, CONXAI witnessed significant improvements in its data management processes. Encord facilitated the transformation of unstructured data into labeled, training-ready, datasets. The intuitive interface of Encord's Annotate tool simplified the annotation process for CONXAI's team, while also providing robust label review capabilities. Moreover, Encord's Active platform allowed CONXAI to efficiently curate and evaluate their datasets. {{light_callout_start}} “The labeling speed of the annotation team improved to almost 60% compared to when using their in-house tool.” - Markus Kittel{{light_callout_end}} CONXAI was able to curate over 40k images with Encord Active. They were then able to efficiently evaluate and prioritize these images based on metrics, facilitating streamlined data management and enhanced decision-making processes within their operations. CONXAI were able to contribute to Encord’s product roadmap by identifying that mapping relationships between labels in their ontology would enhance model performance. The Encord team were able to deploy this functionality, resulting in an improved user experience for CONXAI. Overall, using Encord led to enhanced robustness, simplified data pipelines, and a remarkable 60% increase in labeling speed compared to CONXAI's previous in-house tool. This demonstrates how adopting an end-to-end platform with annotation, curation, and evaluation capabilities provides the best solution for computer vision teams. 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safety"]}},{"node":{"uid":"voxel-case-study","data":{"author":{"document":{"id":"7eacb635-8381-5dfe-bd83-7f2340c17caf","uid":"ulrik-stig-hansen","data":{"full_name":{"text":"Ulrik Stig Hansen"}}}},"content":{"html":"Voxel is a global leader in workplace safety, empowering worksites by providing them with the data they need to protect workers and gain insight into workplace activities. Their mission is to protect the people who power our world.
We spoke with Anurag Kanungo, the co-founder and CTO, about why he decided to transition to Encord to manage their machine learning pipeline and computer vision projects.
As Voxel grew, they encountered several challenges that hampered their ability to deliver on their mission effectively.
The initial approach to data gathering and analysis wasn't sufficient for scale, leading to difficulties in finding relevant data and a lack of dataset diversity. The frequent changes in work environments, such as uniform updates, posed challenges in accurately updating models with new, unseen data. Also, addressing model edge cases and efficiently scaling the data labeling and analysis process became a prominent issue.
Initially, Voxel trained pipelines using open-source tooling for object detection in videos. While sufficient on a small scale, as Voxel grew and required more complexity, the limitations of these tools became evident. Among others, they faced challenges with the user interface, backend data management, interpolation issues, and label exports. Despite being a good starting point, these tools proved inadequate for scaling operations effectively.
{{light_callout_start}} “…as we started growing and adding more customers and more people using the tool there were certainly a bunch of challenges that came in, like our previous OS labeling tool kept running out of disk, so we had to start doing maintenance ourselves. We had to start editing the code and diverging from the main branch, which we really didn’t want to do…because we wanted to focus on our product.” - Anurag Kanungo {{light_callout_end}}
As Voxel scaled, they sought a more robust solution that had critical features such as video support and image classification.
The decision to transition to Encord marked a significant turning point for Voxel. Encord's video-first approach addressed their need for robust video support, while its innovative features, such as image group classification, stood out. Moreover, Encord's exceptional support and technical design resonated with Voxel's needs, offering a seamless and efficient solution that aligned perfectly with their vision for enhancing workplace safety.
{{light_callout_start}}"We went through a bunch of vendors and one of the things that stood out about Encord was the video first support, which other vendors do not have. Specifically understanding how the video works behind the scenes: the encoding, the frame indexes and square pixel ratios."- Anurag Kanungo {{light_callout_end}}
{{try_encord}}
One of the key requirements for Voxel was the ability to integrate their existing data pipelines into a new solution, which Encord was able to provide seamlessly. This enabled their team to continue to focus on their end solution without being preoccupied with the handover.
Voxel were impressed by the robustness of the platform, enabling them to utilise many of the advanced features enabling them to address the safety issues and ergonomic concerns more effectively, aligning with their overarching mission to reduce workplace risks and ensure a safer environment for all workers
Overall, the adoption of Encord has significantly aided Voxel's approach to workplace safety and efficiency. The platform's integration and its capabilities have empowered Voxel to address safety concerns and optimize operations effectively. With Encord's ongoing support, Voxel is well-equipped to navigate future challenges and drive innovation in workplace safety, setting new standards for operational excellence.
","text":"Introducing Customer: Voxel Voxel is a global leader in workplace safety, empowering worksites by providing them with the data they need to protect workers and gain insight into workplace activities. Their mission is to protect the people who power our world. We spoke with Anurag Kanungo, the co-founder and CTO, about why he decided to transition to Encord to manage their machine learning pipeline and computer vision projects. Problem: Operational Challenges in Data Accessibility and Model Scalability As Voxel grew, they encountered several challenges that hampered their ability to deliver on their mission effectively. The initial approach to data gathering and analysis wasn't sufficient for scale, leading to difficulties in finding relevant data and a lack of dataset diversity. The frequent changes in work environments, such as uniform updates, posed challenges in accurately updating models with new, unseen data. Also, addressing model edge cases and efficiently scaling the data labeling and analysis process became a prominent issue. Initially, Voxel trained pipelines using open-source tooling for object detection in videos. While sufficient on a small scale, as Voxel grew and required more complexity, the limitations of these tools became evident. Among others, they faced challenges with the user interface, backend data management, interpolation issues, and label exports. Despite being a good starting point, these tools proved inadequate for scaling operations effectively. {{light_callout_start}} “…as we started growing and adding more customers and more people using the tool there were certainly a bunch of challenges that came in, like our previous OS labeling tool kept running out of disk, so we had to start doing maintenance ourselves. We had to start editing the code and diverging from the main branch, which we really didn’t want to do…because we wanted to focus on our product.” - Anurag Kanungo {{light_callout_end}} As Voxel scaled, they sought a more robust solution that had critical features such as video support and image classification. Solution: Transitioning to Encord for Scalable and Efficient Video Analysis The decision to transition to Encord marked a significant turning point for Voxel. Encord's video-first approach addressed their need for robust video support, while its innovative features, such as image group classification, stood out. Moreover, Encord's exceptional support and technical design resonated with Voxel's needs, offering a seamless and efficient solution that aligned perfectly with their vision for enhancing workplace safety. {{light_callout_start}}\"We went through a bunch of vendors and one of the things that stood out about Encord was the video first support, which other vendors do not have. Specifically understanding how the video works behind the scenes: the encoding, the frame indexes and square pixel ratios.\"- Anurag Kanungo {{light_callout_end}} {{try_encord}} Results: Impact of Encord on Voxel’s Operations One of the key requirements for Voxel was the ability to integrate their existing data pipelines into a new solution, which Encord was able to provide seamlessly. This enabled their team to continue to focus on their end solution without being preoccupied with the handover. Voxel were impressed by the robustness of the platform, enabling them to utilise many of the advanced features enabling them to address the safety issues and ergonomic concerns more effectively, aligning with their overarching mission to reduce workplace risks and ensure a safer environment for all workers Overall, the adoption of Encord has significantly aided Voxel's approach to workplace safety and efficiency. The platform's integration and its capabilities have empowered Voxel to address safety concerns and optimize operations effectively. With Encord's ongoing support, Voxel is well-equipped to navigate future challenges and drive innovation in workplace safety, setting new standards for operational excellence."},"body":[],"image":{"alt":"containers at port with a ship dicked","url":"https://images.prismic.io/encord/Yu23GbkGsHFNWw0n_updated-cover_voxel_1.avif?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/Yu23GbkGsHFNWw0n_updated-cover_voxel_1.avif?auto=format%2Ccompress&fit=max&w=146&h=81&fm=webp 146w,\nhttps://images.prismic.io/encord/Yu23GbkGsHFNWw0n_updated-cover_voxel_1.avif?auto=format%2Ccompress&fit=max&w=292&h=162&fm=webp 292w,\nhttps://images.prismic.io/encord/Yu23GbkGsHFNWw0n_updated-cover_voxel_1.avif?auto=format%2Ccompress&fit=max&w=583&h=324&fm=webp 583w,\nhttps://images.prismic.io/encord/Yu23GbkGsHFNWw0n_updated-cover_voxel_1.avif?auto=format%2Ccompress&fit=max&w=1166&h=648&fm=webp 1166w","sizes":"(min-width: 583px) 583px, 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AI","Logistics","Human safety"]}},{"node":{"uid":"neurons-case-study","data":{"author":{"document":{"id":"7eacb635-8381-5dfe-bd83-7f2340c17caf","uid":"ulrik-stig-hansen","data":{"full_name":{"text":"Ulrik Stig Hansen"}}}},"content":{"html":"Neurons has been a leading company in the field of neuroscience marketing for more than 10 years. With their expertise in understanding how humans react to content, they have developed AI-powered solutions to enable companies to create predictable marketing content at scale. Their mission is to eliminate biases and guesswork from marketing strategies.
As a pioneer in this domain, Neurons faced scalability challenges in their data annotation process due to the sheer volume of label categories and the growing number of assets requiring annotation.
We sat down with Dennis Green-Lieber (Director of Product and Engineering) and Konstantina Kaisheva (AI Product Manager) to discuss how Encord alleviated these challenges.
Neurons, historically, have relied on manual annotation by their internal team using their in-house annotation platform. With a growing volume of assets requiring annotation, they found it increasingly challenging to scale their operations using their in-house solutions. They also encountered difficulties in ensuring precision and consistency when labeling large volumes of data, resulting in delays in getting their models to production.
Seeking a solution to their data annotation challenges, Neurons turned to Encord’s platform. They recognized the need for manual and automated labeling capabilities, so they were drawn to Encord’s comprehensive offering. With Encord, Neurons found a user-friendly platform that facilitated a precise and efficient annotation process. They appreciated the ability to define labeling instructions/ontologies tailored to their specific requirements, ensuring accuracy in their data sets.
Encord's platform offered features for quality assurance, workflow management, and data categorization streamlining Neurons’ annotation workflows. They were particularly impressed by Encord's flexibility, which allowed them to adapt labeling criteria for different content types.
Konstantina, AI Product Manager, noted her happiness with the function of model evaluations. Recognizing its potential as an invaluable tool for the future, she highlighted its ability to provide insights into the performance and accuracy of its AI models.
{{light_callout_start}}"It's a very easy platform to operate. What I really liked was the whole offering of Encord for both automated labeling and manual work, as precision is incredibly important to us."- Konstantina Kaisheva {{light_callout_end}}
With Encord’s platform, Neurons achieved marked improvements in their data annotation process. With the appropriate workflows and quality assurance, they were able to maintain precision and consistency even when they were handling larger volumes of data. This led to building a higher-quality dataset for their AI models.
Neuron’s newly launched Copilot, provides actionable insights for marketing decision makers. This is run by AI models which require large volumes of precisely annotated datasets, which Encord enabled them to achieve. By eliminating guesswork and biases, they empowered their clients to make informed decisions and optimize their marketing strategies effectively.
