How Harvard Medical School and MGH Cut Down Annotation Time and Model Errors with Encord

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 significant 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 (Innovation project 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, the R&D manager of the AI team, 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. "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   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.

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-sourced tools like CVAT 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. “…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 CVAT 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   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. "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   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.

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. “The labeling speed of the annotation team improved to almost 60% compared to when using their in-house tool.” - Markus Kittel  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.