Overall, Encord’s platform proved to be a significant asset for Neurons, enabling them to overcome their data annotation challenges. With a newfound ability to make data-driven decisions, the company saw a tangible improvement in its ability to achieve their desired model outcomes.
","text":"Introducing Customer: Neurons Neurons has been a leading company in the field of neuroscience marketing for more than 10 years. With their expertise in understanding how humans react to content, they have developed AI-powered solutions to enable companies to create predictable marketing content at scale. Their mission is to eliminate biases and guesswork from marketing strategies. As a pioneer in this domain, Neurons faced scalability challenges in their data annotation process due to the sheer volume of label categories and the growing number of assets requiring annotation. We sat down with Dennis Green-Lieber (Director of Product and Engineering) and Konstantina Kaisheva (AI Product Manager) to discuss how Encord alleviated these challenges. Problem: Challenges with in-house solution Neurons, historically, have relied on manual annotation by their internal team using their in-house annotation platform. With a growing volume of assets requiring annotation, they found it increasingly challenging to scale their operations using their in-house solutions. They also encountered difficulties in ensuring precision and consistency when labeling large volumes of data, resulting in delays in getting their models to production. Solution: Scaling with Encord's end-to-end platform Seeking a solution to their data annotation challenges, Neurons turned to Encord’s platform. They recognized the need for manual and automated labeling capabilities, so they were drawn to Encord’s comprehensive offering. With Encord, Neurons found a user-friendly platform that facilitated a precise and efficient annotation process. They appreciated the ability to define labeling instructions/ontologies tailored to their specific requirements, ensuring accuracy in their data sets. Encord's platform offered features for quality assurance, workflow management, and data categorization streamlining Neurons’ annotation workflows. They were particularly impressed by Encord's flexibility, which allowed them to adapt labeling criteria for different content types. Konstantina, AI Product Manager, noted her happiness with the function of model evaluations. Recognizing its potential as an invaluable tool for the future, she highlighted its ability to provide insights into the performance and accuracy of its AI models. {{light_callout_start}}\"It's a very easy platform to operate. What I really liked was the whole offering of Encord for both automated labeling and manual work, as precision is incredibly important to us.\"- Konstantina Kaisheva {{light_callout_end}} Result: Efficient and fast annotation With Encord’s platform, Neurons achieved marked improvements in their data annotation process. With the appropriate workflows and quality assurance, they were able to maintain precision and consistency even when they were handling larger volumes of data. This led to building a higher-quality dataset for their AI models. Neuron’s newly launched Copilot, provides actionable insights for marketing decision makers. This is run by AI models which require large volumes of precisely annotated datasets, which Encord enabled them to achieve. By eliminating guesswork and biases, they empowered their clients to make informed decisions and optimize their marketing strategies effectively. Overall, Encord’s platform proved to be a significant asset for Neurons, enabling them to overcome their data annotation challenges. With a newfound ability to make data-driven decisions, the company saw a tangible improvement in its ability to achieve their desired model outcomes."},"body":[{}],"image":{"alt":"neurons marketing analysis on taking a picture of skincare product","url":"https://images.prismic.io/encord/aBxprSdWJ-7kRvap_neurons.avif?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/aBxprSdWJ-7kRvap_neurons.avif?auto=format%2Ccompress&fit=max&w=146&h=81&fm=webp 146w,\nhttps://images.prismic.io/encord/aBxprSdWJ-7kRvap_neurons.avif?auto=format%2Ccompress&fit=max&w=292&h=162&fm=webp 292w,\nhttps://images.prismic.io/encord/aBxprSdWJ-7kRvap_neurons.avif?auto=format%2Ccompress&fit=max&w=583&h=324&fm=webp 583w","sizes":"(min-width: 583px) 583px, 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the Prediction of Consumer Behaviour with Encord"},"read_time":null,"sub_image":{"alt":"neurons","url":"https://images.prismic.io/encord/aBxp4ydWJ-7kRvaw_Logo_neurons_white.png?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/aBxp4ydWJ-7kRvaw_Logo_neurons_white.png?auto=format%2Ccompress&fit=max&w=2&h=10&fm=webp 2w,\nhttps://images.prismic.io/encord/aBxp4ydWJ-7kRvaw_Logo_neurons_white.png?auto=format%2Ccompress&fit=max&w=4&h=20&fm=webp 4w,\nhttps://images.prismic.io/encord/aBxp4ydWJ-7kRvaw_Logo_neurons_white.png?auto=format%2Ccompress&fit=max&w=8&h=40&fm=webp 8w,\nhttps://images.prismic.io/encord/aBxp4ydWJ-7kRvaw_Logo_neurons_white.png?auto=format%2Ccompress&fit=max&w=16&h=80&fm=webp 16w","sizes":"(min-width: 8px) 8px, 100vw","type":"image/webp"}],"fallback":{"src":"https://images.prismic.io/encord/aBxp4ydWJ-7kRvaw_Logo_neurons_white.png?auto=format%2Ccompress&fit=max&w=8&h=40","srcSet":"https://images.prismic.io/encord/aBxp4ydWJ-7kRvaw_Logo_neurons_white.png?auto=format%2Ccompress&fit=max&w=2&h=10 2w,\nhttps://images.prismic.io/encord/aBxp4ydWJ-7kRvaw_Logo_neurons_white.png?auto=format%2Ccompress&fit=max&w=4&h=20 4w,\nhttps://images.prismic.io/encord/aBxp4ydWJ-7kRvaw_Logo_neurons_white.png?auto=format%2Ccompress&fit=max&w=8&h=40 8w,\nhttps://images.prismic.io/encord/aBxp4ydWJ-7kRvaw_Logo_neurons_white.png?auto=format%2Ccompress&fit=max&w=16&h=80 16w","sizes":"(min-width: 8px) 8px, 100vw"}},"layout":"constrained","placeholder":{"fallback":"data:image/png;base64,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"},"width":8,"height":40}}},"first_publication_date":"2024-04-19T10:01:34+0000","tags":["Customers","Consumer"]}},{"node":{"uid":"pickle-robot","data":{"author":{"document":{"id":"402d4f74-b4f3-52dc-9511-d9b72f2713d8","uid":"alexandre-bonnet","data":{"full_name":{"text":"Alexandre Bonnet"}}}},"content":{"html":"Pickle Robot, a Cambridge, MA-based Physical AI innovator, is revolutionizing the logistics industry with cutting-edge applications of hardware, AI, and data. Focused first on optimizing truck unloading and loading, Pickle Robot streamlines one of the most physically demanding tasks in warehousing.
Historically, the process of unloading non-palletized goods from trailers relied heavily on manual labor, resulting in fatigue and inefficiencies and prone to high worker safety risks.
To address these challenges, a team of MIT graduates came together to form Pickle Robot. They developed a sophisticated solution that uses advanced AI algorithms trained on highly accurate images and videos to automate the unloading of up to 1,500 packages per hour with its green mobile manipulation robots.
The purpose-built solution combines custom AI/ML, highly contextualized data, and off-the-shelf hardware and sensors, significantly reducing the time, effort, and risk in an often overlooked part of the supply chain that warehouses spend over $100 Billion a year to do. Today, they are helping their customers like UPS, Ryobi Tools, and Randa Apparel & Accessories unload millions of pounds of packages monthly.
To build fast and precise robotic systems, Pickle Robot needed to think about the end-to-end experience while facing hurdles across hardware and AI models. Specifically on the model front, Pickle Robot recognized the need for a robust AI system capable of handling diverse cargo. This led the team to implement a unique combination of sensors and machine learning models to identify different types of packages and manipulate various goods accurately. The integration of these technologies enhanced operational efficiency and minimized errors and downtime. Achieving success required a robust data engine and rich images with precise annotations.
Prior to Encord, Pickle Robot used data annotation services from other providers. It relied on an outsourced labeling team to conduct the labeling within the software's limitations and the skills of the outsourced labelers. Challenges included:
Accuracy in training data is critical when your business depends on the accuracy and efficiency of the robotics system's performance.
To address these challenges, Pickle Robot made a strategic decision to partner with Encord.
With Encord, Pickle Robot gained a platform that does data curation, annotation, and provides robust analytics and model evaluation functionality, with full integration to Pickle Robot's Google Cloud Platform based data engine infrastructure.
The Encord platform enables data management and discoverability capabilities, granular annotation features (bounding boxes, polylines, key points, bitmasks, and polygons), nested ontologies, collaborative workflows, AI-assisted labeling with HITL, and comprehensive data curation functionality required to run efficient data pipelines.
{{light_callout_start}} "For our AI initiatives, rapid iteration is critical. Encord and our ML infrastructure allow us to prototype learning tasks efficiently. The composability of Encord enables us to merge diverse data sources, facilitating extensive experimentation. With a well-integrated SDK, it's a matter of a few lines of code to achieve seamless integration and functionality." - Matt Pearce, Applied ML, Pickle Robot {{light_callout_end}}
Since Pickle Robot partnered with Encord, Pickle Robot has seen a drastic improvement in the AI and ML engineers’ productivity, improved precision in task execution, and faster time-to-model improvement.
Key benefits:
Retailers today face some of the toughest challenges in physical stores — from tracking shopper behavior to optimizing promotions and in-store media.
Standard AI solves these challenges by equipping retailers and CPG brands with AI-powered computer vision technology that unlocks real-time insights on promotions, media impact, new product launches, and more. The VISION platform turns data into actionable insights, helping retailers drive sales, improve store performance, and gain deeper visibility into the shopper journey.
Standard AI is equipping brick-and-mortar stores and their suppliers with real-time in-store analytics, all while maintaining a privacy-first approach. Helmed by an executive team that spans firms like Mars, Sara Lee, Oracle, NASA, and Adobe, Standard AI is at the forefront of applying artificial intelligence and computer vision to physical retail spaces.
At its core, Standard AI’s business is based on its ability to extract meaningful insights from millions of hours of video footage from store’s security cameras.
For the ML team, access to high-quality and enriched video data was an extremely cumbersome, time-consuming process that relied on multiple internal and agency hand-off points, lengthy quality control review cycles, and fragmented point solutions. In several instances, custom scripts were needed to glue these workflows together. The ML team spent more time managing fragile data pipelines than focusing on model performance.
The product and ML teams also wanted to find a way to streamline the data curation and annotation processes and spend more time evaluating and fine-tuning their models to continue improving performance. This wasn’t possible due to limitations of Standard AI’s existing tooling, which offered limited debugging, quality control, and statistical insights.
From a business perspective, Standard AI understood that the way of managing their AI data pipeline wouldn’t scale or support the rapid innovation needed to meet customer demands. Standard AI’s Product and ML team defined strict requirements for their updated approach:
{{testimonial_mondrian}}
{{testimonial_mondrian}}
{{testimonial_mondrian}}
Since moving to Encord, Standard AI has seen a drastic improvement in productivity across its product and ML teams, significant cost savings, improved data quality, and faster time-to-model improvement. Key benefits:
Archetype AI is building the first developer platform for Physical AI — powered by Newton, a foundation model trained on real-world sensor data. The platform enables enterprises to build and deploy custom AI applications for the physical world through simple APIs and no-code tools. With deep multimodal sensor fusion, Newton delivers rich, accurate insights across industries — from predictive maintenance to human behavior understanding. They're working with global leaders like Mercedes-Benz, NTT Data, and SLB, and backed by Venrock, Amazon, and Bezos Expeditions.
Prior to adopting Encord, Archetype AI managed data curation and annotation through a combination of internally developed tools and third-party platforms. A key limitation was the lack of native video support within their primary annotation tool. Without the ability to annotate directly on video, annotators were forced to label frame by frame — an inefficient and labor-intensive process that significantly hindered throughput. Object tracking capabilities were similarly constrained, leading to inconsistent performance and difficulty in maintaining continuity across frames.
As the scale and complexity of the data increased, maintaining label accuracy was difficult due to limited visibility in the annotation interface, and the overall user experience lacked the flexibility required for managing high-volume video datasets. These inefficiencies created friction throughout the pipeline, delaying project timelines and impacting the quality of data used for model development.
Recognizing the need for a more robust and purpose-built solution, the team began evaluating platforms capable of supporting scalable, high-performance video and multimodal annotation workflows.
{{testimonial_mondrian}}
Since adopting Encord, the team has achieved a 70% increase in overall productivity. Most significantly, annotation speed has doubled, allowing the team to work far more efficiently than with previous tooling. This improvement is driven by Encord’s fast and intuitive annotation tools, powerful features like trackers and bulk operations, and most importantly, the flexibility of Encord's SDK, which enabled the team to build custom ETL pipelines for seamless data ingestion and annotation retrieval. Tasks that previously required manual upload/download steps and browser-heavy interactions are now fully streamlined. Together, these capabilities have drastically streamlined Archetype AI’s end-to-end labeling workflow.
Encord’s intuitive interface has streamlined project setup and management. It’s now easier for them to design workflows, assign and review tasks, and maintain full visibility across active projects. Labeling accuracy has also improved, leading to tangible gains in model performance, highlighting the impact of having the right data labeled correctly from the start.
The platform made it easy for the team to experiment with new annotation methodologies and iterate quickly, without being constrained by tooling limitations. This flexibility introduced a new level of agility, enabling the team to adapt and innovate faster across their data pipeline.
Collectively, these benefits have resulted in substantial time savings, improved model outcomes, and a more scalable and adaptable approach to handling complex multimodal data.
","text":"Introducing Customer: Archetype AI Archetype AI is building the first developer platform for Physical AI — powered by Newton, a foundation model trained on real-world sensor data. The platform enables enterprises to build and deploy custom AI applications for the physical world through simple APIs and no-code tools. With deep multimodal sensor fusion, Newton delivers rich, accurate insights across industries — from predictive maintenance to human behavior understanding. They're working with global leaders like Mercedes-Benz, NTT Data, and SLB, and backed by Venrock, Amazon, and Bezos Expeditions. Before Encord Prior to adopting Encord, Archetype AI managed data curation and annotation through a combination of internally developed tools and third-party platforms. A key limitation was the lack of native video support within their primary annotation tool. Without the ability to annotate directly on video, annotators were forced to label frame by frame — an inefficient and labor-intensive process that significantly hindered throughput. Object tracking capabilities were similarly constrained, leading to inconsistent performance and difficulty in maintaining continuity across frames. As the scale and complexity of the data increased, maintaining label accuracy was difficult due to limited visibility in the annotation interface, and the overall user experience lacked the flexibility required for managing high-volume video datasets. These inefficiencies created friction throughout the pipeline, delaying project timelines and impacting the quality of data used for model development. Recognizing the need for a more robust and purpose-built solution, the team began evaluating platforms capable of supporting scalable, high-performance video and multimodal annotation workflows.\n The Encord Solution {{testimonial_mondrian}} Since adopting Encord, the team has achieved a 70% increase in overall productivity. Most significantly, annotation speed has doubled, allowing the team to work far more efficiently than with previous tooling. This improvement is driven by Encord’s fast and intuitive annotation tools, powerful features like trackers and bulk operations, and most importantly, the flexibility of Encord's SDK, which enabled the team to build custom ETL pipelines for seamless data ingestion and annotation retrieval. Tasks that previously required manual upload/download steps and browser-heavy interactions are now fully streamlined. Together, these capabilities have drastically streamlined Archetype AI’s end-to-end labeling workflow. Encord’s intuitive interface has streamlined project setup and management. It’s now easier for them to design workflows, assign and review tasks, and maintain full visibility across active projects. Labeling accuracy has also improved, leading to tangible gains in model performance, highlighting the impact of having the right data labeled correctly from the start.\n The platform made it easy for the team to experiment with new annotation methodologies and iterate quickly, without being constrained by tooling limitations. This flexibility introduced a new level of agility, enabling the team to adapt and innovate faster across their data pipeline. Collectively, these benefits have resulted in substantial time savings, improved model outcomes, and a more scalable and adaptable approach to handling complex multimodal data."},"body":[{},{}],"image":{"alt":"Satellite over earth","url":"https://images.prismic.io/encord/QE34VadNbUoc9jdz_updated-cover_archetype_1.avif?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/QE34VadNbUoc9jdz_updated-cover_archetype_1.avif?auto=format%2Ccompress&fit=max&w=146&h=81&fm=webp 146w,\nhttps://images.prismic.io/encord/QE34VadNbUoc9jdz_updated-cover_archetype_1.avif?auto=format%2Ccompress&fit=max&w=292&h=162&fm=webp 292w,\nhttps://images.prismic.io/encord/QE34VadNbUoc9jdz_updated-cover_archetype_1.avif?auto=format%2Ccompress&fit=max&w=583&h=324&fm=webp 583w,\nhttps://images.prismic.io/encord/QE34VadNbUoc9jdz_updated-cover_archetype_1.avif?auto=format%2Ccompress&fit=max&w=1166&h=648&fm=webp 1166w","sizes":"(min-width: 583px) 583px, 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AI"]}},{"node":{"uid":"zeitview","data":{"author":{"document":{"id":"b8a614e1-9a7a-5f51-b1ef-1a6f1871e559","uid":"mayanayyar","data":{"full_name":{"text":"Maya Nayyar"}}}},"content":{"html":"Zeitview, a provider of advanced inspection solutions for the energy and infrastructure sectors, significantly improved performance of their rooftop penetration detection model by focusing on high quality data delivery.
We spoke with Jonathan Lwowski, Head of AI/ML, and Conor Wallace, Machine Learning Engineer, to understand how leveraging Encord helped improve data quality, tightened their feedback loop, and accelerated the deployment of machine learning models into production.
Initial training data for Zeitview's rooftop penetration detection models suffered from quality issues. The first dataset was labeled by 15 external contractors using a third-party tool, resulting in inconsistent annotations and suboptimal model performance.
Zeitview improved data quality using the Encord platform:
These changes yielded meaningful results:
The recall improvement is particularly significant for Zeitview's inspection use case, representing a meaningful enhancement in detection reliability.
Encord's platform enabled several workflow improvements:
As Jonathan notes, human labeling data can be expensive and time-consuming if not done efficiently:
{{testimonial_mondrian}}
For complex projects with no reference data – like rooftop penetrations – Zeitview now leverages image similarity search and image quality metrics to curate high-quality datasets. This approach allows the team to rapidly identify edge cases and ensure diverse training datasets.
{{testimonial_mondrian}}
![](\"https://images.prismic.io/encord/aHoYl0MqNJQqIGDx_Indexsimilaritysearch-min.avif?auto=format,compress\")
Zeitview moved from spreadsheet-based feedback to a three-round QA workflow built within Encord, integrating ML engineers directly into the review process. As label accuracy improves, they are able to gradually reduce human oversight.
{{testimonial_mondrian}}
![](\"https://images.prismic.io/encord/aHoYpEMqNJQqIGDy_Collaborate-updated-min.avif?auto=format,compress\")
Zeitview is preparing to use Encord Active to close the loop between model predictions and data curation. The goal: trigger automated retraining when failed predictions are detected, ensuring continuous model improvement.
Zeitview integrated Encord’s platform to centralize their data operations and automate key parts of the annotation and feedback workflow, enabling greater efficiency and consistency across teams.
With Encord, Zeitview has established a foundation for trustworthy, production-ready datasets and continuous model improvement - delivering faster iteration cycles, higher label quality, and more accurate inspection models.
","text":"Zeitview, a provider of advanced inspection solutions for the energy and infrastructure sectors, significantly improved performance of their rooftop penetration detection model by focusing on high quality data delivery. \n We spoke with Jonathan Lwowski, Head of AI/ML, and Conor Wallace, Machine Learning Engineer, to understand how leveraging Encord helped improve data quality, tightened their feedback loop, and accelerated the deployment of machine learning models into production. Key results Initial training data for Zeitview's rooftop penetration detection models suffered from quality issues. The first dataset was labeled by 15 external contractors using a third-party tool, resulting in inconsistent annotations and suboptimal model performance. \nZeitview improved data quality using the Encord platform: Moved from external contractors to a smaller, specialized 5-person internal labeling team Implemented robust QA workflows for systematic quality control Relabeled problematic data while simultaneously expanding the dataset \nThese changes yielded meaningful results: 3.67% boost in precision 12.33% boost in recall 2x increase in dataset size and throughput Reduced team size by ⅓ \nThe recall improvement is particularly significant for Zeitview's inspection use case, representing a meaningful enhancement in detection reliability. \n How did Zeitview achieve these results? Encord's platform enabled several workflow improvements: Consolidate labeling and QA operations within a single environment Implemented structured QA/QC workflows that integrated MLEs directly into the feedback loop Leveraged image similarity search to quickly curate high-quality datasets As Jonathan notes, human labeling data can be expensive and time-consuming if not done efficiently: {{testimonial_mondrian}} Intelligent data curation For complex projects with no reference data – like rooftop penetrations – Zeitview now leverages image similarity search and image quality metrics to curate high-quality datasets. This approach allows the team to rapidly identify edge cases and ensure diverse training datasets. {{testimonial_mondrian}} Automated QA Zeitview moved from spreadsheet-based feedback to a three-round QA workflow built within Encord, integrating ML engineers directly into the review process. As label accuracy improves, they are able to gradually reduce human oversight. {{testimonial_mondrian}} Active learning Zeitview is preparing to use Encord Active to close the loop between model predictions and data curation. The goal: trigger automated retraining when failed predictions are detected, ensuring continuous model improvement. Summary Zeitview integrated Encord’s platform to centralize their data operations and automate key parts of the annotation and feedback workflow, enabling greater efficiency and consistency across teams. With Encord, Zeitview has established a foundation for trustworthy, production-ready datasets and continuous model improvement - delivering faster iteration cycles, higher label quality, and more accurate inspection models."},"body":[{},{},{},{}],"image":{"alt":"wind turbines over rolling fields","url":"https://images.prismic.io/encord/pyMQioU3_eOThTOF_updated-cover_zeitview_1.avif?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/pyMQioU3_eOThTOF_updated-cover_zeitview_1.avif?auto=format%2Ccompress&fit=max&w=146&h=81&fm=webp 146w,\nhttps://images.prismic.io/encord/pyMQioU3_eOThTOF_updated-cover_zeitview_1.avif?auto=format%2Ccompress&fit=max&w=292&h=162&fm=webp 292w,\nhttps://images.prismic.io/encord/pyMQioU3_eOThTOF_updated-cover_zeitview_1.avif?auto=format%2Ccompress&fit=max&w=583&h=324&fm=webp 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Improves Recall by 12% and Doubles Data Throughput Using Encord"},"read_time":null,"sub_image":{"alt":null,"url":"https://images.prismic.io/encord/aHoXp0MqNJQqIGDZ_zeitviewlogo.avif?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/aHoXp0MqNJQqIGDZ_zeitviewlogo.avif?auto=format%2Ccompress&fit=max&w=2&h=10&fm=webp 2w,\nhttps://images.prismic.io/encord/aHoXp0MqNJQqIGDZ_zeitviewlogo.avif?auto=format%2Ccompress&fit=max&w=4&h=20&fm=webp 4w,\nhttps://images.prismic.io/encord/aHoXp0MqNJQqIGDZ_zeitviewlogo.avif?auto=format%2Ccompress&fit=max&w=8&h=40&fm=webp 8w,\nhttps://images.prismic.io/encord/aHoXp0MqNJQqIGDZ_zeitviewlogo.avif?auto=format%2Ccompress&fit=max&w=16&h=80&fm=webp 16w","sizes":"(min-width: 8px) 8px, 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AI"]}},{"node":{"uid":"onsiteiq","data":{"author":{"document":{"id":"b8a614e1-9a7a-5f51-b1ef-1a6f1871e559","uid":"mayanayyar","data":{"full_name":{"text":"Maya Nayyar"}}}},"content":{"html":"OnsiteIQ, a construction intelligence platform using computer vision for safety and quality inspections, migrated their data workflows to Encord after overhauling their AI infrastructure. This enabled them to become operational 4x faster and achieve 5x data throughput with their existing team.
"Encord integrates seamlessly into our entire AI infrastructure," says Evgeny Nuger, Principal Engineer at OnsiteIQ. "By implementing Encord within our redesigned ML infrastructure, we've established an efficient end-to-end workflow from data sampling through to model training."
OnsiteIQ faced significant limitations with their previous annotation platform, struggling with poor usability and underperforming automation capabilities that hindered their workflow efficiency.
After evaluating several vendors, the team chose Encord for its advanced features and intuitive interface, particularly the SAM 2 integration for automated labeling workflows.
{{testimonial_mondrian}}
The results:
![\"Analytics](\"https://images.prismic.io/encord/aKbijqTt2nPbakGM_Analytics-asset_2.avif?auto=format,compress\")
The automation capabilities paired with the UX of Encord's platform were decisive factors in OnsiteIQ's selection:
{{testimonial_mondrian}}
Despite offering sophisticated organization with distinct layers for datasets, projects, ontologies, and files, the team found Encord's platform surprisingly intuitive, effectively balancing complexity with usability. Evgeny notes, "The UX on Encord's platform was way more clear as to how everything comes together."
The implementation timeline also improved dramatically. The team saw a 4x acceleration in their time to value, reducing time to value from 2 months to just 2 weeks:
{{testimonial_mondrian}}
OnsiteIQ now utilizes Encord's built-in analytics to optimize their operations:
{{testimonial_mondrian}}
The fully integrated system has improved operational efficiency for OnsiteIQ. The company is now actively using the data generated through Encord for deploying models into production.
","text":"OnsiteIQ, a construction intelligence platform using computer vision for safety and quality inspections, migrated their data workflows to Encord after overhauling their AI infrastructure. This enabled them to become operational 4x faster and achieve 5x data throughput with their existing team. \"Encord integrates seamlessly into our entire AI infrastructure,\" says Evgeny Nuger, Principal Engineer at OnsiteIQ. \"By implementing Encord within our redesigned ML infrastructure, we've established an efficient end-to-end workflow from data sampling through to model training.\" Platform Migration + Key Results OnsiteIQ faced significant limitations with their previous annotation platform, struggling with poor usability and underperforming automation capabilities that hindered their workflow efficiency. After evaluating several vendors, the team chose Encord for its advanced features and intuitive interface, particularly the SAM 2 integration for automated labeling workflows. {{testimonial_mondrian}} The results: 5x improvement in data throughput – with corresponding decrease in labeling costs through advanced automation features 75% reduction in time to value – implementation time decreased from 2 months to just 2 weeks Real-time annotation analytics – simplified team management through Encord's monitoring dashboard Superior Automation and Intuitive User Experience The automation capabilities paired with the UX of Encord's platform were decisive factors in OnsiteIQ's selection: {{testimonial_mondrian}} Despite offering sophisticated organization with distinct layers for datasets, projects, ontologies, and files, the team found Encord's platform surprisingly intuitive, effectively balancing complexity with usability. Evgeny notes, \"The UX on Encord's platform was way more clear as to how everything comes together.\" Accelerated Time to Value The implementation timeline also improved dramatically. The team saw a 4x acceleration in their time to value, reducing time to value from 2 months to just 2 weeks: {{testimonial_mondrian}} Improved Team Management OnsiteIQ now utilizes Encord's built-in analytics to optimize their operations: {{testimonial_mondrian}} The fully integrated system has improved operational efficiency for OnsiteIQ. 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Achieves 5x Faster Data Throughput and Streamlines Their AI Infrastructure"},"read_time":null,"sub_image":{"alt":"Onsite IQ Logo","url":"https://images.prismic.io/encord/aZ83-MFoBIGEgywE_Logo_onsite-iq_white.avif?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/aZ83-MFoBIGEgywE_Logo_onsite-iq_white.avif?auto=format%2Ccompress&fit=max&w=2&h=10&fm=webp 2w,\nhttps://images.prismic.io/encord/aZ83-MFoBIGEgywE_Logo_onsite-iq_white.avif?auto=format%2Ccompress&fit=max&w=4&h=20&fm=webp 4w,\nhttps://images.prismic.io/encord/aZ83-MFoBIGEgywE_Logo_onsite-iq_white.avif?auto=format%2Ccompress&fit=max&w=8&h=40&fm=webp 8w,\nhttps://images.prismic.io/encord/aZ83-MFoBIGEgywE_Logo_onsite-iq_white.avif?auto=format%2Ccompress&fit=max&w=16&h=80&fm=webp 16w","sizes":"(min-width: 8px) 8px, 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AI"]}},{"node":{"uid":"yutori","data":{"author":{"document":{"id":"7eacb635-8381-5dfe-bd83-7f2340c17caf","uid":"ulrik-stig-hansen","data":{"full_name":{"text":"Ulrik Stig Hansen"}}}},"content":{"html":"Yutori is reimagining how people interact with the web. Founded in 2024 by former Meta AI leaders Devi Parikh, Dhruv Batra and Abhishek Das, the San Francisco startup builds autonomous web agents that reliably execute complex tasks – from booking reservations to researching products and making purchases.
In November 2025, Yutori launched Navigator, a web agent that operates a real browser to autonomously complete web tasks. Navigator outperforms Gemini 2.5, Claude 4.0 and 4.5, and OpenAI's Operator by 10-20 percentage points in accuracy while being 2-3x faster.
Foundation models struggle with dynamic interfaces, multi-step workflows, ambiguous user intent, and the need to reason about both what to do and how to do it.
"Models aren't ready yet to deliver this out of the box," Devi explained. "In sequential decision making problems, errors compound at each step. We need to push on modeling capabilities to complete complex workflows on the web with high reliability. To this end, it is critical for data that models are trained on to be high quality. It's the starting point for where model capabilities will come from."
Yutori faced critical data challenges. Supervised fine-tuning requires high quality human-annotated trajectories. Every action an annotator takes on the web while completing a specified task – including any exploration and backtracking – needs to be accurately recorded while keeping the annotation interface simple. Trajectory evaluation can’t be fully automated yet across the entire diversity of tasks. Incorrect actions or subgoals along the way may still lead to successful outcomes. That needs to be appropriately accounted for. While quality is most important, throughput of data annotation also needs to be reasonable to make fast progress.
We partnered with Yutori to develop data infrastructure spanning the agent development lifecycle.
We created a tailored pipeline for supervised fine-tuning:
{{testimonial_mondrian}}
As Yutori's customer base grew, evaluating agent performance at scale became critical. We worked together to build a comprehensive evaluation framework:
The partnership enabled Yutori to launch Navigator as the highest-performing web agent available:
![\"Yutori](\"https://images.prismic.io/encord/aWXIMAIvOtkhBadn_merged_grid.gif?auto=format,compress\")
Yutori agents completing autonomous tasks (Videos courtesy of Yutori)
You can try Yutori’s product Scouts or build on their state-of-the-art web agentic tech via their API.
As agentic AI expands into web browsing, coding assistants, and customer service, evaluating performance in production becomes critical. Our work with Yutori demonstrates what frontier AI teams need:
If you're building agentic AI that needs to work reliably in the real world, your data pipeline quality will determine whether your agents deliver on their promise.
","text":"Key Stats Thousands of weekly trajectory evaluations with 20+ evolving error categories Systematic head-to-head model comparisons across hundreds of trajectory pairs weekly to quantify performance gains Yutori Navigator outperforms Gemini 2.5 and Claude 4.5 by 10-20 percentage points on real-world web tasks Yutori is reimagining how people interact with the web. Founded in 2024 by former Meta AI leaders Devi Parikh, Dhruv Batra and Abhishek Das, the San Francisco startup builds autonomous web agents that reliably execute complex tasks – from booking reservations to researching products and making purchases. In November 2025, Yutori launched Navigator, a web agent that operates a real browser to autonomously complete web tasks. Navigator outperforms Gemini 2.5, Claude 4.0 and 4.5, and OpenAI's Operator by 10-20 percentage points in accuracy while being 2-3x faster. The Challenge: Foundation Models Can't Navigate the Real Web Foundation models struggle with dynamic interfaces, multi-step workflows, ambiguous user intent, and the need to reason about both what to do and how to do it. \"Models aren't ready yet to deliver this out of the box,\" Devi explained. \"In sequential decision making problems, errors compound at each step. We need to push on modeling capabilities to complete complex workflows on the web with high reliability. To this end, it is critical for data that models are trained on to be high quality. It's the starting point for where model capabilities will come from.\" Yutori faced critical data challenges. Supervised fine-tuning requires high quality human-annotated trajectories. Every action an annotator takes on the web while completing a specified task – including any exploration and backtracking – needs to be accurately recorded while keeping the annotation interface simple. Trajectory evaluation can’t be fully automated yet across the entire diversity of tasks. Incorrect actions or subgoals along the way may still lead to successful outcomes. That needs to be appropriately accounted for. While quality is most important, throughput of data annotation also needs to be reasonable to make fast progress. How Encord Built a Scalable Data Pipeline for Yutori We partnered with Yutori to develop data infrastructure spanning the agent development lifecycle. High-Quality Training Data We created a tailored pipeline for supervised fine-tuning: Human-annotated trajectories Iterative quality control with strict standards enforced by both teams {{testimonial_mondrian}} Large-Scale Evaluation As Yutori's customer base grew, evaluating agent performance at scale became critical. We worked together to build a comprehensive evaluation framework: Custom error taxonomy: We iteratively developed 20+ error types capturing action prediction, model reasoning and infra errors. Error categories evolved weekly as Yutori identified new model behaviors. We trained our team to adapt quickly while maintaining quality. Massive scale: We conducted thousands of trajectory evaluations per week, evolving from task outcome evaluation to detailed error categorization of both the process and outcome of the agentic trace. Model evaluations: Beyond single trajectory and Online Mind2Web evaluations, we conduct systematic head-to-head comparisons for hundreds of trajectory pairs weekly where different models attempt identical prompts. Our framework has captured signals across multiple performance dimensions - identifying improvement areas and quantifying Navigator's gains over Claude 4.5 and Gemini 2.5. Model-in-the-loop: We balanced human expertise with AI efficiency. Over time, human annotators provided the correct action labels only when models made mistakes. The Result: Yutori Navigator Sets a New Standard The partnership enabled Yutori to launch Navigator as the highest-performing web agent available: 10-20 percentage points higher in accuracy than Gemini 2.5, Claude 4.0 and 4.5, and OpenAI's Operator 2-3x faster task completion with lower inference costs Uniformly preferred by users in head-to-head evaluations Yutori agents completing autonomous tasks (Videos courtesy of Yutori) You can try Yutori’s product Scouts or build on their state-of-the-art web agentic tech via their API. What This Means for Agentic AI As agentic AI expands into web browsing, coding assistants, and customer service, evaluating performance in production becomes critical. Our work with Yutori demonstrates what frontier AI teams need: Flexible data infrastructure that adapts to key experimental needs as model capabilities evolve Custom workflows for each development phase Quality and quantity of training data Human-model collaboration balancing automation with nuanced understanding Structured evaluation frameworks capturing fine-grained signals If you're building agentic AI that needs to work reliably in the real world, your data pipeline quality will determine whether your agents deliver on their promise."},"body":[{}],"image":{"alt":"Laptops, tablet, and phones in use at a table","url":"https://images.prismic.io/encord/aWUuAAIvOtkhBWSU_Yutoria_case-study-image_2.avif?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/aWUuAAIvOtkhBWSU_Yutoria_case-study-image_2.avif?auto=format%2Ccompress&fit=max&w=146&h=81&fm=webp 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achieved 2-3x faster task completion with lower inference costs"},"read_time":null,"sub_image":{"alt":"Yutori logo","url":"https://images.prismic.io/encord/aV6MeHNYClf9o3df_Logo_yutori_white.avif?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/aV6MeHNYClf9o3df_Logo_yutori_white.avif?auto=format%2Ccompress&fit=max&w=2&h=10&fm=webp 2w,\nhttps://images.prismic.io/encord/aV6MeHNYClf9o3df_Logo_yutori_white.avif?auto=format%2Ccompress&fit=max&w=4&h=20&fm=webp 4w,\nhttps://images.prismic.io/encord/aV6MeHNYClf9o3df_Logo_yutori_white.avif?auto=format%2Ccompress&fit=max&w=8&h=40&fm=webp 8w,\nhttps://images.prismic.io/encord/aV6MeHNYClf9o3df_Logo_yutori_white.avif?auto=format%2Ccompress&fit=max&w=16&h=80&fm=webp 16w","sizes":"(min-width: 8px) 8px, 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Agents "]}},{"node":{"uid":"mmi","data":{"author":{"document":{"id":"7eacb635-8381-5dfe-bd83-7f2340c17caf","uid":"ulrik-stig-hansen","data":{"full_name":{"text":"Ulrik Stig Hansen"}}}},"content":{"html":"MMI is building the future of digital and robotic microsurgery. Their work sits at the intersection of robotics, computer vision, and AI, supporting surgeons who operate at an extremely small scale where precision, tremor reduction, and motion control are critical.
To power these systems, MMI relies on large volumes of high-quality annotated video data. But microsurgical data presents unique challenges: it comes from multiple sources, requires extreme precision, and must be curated and validated carefully to ensure stable model performance across environments.
As their computer vision efforts scaled, manual data handling and annotation became a bottleneck. Engineers needed to focus on model optimisation, testing, and innovation, not spending disproportionate time curating datasets, reviewing annotations, or managing fragmented workflows.
MMI needed a solution that could:
MMI adopted Encord as a core part of their computer vision and data operations, using it across annotation, review, data curation, and automation.
With Encord, the team gained access to:
Engineers across roles, from senior CV engineers to PhD researchers and interns, use Encord daily to review annotations, visualise edge cases, automate parts of the workflow, and move faster from experimentation to validation.
{{testimonial_mondrian}}
By integrating Encord into their AI-driven workflows, MMI significantly improved both operational efficiency and model quality.
Annotation and review cycles became faster and more consistent, allowing the team to iterate on multiple model versions more quickly. Improved dataset quality translated directly into more stable model performance across different test environments.
Most importantly, Encord freed up valuable engineering time. Instead of manually handling data, MMI’s team can focus on what matters most: advancing the state of the art in digital surgery, developing assistive and autonomous capabilities, and delivering better outcomes for surgeons and patients.
As MMI continues to expand its digital surgery platforms, including first-in-human deployments, Encord remains a critical partner in enabling scalable, reliable, and high-quality computer vision development.
","text":"The Challenge: Scaling Computer Vision for Robotic Microsurgery MMI is building the future of digital and robotic microsurgery. Their work sits at the intersection of robotics, computer vision, and AI, supporting surgeons who operate at an extremely small scale where precision, tremor reduction, and motion control are critical. To power these systems, MMI relies on large volumes of high-quality annotated video data. But microsurgical data presents unique challenges: it comes from multiple sources, requires extreme precision, and must be curated and validated carefully to ensure stable model performance across environments. As their computer vision efforts scaled, manual data handling and annotation became a bottleneck. Engineers needed to focus on model optimisation, testing, and innovation, not spending disproportionate time curating datasets, reviewing annotations, or managing fragmented workflows. MMI needed a solution that could: Scale annotation and review efficiently Support complex video-based computer vision workflows Integrate tightly with their existing AI pipelines Improve dataset consistency and model stability Reduce the time from hypothesis to model validation The Solution: Encord as the Backbone of MMI’s Data & Annotation Workflow MMI adopted Encord as a core part of their computer vision and data operations, using it across annotation, review, data curation, and automation. With Encord, the team gained access to: An external annotation workforce, allowing engineers to prioritise model innovation over manual labeling Workflow automation and model-assisted labeling, accelerating annotation and review cycles Systematic review policies and targeted relabeling, improving dataset consistency and edge-case handling Encord Index, enabling efficient browsing, categorisation, and reuse of large-scale surgical video data SDK integrations, allowing seamless export into MMI’s internal training pipelines Engineers across roles, from senior CV engineers to PhD researchers and interns, use Encord daily to review annotations, visualise edge cases, automate parts of the workflow, and move faster from experimentation to validation. {{testimonial_mondrian}} The Results: Faster Iteration, Better Data, More Focus on Innovation By integrating Encord into their AI-driven workflows, MMI significantly improved both operational efficiency and model quality. Annotation and review cycles became faster and more consistent, allowing the team to iterate on multiple model versions more quickly. Improved dataset quality translated directly into more stable model performance across different test environments. Most importantly, Encord freed up valuable engineering time. Instead of manually handling data, MMI’s team can focus on what matters most: advancing the state of the art in digital surgery, developing assistive and autonomous capabilities, and delivering better outcomes for surgeons and patients. As MMI continues to expand its digital surgery platforms, including first-in-human deployments, Encord remains a critical partner in enabling scalable, reliable, and high-quality computer vision development."},"body":[{}],"image":{"alt":"MMI symani surgery system being teleoperated","url":"https://images.prismic.io/encord/NLZC0DBoOhlIhUwC_updated-cover_tractable_1-1.avif?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/NLZC0DBoOhlIhUwC_updated-cover_tractable_1-1.avif?auto=format%2Ccompress&fit=max&w=146&h=81&fm=webp 146w,\nhttps://images.prismic.io/encord/NLZC0DBoOhlIhUwC_updated-cover_tractable_1-1.avif?auto=format%2Ccompress&fit=max&w=292&h=162&fm=webp 292w,\nhttps://images.prismic.io/encord/NLZC0DBoOhlIhUwC_updated-cover_tractable_1-1.avif?auto=format%2Ccompress&fit=max&w=583&h=324&fm=webp 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MMI Accelerates Digital Surgery Innovation with Encord"},"read_time":null,"sub_image":{"alt":"MMI Logo","url":"https://images.prismic.io/encord/afHlpMBOoF08xaf9_Logo_MMI_white_1.avif?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/afHlpMBOoF08xaf9_Logo_MMI_white_1.avif?auto=format%2Ccompress&fit=max&w=2&h=10&fm=webp 2w,\nhttps://images.prismic.io/encord/afHlpMBOoF08xaf9_Logo_MMI_white_1.avif?auto=format%2Ccompress&fit=max&w=4&h=20&fm=webp 4w,\nhttps://images.prismic.io/encord/afHlpMBOoF08xaf9_Logo_MMI_white_1.avif?auto=format%2Ccompress&fit=max&w=8&h=40&fm=webp 8w,\nhttps://images.prismic.io/encord/afHlpMBOoF08xaf9_Logo_MMI_white_1.avif?auto=format%2Ccompress&fit=max&w=16&h=80&fm=webp 16w","sizes":"(min-width: 8px) 8px, 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Agents "]}},{"node":{"uid":"thoro-ai","data":{"author":{"document":{"id":"7eacb635-8381-5dfe-bd83-7f2340c17caf","uid":"ulrik-stig-hansen","data":{"full_name":{"text":"Ulrik Stig Hansen"}}}},"content":{"html":"Thoro is building an indoor autonomy stack for a broad range of mobile robots, with a primary focus on material handling in logistics and manufacturing environments, as well as industrial floor cleaning. For material handling, the company’s core vision is simple but ambitious: enable robots to move any pallet, anywhere.
Safety sits at the heart of everything Thoro builds, with the ongoing goal of achieving independent safety certification for all its robots, allowing them to operate reliably around both trained and untrained people.
![](\"https://images.prismic.io/encord/afxebcBOoF08xtjq_thororobot.avif?auto=format,compress\")
Thoro’s most challenging deployment environments are large U.S. warehouses, up to 800,000 square feet, with no permanent racking. The only fixed landmarks are I-beams spaced roughly 30 feet apart, while the internal layout of pallets changes frequently.
This makes reliable navigation exceptionally difficult: robots must distinguish real aisles from temporary pallet storage areas with virtually no persistent spatial reference points.
Compounding this challenge was the team’s data and annotation infrastructure. Before Encord, Thoro managed all annotations manually using an open-source tool hosted on a local server. This setup created three major friction points:
First, because it was locally hosted, remote team members could not label data outside the office.
Second, dataset visibility was limited—the team could only see whether data was labeled, with no way to track progress or inspect pipeline flow.
Third, there was no automation: no auto-labeling, no automated data ingestion, and no mature pipeline to move data from robot to model. Everything had to be manually uploaded and downloaded.
As Chris Dunkers from Thoro put it:
{{light_callout_start}}
"We could only see if data was labeled or not labeled. Tracking its progress through a project wasn’t really easy. And labeling from anywhere… that was one of the big things that drove us to look elsewhere."
{{light_callout_end}}
Thoro partnered with Encord to replace its fragmented, locally hosted setup with a fully cloud-based, automated data pipeline.
Data now flows from the robot into AWS, into Encord for labeling and quality review, and back out through Python training scripts before the updated model is deployed onto the robot.
The team primarily works with stereo image data (color images paired with aligned depth images), focusing on keypoint labeling and tracking for pallets across image sets.
Encord’s project and ontology tools provide a live view of data flow at every stage. One particularly valuable capability is the ability to add new ontology items and immediately reopen existing datasets to label only the new element, with full visibility into relabeling progress.
For model evaluation and experiment tracking, the team uses Weights & Biases alongside their Encord workflow.
![\"Thoro](\"https://images.prismic.io/encord/afxiaMBOoF08xtl8_thoro1.gif?auto=format,compress\")
Thoro’s onboarding experience was smooth from the start. The team ran a one-week trial and was fully operational by the end.
Connecting to their existing AWS infrastructure worked on the first attempt, and Encord’s support team provided fast, high-quality responses throughout.
{{light_callout_start}}
"The support has been phenomenal. Questions answered in about 20 minutes. And integrating with AWS just worked the first time I tried it."
{{light_callout_end}}
![\"thoro](\"https://images.prismic.io/encord/afxiacBOoF08xtl9_thoro2.gif?auto=format,compress\")
Since adopting Encord, Thoro has significantly increased the speed at which it can respond to real-world model failures, a critical capability in live warehouse environments.
The team estimates they are at least 50% faster compared to earlier stages of the project, with each team member labeling or quality-controlling roughly twice as many images as before. The primary driver of this improvement has been the integrated auto-labeler, which performs a highly effective first pass on incoming data.
Model deployment timelines have shrunk to approximately one week: collect data on Monday, deploy an updated model by Friday. The team targets around 3,000 new images added within that window, a pace they are already achieving.
Beyond throughput, Encord’s pipeline visibility gave Thoro the operational insight needed to identify bottlenecks and optimize workflows. When one team member was labeling significantly faster than others, project insights helped surface and scale that workflow across the team.
The speed advantage has also unlocked new commercial opportunities. When Thoro deployed robots at a new customer site and encountered plastic-wrapped pallets, an edge case the existing model couldn’t handle, the team collected data, processed it through Encord, and deployed an updated model within three weeks.
","text":"Introducing Customer: Thoro Thoro is building an indoor autonomy stack for a broad range of mobile robots, with a primary focus on material handling in logistics and manufacturing environments, as well as industrial floor cleaning. For material handling, the company’s core vision is simple but ambitious: enable robots to move any pallet, anywhere. Safety sits at the heart of everything Thoro builds, with the ongoing goal of achieving independent safety certification for all its robots, allowing them to operate reliably around both trained and untrained people. Problem: A Demanding Deployment Environment and a Fragile Data Pipeline Thoro’s most challenging deployment environments are large U.S. warehouses, up to 800,000 square feet, with no permanent racking. The only fixed landmarks are I-beams spaced roughly 30 feet apart, while the internal layout of pallets changes frequently. This makes reliable navigation exceptionally difficult: robots must distinguish real aisles from temporary pallet storage areas with virtually no persistent spatial reference points. Compounding this challenge was the team’s data and annotation infrastructure. Before Encord, Thoro managed all annotations manually using an open-source tool hosted on a local server. This setup created three major friction points: First, because it was locally hosted, remote team members could not label data outside the office.\nSecond, dataset visibility was limited—the team could only see whether data was labeled, with no way to track progress or inspect pipeline flow.\nThird, there was no automation: no auto-labeling, no automated data ingestion, and no mature pipeline to move data from robot to model. Everything had to be manually uploaded and downloaded. As Chris Dunkers from Thoro put it: {{light_callout_start}}\n\"We could only see if data was labeled or not labeled. Tracking its progress through a project wasn’t really easy. And labeling from anywhere… that was one of the big things that drove us to look elsewhere.\"\n{{light_callout_end}} Solution: A Cloud-Native Pipeline from Robot to Model Thoro partnered with Encord to replace its fragmented, locally hosted setup with a fully cloud-based, automated data pipeline. Data now flows from the robot into AWS, into Encord for labeling and quality review, and back out through Python training scripts before the updated model is deployed onto the robot. The team primarily works with stereo image data (color images paired with aligned depth images), focusing on keypoint labeling and tracking for pallets across image sets. Encord’s project and ontology tools provide a live view of data flow at every stage. One particularly valuable capability is the ability to add new ontology items and immediately reopen existing datasets to label only the new element, with full visibility into relabeling progress. For model evaluation and experiment tracking, the team uses Weights & Biases alongside their Encord workflow. Implementation: Operational Within a Week Thoro’s onboarding experience was smooth from the start. The team ran a one-week trial and was fully operational by the end. Connecting to their existing AWS infrastructure worked on the first attempt, and Encord’s support team provided fast, high-quality responses throughout. {{light_callout_start}}\n\"The support has been phenomenal. Questions answered in about 20 minutes. And integrating with AWS just worked the first time I tried it.\"\n{{light_callout_end}} Results: 50% Faster Labeling, One-Week Deployment Cycles, and New Customers Unlocked Since adopting Encord, Thoro has significantly increased the speed at which it can respond to real-world model failures, a critical capability in live warehouse environments. The team estimates they are at least 50% faster compared to earlier stages of the project, with each team member labeling or quality-controlling roughly twice as many images as before. The primary driver of this improvement has been the integrated auto-labeler, which performs a highly effective first pass on incoming data. Model deployment timelines have shrunk to approximately one week: collect data on Monday, deploy an updated model by Friday. The team targets around 3,000 new images added within that window, a pace they are already achieving. Beyond throughput, Encord’s pipeline visibility gave Thoro the operational insight needed to identify bottlenecks and optimize workflows. When one team member was labeling significantly faster than others, project insights helped surface and scale that workflow across the team. The speed advantage has also unlocked new commercial opportunities. When Thoro deployed robots at a new customer site and encountered plastic-wrapped pallets, an edge case the existing model couldn’t handle, the team collected data, processed it through Encord, and deployed an updated model within three weeks."},"body":[{},{}],"image":{"alt":"Thoro robot navigating warehouse autonomously","url":"https://images.prismic.io/encord/t_xRkjfAZF1sVdbR_updated-cover_thoro-ai_1.avif?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/t_xRkjfAZF1sVdbR_updated-cover_thoro-ai_1.avif?auto=format%2Ccompress&fit=max&w=146&h=81&fm=webp 146w,\nhttps://images.prismic.io/encord/t_xRkjfAZF1sVdbR_updated-cover_thoro-ai_1.avif?auto=format%2Ccompress&fit=max&w=292&h=162&fm=webp 292w,\nhttps://images.prismic.io/encord/t_xRkjfAZF1sVdbR_updated-cover_thoro-ai_1.avif?auto=format%2Ccompress&fit=max&w=583&h=324&fm=webp 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Thoro Cut Model Deployment to One Week and Increased Labeling Speed by 50% with Encord "},"read_time":null,"sub_image":{"alt":"Thoro ai logo","url":"https://images.prismic.io/encord/afIYHcBOoF08xbBI_logo_thoro_2.avif?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/afIYHcBOoF08xbBI_logo_thoro_2.avif?auto=format%2Ccompress&fit=max&w=2&h=10&fm=webp 2w,\nhttps://images.prismic.io/encord/afIYHcBOoF08xbBI_logo_thoro_2.avif?auto=format%2Ccompress&fit=max&w=4&h=20&fm=webp 4w,\nhttps://images.prismic.io/encord/afIYHcBOoF08xbBI_logo_thoro_2.avif?auto=format%2Ccompress&fit=max&w=8&h=40&fm=webp 8w,\nhttps://images.prismic.io/encord/afIYHcBOoF08xbBI_logo_thoro_2.avif?auto=format%2Ccompress&fit=max&w=16&h=80&fm=webp 16w","sizes":"(min-width: 8px) 8px, 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100vw"}},"layout":"constrained","placeholder":{"fallback":"data:image/jpeg;base64,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"},"width":8,"height":40}}},"first_publication_date":"2026-04-28T08:46:37+0000","tags":["Customers","Computer Vision","Physical AI"]}},{"node":{"uid":"uipath","data":{"author":{"document":{"id":"7eacb635-8381-5dfe-bd83-7f2340c17caf","uid":"ulrik-stig-hansen","data":{"full_name":{"text":"Ulrik Stig Hansen"}}}},"content":{"html":"UiPath started as an RPA (Robotic Process Automation) company, enabling people to build UI-based automations using a low-code environment. As the company has expanded, so too have the modalities it works with, moving well beyond images of UI screens or scanned documents to encompass videos of automations, text models for tool-use and customer support agents, and voice models for real-time agents.
With that breadth comes a significant annotation and data management challenge: keeping training data high quality, well-tracked, and scalable across a diverse and growing set of model types.
Annotating data sounds straightforward until you're doing it with multiple people working on the same dataset simultaneously. That is when coordination overhead, inconsistent workflows, and limited visibility into pipeline progress can quickly become a bottleneck.
Before Encord, UiPath had tried annotating with a different vendor, but found it painful, particularly when dealing with edge cases, where turnaround times were frustratingly slow. The team needed a platform that could match their pace of iteration, provide genuine pipeline transparency, and handle the complexity of multi-reviewer workflows across diverse data types.
For UiPath's table extraction model, a submodel of their broader UI understanding system, data quality was especially critical. The model structure is complex, requiring both images and labels to be precisely correct. Any annotation platform would need to support in-place editing and multiple review flows to keep their internal quality bar high.
![\"uipath](\"https://images.prismic.io/encord/ahVZDrK9tuLqEIyf_Screenshot2026-05-26at09.25.29.png?auto=format,compress\")
UiPath migrated to Encord after their Director, Applied Science, picked up the SDK, experimented with it, and found it to be both powerful and easy to move in and out of as needed.
According to their Senior Manager, Enterprise BI Analytics, a key factor in the decision was flexibility and infrastructure. The team needed a data partner that genuinely understood their use case. One that could move quickly without sending them in the wrong direction. When Encord proactively surfaced inefficiencies in the annotation process and helped address them, it confirmed they had chosen the right partner.
Encord’s native support for multiple simultaneous annotators, with fast sync between them, proved to be a significant operational upgrade. The team was able to use Encord to improve data they already had, and to iterate on their labeling process to find the right balance between speed and quality. Pipeline visibility made bottlenecks transparent and addressable in real time.
For complex annotation tasks like the table extraction model where both the image and its labels need to be exactly right, Encord's review flow gave UiPath the structured internal review process they needed.
![\"uipath](\"https://images.prismic.io/encord/ahVZDbK9tuLqEIye_Screenshot2026-05-26at09.24.09.png?auto=format,compress\")
The impact of moving to Encord was most visible on UiPath's table extraction model. Before, the model was performing at around 96–97% mAP on their internal dataset, which is already strong. After using Encord to more than 10x the size of the training dataset, the team achieved more than a 4x reduction in error rate, pushing accuracy above 98% and close to 99%.
Critically, the improvement wasn't just in raw numbers: the expanded dataset also brought the model's distribution much closer to real production data, making the gains durable rather than artefacts of benchmark overfitting. Customers noticed the difference.
Beyond the table extraction model, Encord enabled UiPath to scale a handful of models across their growing portfolio, freeing the team from the overhead of starting each new project from scratch, and giving them the headspace to focus on model development rather than data infrastructure.
{{testimonial_mondrian}}
","text":"Introducing Customer: UiPath UiPath started as an RPA (Robotic Process Automation) company, enabling people to build UI-based automations using a low-code environment. As the company has expanded, so too have the modalities it works with, moving well beyond images of UI screens or scanned documents to encompass videos of automations, text models for tool-use and customer support agents, and voice models for real-time agents. With that breadth comes a significant annotation and data management challenge: keeping training data high quality, well-tracked, and scalable across a diverse and growing set of model types. Problem: Annotation at Scale Across Multiple Modalities Annotating data sounds straightforward until you're doing it with multiple people working on the same dataset simultaneously. That is when coordination overhead, inconsistent workflows, and limited visibility into pipeline progress can quickly become a bottleneck. Before Encord, UiPath had tried annotating with a different vendor, but found it painful, particularly when dealing with edge cases, where turnaround times were frustratingly slow. The team needed a platform that could match their pace of iteration, provide genuine pipeline transparency, and handle the complexity of multi-reviewer workflows across diverse data types. For UiPath's table extraction model, a submodel of their broader UI understanding system, data quality was especially critical. The model structure is complex, requiring both images and labels to be precisely correct. Any annotation platform would need to support in-place editing and multiple review flows to keep their internal quality bar high. Solution: A Flexible, Transparent Platform Built for Iteration UiPath migrated to Encord after their Director, Applied Science, picked up the SDK, experimented with it, and found it to be both powerful and easy to move in and out of as needed. According to their Senior Manager, Enterprise BI Analytics, a key factor in the decision was flexibility and infrastructure. The team needed a data partner that genuinely understood their use case. One that could move quickly without sending them in the wrong direction. When Encord proactively surfaced inefficiencies in the annotation process and helped address them, it confirmed they had chosen the right partner. Encord’s native support for multiple simultaneous annotators, with fast sync between them, proved to be a significant operational upgrade. The team was able to use Encord to improve data they already had, and to iterate on their labeling process to find the right balance between speed and quality. Pipeline visibility made bottlenecks transparent and addressable in real time. For complex annotation tasks like the table extraction model where both the image and its labels need to be exactly right, Encord's review flow gave UiPath the structured internal review process they needed. Results: Near-99% Accuracy and a 10x Dataset Expansion The impact of moving to Encord was most visible on UiPath's table extraction model. Before, the model was performing at around 96–97% mAP on their internal dataset, which is already strong. After using Encord to more than 10x the size of the training dataset, the team achieved more than a 4x reduction in error rate, pushing accuracy above 98% and close to 99%. Critically, the improvement wasn't just in raw numbers: the expanded dataset also brought the model's distribution much closer to real production data, making the gains durable rather than artefacts of benchmark overfitting. Customers noticed the difference. Beyond the table extraction model, Encord enabled UiPath to scale a handful of models across their growing portfolio, freeing the team from the overhead of starting each new project from scratch, and giving them the headspace to focus on model development rather than data infrastructure. {{testimonial_mondrian}}"},"body":[{},{}],"image":{"alt":"office blocks","url":"https://images.prismic.io/encord/iHFxiWUQKdqYygxp_updated-cover_ui-path_1.avif?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/iHFxiWUQKdqYygxp_updated-cover_ui-path_1.avif?auto=format%2Ccompress&fit=max&w=146&h=81&fm=webp 146w,\nhttps://images.prismic.io/encord/iHFxiWUQKdqYygxp_updated-cover_ui-path_1.avif?auto=format%2Ccompress&fit=max&w=292&h=162&fm=webp 292w,\nhttps://images.prismic.io/encord/iHFxiWUQKdqYygxp_updated-cover_ui-path_1.avif?auto=format%2Ccompress&fit=max&w=583&h=324&fm=webp 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UiPath 10x'd Their Dataset and Improved Model Accuracy to Near 99% with Encord"},"read_time":null,"sub_image":{"alt":"UiPath logo","url":"https://images.prismic.io/encord/aiACugeQX7-eWpqh_Logo_UIPath_white-2-1.avif?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/aiACugeQX7-eWpqh_Logo_UIPath_white-2-1.avif?auto=format%2Ccompress&fit=max&w=2&h=10&fm=webp 2w,\nhttps://images.prismic.io/encord/aiACugeQX7-eWpqh_Logo_UIPath_white-2-1.avif?auto=format%2Ccompress&fit=max&w=4&h=20&fm=webp 4w,\nhttps://images.prismic.io/encord/aiACugeQX7-eWpqh_Logo_UIPath_white-2-1.avif?auto=format%2Ccompress&fit=max&w=8&h=40&fm=webp 8w,\nhttps://images.prismic.io/encord/aiACugeQX7-eWpqh_Logo_UIPath_white-2-1.avif?auto=format%2Ccompress&fit=max&w=16&h=80&fm=webp 16w","sizes":"(min-width: 8px) 8px, 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Vision"]}},{"node":{"uid":"hudl","data":{"author":{"document":{"id":"7eacb635-8381-5dfe-bd83-7f2340c17caf","uid":"ulrik-stig-hansen","data":{"full_name":{"text":"Ulrik Stig Hansen"}}}},"content":{"html":"Hudl is the complete sports analytics ecosystem trusted by teams, coaches, and athletes across professional and amateur sports. From recruitment and performance analysis to high-fidelity video tracking, Hudl's Applied Machine Learning (AML) team powers the data intelligence behind some of the world's most demanding sports analytics workflows, spanning soccer, American football, ice hockey, basketball, and beyond.
At the heart of Hudl's product is video and wearables: sensor feeds, live match footage, and uploaded game film that must be annotated with dense, precise tracking data to train and improve the models.
Hudl's AML team had built its annotation workflow using third-party platform that served them well for a time. But as the team's ambitions grew, the fit became less comfortable. The vendor was increasingly focused on other product areas, leaving video annotation lower on its roadmap than Hudl needed it to be.
The platform's manual, frame-by-frame workflow also became a limiting factor as volumes scaled. Integration into Hudl’s prelabelling workflows was also key as requirements scaled.
At the time, annotation and data science operated as sequential rather than collaborative workstreams. Outputs were passed between teams without shared visibility, aligned ontologies, or joint oversight, a model that limited the feedback loops needed to improve data quality iteratively. It was an arrangement that made sense early on but that the team had begun to outgrow as the two disciplines became more intertwined.
Finally, the platform lacked instant annotation analytics — capabilities the team didn't need at first, but which would prove essential to managing an annotation operation of the size they were growing into.
Hudl selected Encord as its annotation and data management platform for all AML workloads. The decision was driven by Encord's video-native tooling, the ability to integrate Hudl's own ML models as pre-labelling agents, and the platform's flexibility to grow with the team.
Encord now sits as the central annotation and data management layer in Hudl's MLOps pipeline. Raw camera video, live match sources, and other sources land in S3, is ingested into Encord via SDK-based pipelines, and flows through a multi-stage workflow: pre-labelling via Hudl's own models, human correction and completion by the annotation team, automated validation by a Task Agent, and final review before export to model training pipelines.
The platform is used across a wide range of active projects, including soccer and football object tracking from Hudl's current and prototype cameras, broadcast ice hockey and football tracking, American football end zone cameras, and basketball player and ball detection.
Data scientists are now directly involved in the annotation process, such as co-creating ontologies, reviewing outputs, and assessing metrics, rather than waiting for results to be handed over.
{{testimonial_mondrian}}
The ability to connect Hudl's own ML inference pipeline directly into Encord's annotation workflow sits at the centre of the team's strategy. A pre-labelling agent feeds model predictions directly into the annotation queue, drastically reducing the work required from human annotators. A Validation Task Agent automatically flags annotation errors before tasks reach the review stage, ensuring analysts focus on quality rather than error-hunting.
Within the annotation team, SAM v2's automated tracking, including backwards tracking, and SAM v3 for detection have been actively used alongside Hudl's custom pipelines, providing additional tooling flexibility.
Track split/merge and interpolation are also essential for correcting identity switches in multi-object tracking, a critical requirement given the density of annotation across player, ball, and referee tracking in live sports footage.
Encord's analytics features have transformed how the AML team manages annotators and tracks project progress. Managers can view team-level throughput and individual performance, enabling coaching and workload optimisation in real time.
Custom metadata schemas are also used to attach compliance metadata to ingested assets, supporting Hudl's internal data governance requirements.
![](\"https://images.prismic.io/encord/p1gmDfcOs2DGVS-0_sportsai3.jpeg?auto=format,compress\")
One of the most significant developments in Hudl's use of Encord has been the co-development of a custom homography editor. This is a pitch overlay tool that maps annotation points in video space to their corresponding locations on the pitch.
Annotating the homography (the projection between video and pitch space) had been a challenge for Hudl, previously requiring bespoke internal tooling. Working closely with Encord, a dual-view editor was developed, enabling annotators to click both on the pitch diagram and on the video frame simultaneously.
This capability unlocks a whole class of spatial annotation that was simply not possible through any external product and demonstrates the depth of customisation available within the Encord platform.
10x annotation speed improvement on tracking data: By integrating Hudl's own pre-labelling pipeline into Encord, use cases that previously required approximately 10 hours of manual annotation now take around 1 hour. This step-change in throughput is enabling significantly more training data to be produced, accelerating model development cycles across the AML team.
40% reduction in review time for Ice Hockey tracking: Prior to Encord's Validation Task Agent, review analysts spent considerable time reviewing the jobs manually searching for errors, rejecting tasks, and returning them to annotators. With the validation agent in place, the same process is now 40% faster. Errors are flagged automatically before reaching review, so analysts can focus on overall quality rather than error detection.
Significant speed uplift from interactive tracking tools: Even without pre-labelling, annotators report that Encord's built-in tracking tools provide a substantial speed improvement compared to previous solutions, a testament to the quality of the core annotation experience.
Better alignment between annotation and data science: Encord has brought the annotation and data science teams into a shared workflow. With jointly created ontologies, collaborative review, and real-time analytics, the two teams now work from the same picture — producing clearer requests, smoother delivery, and faster iteration on models.
![](\"https://images.prismic.io/encord/vfjbA_MkI6euUblw_sportsai2.jpeg?auto=format,compress\")
UVeye builds automated, AI-powered vehicle inspection systems that scan a vehicle's undercarriage, tires, exterior, and interior in seconds - detecting damage, defects, and maintenance needs across the entire automotive lifecycle. With deployments at hundreds of locations serving GM, Volvo, Toyota, and Amazon, UVeye's computer vision systems operate at production scale. Their deep learning models, trained on millions of vehicle scans, require precise annotation of complex visual data including video sequences, multi-frame image groups, and detailed object tracking to maintain the accuracy that safety-critical automotive applications demand.
As UVeye's global footprint expanded, scaling computer vision operations required a more integrated data platform. While their previous platform offered basic drawing tools, it fell short in the areas that matter most for production AI teams:
Analytics and visibility gaps: Legacy analytics lacked the granular attribute tracking needed for multi-frame vehicle inspection datasets. The team needed more visibility into individual annotator productivity and a more structured way to monitor team performance.
Workflow limitations: They couldn't effectively manage sampling strategies, assign collaborators flexibly, or adjust workflows to fit their evolving needs. The absence of feedback tools built into the workflow made it difficult to provide timely guidance to annotators and reviewers, impacting both quality control and onboarding efficiency.
Platform limitations: The existing annotation platform offered limited API integration and extensibility, making it difficult to tailor workflows to the project's needs. Capabilities such as grouping related frames and maintaining object associations across video sequences were not natively supported.
Manual coordination overhead: Manual coordination across large teams created unnecessary administrative overhead that distracted from core AI development. Cross-team workflows needed streamlined, real-time feedback loops to support rapid annotator onboarding and smooth collaboration.
These limitations led UVeye to seek a solution with three clear goals: increase annotation speed and validation throughput, maintain low error rates, and gain clearer insights into team performance.
{{light_callout_start}} "What stood out most about Encord compared to other options was its flexibility, customization potential, and focus on workflow efficiency. The active SDK and accessible API gave us the ability to tailor the platform to our specific needs, automate processes, and integrate more deeply with our existing systems." - Inbal Friedlander, Data Operations Team Lead at UVeye {{light_callout_end}}
Since implementing Encord, UVeye has seen around a 20-30% improvement in their data annotation rate, allowing them to deliver more training data within the same number of working hours while completing projects in shorter cycles.
Real-time project intelligence: Encord's real-time project tracking and productivity insights eliminated spreadsheet-based coordination. Task ownership, progress status, and team performance are now visible at a glance through built-in analytics.
Powerful workflow automation: Flexible workflow configuration, sampling strategies for targeted QA, and features like object tracking, image grouping, and improved polygon tools have made both labeling and validation noticeably faster, particularly valuable for annotating vehicle components across video sequences.
Built-in quality control: The review functionality enables efficient validation workflows with structured feedback loops integrated directly into the platform. The team now identifies labeling issues much earlier in the project lifecycle, helping fine-tune error rates and reduce mistakes.
{{light_callout_start}} "Encord has eliminated the need for manual tracking tables. In our previous platform, we had to rely on spreadsheets to ensure that annotators and reviewers weren't overlapping. With Encord the process is built into the workflow, allowing the team to focus entirely on the task without worrying about coordination overhead." - Inbal Friedlander, Data Operations Team Lead at UVeye {{light_callout_end}}
Comprehensive analytics: Class-based, user-based, and issue-based analytics give UVEye clear visibility into team performance and labeling quality, helping them quickly identify areas for improvement and maintain high standards across projects.
Enterprise customization: The active SDK and accessible API enable deep integration with UVeye's existing systems, with per-user configuration making the platform feel adaptable and production-ready.
A breakthrough use case has been the ability to group sequences of images as individual tasks. This has significantly sped up both annotation and validation, provided better context for annotators working with vehicle inspection imagery, and improved workflow management.
"One new use case that has proven to be very beneficial has been the ability to group sequences of images as individual tasks. This has significantly sped up the annotation and validation process, provided better context for annotation and improved workflow management significantly." - Inbal Friedlander, Data Operations Team Lead at UVeye
{{testimonial_mondrian}}
UVeye's migration to Encord has transformed their annotation operations from a fragmented, manually-intensive process to a streamlined, high-performance workflow, delivering higher quality annotated data in less time while eliminating manual coordination overhead.
With Encord's analytics, workflow automation, and customization capabilities, UVeye has established a foundation for continuous improvement in their computer vision data operations, enabling them to move faster while maintaining the quality standards their safety-critical automotive inspection systems demand.
","text":"Introducing Customer: UVeye UVeye builds automated, AI-powered vehicle inspection systems that scan a vehicle's undercarriage, tires, exterior, and interior in seconds - detecting damage, defects, and maintenance needs across the entire automotive lifecycle. With deployments at hundreds of locations serving GM, Volvo, Toyota, and Amazon, UVeye's computer vision systems operate at production scale. Their deep learning models, trained on millions of vehicle scans, require precise annotation of complex visual data including video sequences, multi-frame image groups, and detailed object tracking to maintain the accuracy that safety-critical automotive applications demand.\n Before Encord As UVeye's global footprint expanded, scaling computer vision operations required a more integrated data platform. While their previous platform offered basic drawing tools, it fell short in the areas that matter most for production AI teams: Analytics and visibility gaps: Legacy analytics lacked the granular attribute tracking needed for multi-frame vehicle inspection datasets. The team needed more visibility into individual annotator productivity and a more structured way to monitor team performance. Workflow limitations: They couldn't effectively manage sampling strategies, assign collaborators flexibly, or adjust workflows to fit their evolving needs. The absence of feedback tools built into the workflow made it difficult to provide timely guidance to annotators and reviewers, impacting both quality control and onboarding efficiency. Platform limitations: The existing annotation platform offered limited API integration and extensibility, making it difficult to tailor workflows to the project's needs. Capabilities such as grouping related frames and maintaining object associations across video sequences were not natively supported. \n Manual coordination overhead: Manual coordination across large teams created unnecessary administrative overhead that distracted from core AI development. Cross-team workflows needed streamlined, real-time feedback loops to support rapid annotator onboarding and smooth collaboration. These limitations led UVeye to seek a solution with three clear goals: increase annotation speed and validation throughput, maintain low error rates, and gain clearer insights into team performance.\n The Encord Solution 20-30% improvement in annotation rate Eliminated manual tracking overhead Earlier detection of labeling issues\n {{light_callout_start}} \"What stood out most about Encord compared to other options was its flexibility, customization potential, and focus on workflow efficiency. The active SDK and accessible API gave us the ability to tailor the platform to our specific needs, automate processes, and integrate more deeply with our existing systems.\" - Inbal Friedlander, Data Operations Team Lead at UVeye {{light_callout_end}}\n Since implementing Encord, UVeye has seen around a 20-30% improvement in their data annotation rate, allowing them to deliver more training data within the same number of working hours while completing projects in shorter cycles. \n What Made the Difference Real-time project intelligence: Encord's real-time project tracking and productivity insights eliminated spreadsheet-based coordination. Task ownership, progress status, and team performance are now visible at a glance through built-in analytics.\n Powerful workflow automation: Flexible workflow configuration, sampling strategies for targeted QA, and features like object tracking, image grouping, and improved polygon tools have made both labeling and validation noticeably faster, particularly valuable for annotating vehicle components across video sequences.\n Built-in quality control: The review functionality enables efficient validation workflows with structured feedback loops integrated directly into the platform. The team now identifies labeling issues much earlier in the project lifecycle, helping fine-tune error rates and reduce mistakes.\n {{light_callout_start}} \"Encord has eliminated the need for manual tracking tables. In our previous platform, we had to rely on spreadsheets to ensure that annotators and reviewers weren't overlapping. With Encord the process is built into the workflow, allowing the team to focus entirely on the task without worrying about coordination overhead.\" - Inbal Friedlander, Data Operations Team Lead at UVeye {{light_callout_end}}\n Comprehensive analytics: Class-based, user-based, and issue-based analytics give UVEye clear visibility into team performance and labeling quality, helping them quickly identify areas for improvement and maintain high standards across projects.\n Enterprise customization: The active SDK and accessible API enable deep integration with UVeye's existing systems, with per-user configuration making the platform feel adaptable and production-ready.\n Unlocking New Capabilities A breakthrough use case has been the ability to group sequences of images as individual tasks. This has significantly sped up both annotation and validation, provided better context for annotators working with vehicle inspection imagery, and improved workflow management.\n \"One new use case that has proven to be very beneficial has been the ability to group sequences of images as individual tasks. This has significantly sped up the annotation and validation process, provided better context for annotation and improved workflow management significantly.\" - Inbal Friedlander, Data Operations Team Lead at UVeye\n Partnership and Support {{testimonial_mondrian}}\n Summary UVeye's migration to Encord has transformed their annotation operations from a fragmented, manually-intensive process to a streamlined, high-performance workflow, delivering higher quality annotated data in less time while eliminating manual coordination overhead. With Encord's analytics, workflow automation, and customization capabilities, UVeye has established a foundation for continuous improvement in their computer vision data operations, enabling them to move faster while maintaining the quality standards their safety-critical automotive inspection systems demand."},"body":[{},{}],"image":{"alt":"uv eye background car ","url":"https://images.prismic.io/encord/LUkGZuOwawoYbcTl_uveyebannerbackground.png?auto=format%2Ccompress&fit=max","gatsbyImageData":{"images":{"sources":[{"srcSet":"https://images.prismic.io/encord/LUkGZuOwawoYbcTl_uveyebannerbackground.png?auto=format%2Ccompress&fit=max&w=146&h=81&fm=webp 146w,\nhttps://images.prismic.io/encord/LUkGZuOwawoYbcTl_uveyebannerbackground.png?auto=format%2Ccompress&fit=max&w=292&h=162&fm=webp 292w,\nhttps://images.prismic.io/encord/LUkGZuOwawoYbcTl_uveyebannerbackground.png?auto=format%2Ccompress&fit=max&w=583&h=324&fm=webp 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