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In a recent conversations with leaders in Ag-Tech and robotics, we have dug into a common but often under-discussed challenge in applied AI: How do you build computer vision models that can actually perform in the real world? From sun glare and dusty conditions to shaky camera mounts and wildly varying plant types, the AgTech and robotics fields present some of the harshest environments for deploying AI. But these challenges are faced across industries in which conditions can vary in both training and deploying models.  In this article, we are going to explore how you can curate edge-case data to build models that perform in the real-world.   The Challenge With Variable Environments Let’s start with the baseline problem: while  CV models can be trained and evaluated in ideal conditions with clean datasets, balanced lighting, and minimal noise, many are not being trained on the right data. In turn, as soon as those models leave the lab, things fall apart fast. To take the AgTech example, AI systems are forced to deal with: Inconsistent lighting: clouds rolling over, shadows from crop canopies, backlight during golden hour Dust, water, and vibration: from machines plowing soil or navigating uneven terrain Sensor instability: shaky footage, motion blur, camera obstructions Massive biological variation: different plant species, growth stages, weed types, soil textures, and even pest interference That’s not just a harder dataset, but rather a completely different operating context. A model that performs with 92% accuracy in synthetic tests may suddenly hit 60% when exposed to edge cases, like backlit weeds partially covered by dust, in motion, and with similar coloring to the surrounding soil. This is why robustness matters more than theoretical accuracy. In the wild, your model needs to handle variability gracefully, not just perform well in ideal conditions. The True Bottleneck: Curating and Labeling the Right Data If there’s one consistent theme across all the teams we’ve worked with in AgTech and field robotics, it’s this: while having an AI data labeling pipeline is crucial to model success, labeling more data isn’t always the answer. Labeling the right data is key to ensure that variability in the real-world is accounted for while maintaining maximum efficiency across the AI data pipeline.  Annotation Fatigue Is Real Labeling thousands of field images, with weeds, crops, shadows, and motion blur, is time-consuming and expensive. For most teams, annotation quickly becomes the bottleneck in model iteration. Even more frustrating: you may end up labeling hours of video or thousands of images that add little to no model improvement. So how do the best teams tackle this? Curate for Edge Cases, Not Volume Top-performing computer vision pipelines focus on edge-case sampling, such as: Occluded or partially visible objects (e.g., items behind obstacles, people partially out of frame) Low-light, high-glare, or overexposed conditions (e.g., poorly lit warehouses, shiny surfaces, backlit scenes) Uncommon object variations or rare classes (e.g., damaged products, rare defects, unusual medical cases) Motion blur or shaky footage (e.g., handheld cameras, moving platforms, vibration-prone environments) These are the moments that hinder model performance in the real world and improving on these has an outsized impact on real-world performance. How Encord Helps Teams Go Faster This is exactly where Encord fits in,  as the data engine for teams building robust computer vision systems across industries like AgTech, robotics, healthcare, logistics, manufacturing, and more. Encord gives you the tools to focus your effort on the data that actually improves model performance. Here’s how: Curate Smarter with Visual Search & Metadata Filters Not all data is equally valuable, especially when you're looking for edge cases. Encord lets you: Search across metadata such as lighting conditions, blur, object class, and camera source Tag and retrieve examples with visual search, identifying hard-to-label or rare cases with ease Organize your dataset dynamically based on failure patterns, geography, device, or any custom field Cluster & Surface Edge Cases with ML-Powered Embeddings Finding the “long tail” of your dataset is hard. Encord helps by: Clustering visually similar images using learned embeddings Letting you surface diverse and representative samples from across the distribution Identifying outliers and edge cases that models tend to miss or misclassify Label Faster with Automation & Integrated QA Once you’ve found the right data, annotation needs to be fast and accurate. Encord delivers: Multimodal annotation tools for image, video, LiDAR, 3D, documents, and more SAM-powered segmentation and pre-labeling tools to accelerate pixel-perfect annotations Custom labeling workflows with built-in QA layers, reviewer roles, and audit logs Close the Loop from Model Evaluation to Data Re-Labeling With Encord, your annotation platform becomes part of the ML feedback loop. Use model predictions to flag weak spots or uncertain examples Automatically route failure cases back into labeling or review queues Measure annotation quality and track the impact of new data on model performance Instead of randomly sampling 10,000 images, teams can focus annotation on the 1,000 examples that actually move the needle.  👉 See how Encord works Architecture Trade-Offs That Matter for Deploying Models on the Edge Once you've built a strong, diverse dataset, the next big challenge comes during deployment, especially when you're running models on edge devices, not cloud servers. In many real-world scenarios, you’re deploying to: Embedded systems in autonomous machines, vehicles, drones, or mobile devices On-premise edge hardware in environments with limited power, compute, or connectivity Ruggedized environments with physical challenges like motion, vibration, dust, or poor lighting That makes model selection and architecture choices absolutely critical. How to Create a CV Data Pipeline  Successful computer vision teams don’t treat model development as linear. Instead, they understand that they need a continuous feedback loop.  Deploy a model in production Monitor for failure cases (missed weeds, misclassifications) Capture and curate those cases Retrain on newly labeled data Evaluate and redeploy rapidly The goal is to optimise data caution, model training and model evaluation. This in turn, will improve model performance exponentially faster. This is especially critical for teams deploying physical AI (like robotics) where safety, efficiency, and explainability are all non-negotiable. 5 Ways to Build Resilient CV Systems Based on our experience with teams across robotics, AgTech, and logistics, here are 5 principles that help CV teams succeed in unpredictable environments: 1. Design for Diversity from Day One Don’t just collect clean daytime images,  gather dusk, glare, partial occlusion, and motion blur examples upfront. A diverse dataset prevents downstream surprises. 2. Prioritize Edge Case Labeling Don’t spread your labeling budget thin across all data. Focus your annotation effort on high-impact edge cases that cause model errors. 3. Build Small, Fast, Resilient Models Your model doesn’t need to be state-of-the-art. It needs to work reliably on real hardware. Optimize for latency, size, and stability. 4. Monitor Contextually Aggregate metrics can be misleading. Monitor performance by environmental condition (e.g., lighting, terrain, sensor angle) to detect hidden weaknesses. 5. Plan for Iteration, Not Perfection You won’t get it right the first time. Build pipelines, not one-off solutions. Make retraining and annotation easy to trigger from real-world feedback. Want to Build Computer Vision That Actually Works in the Real World? If you’re working on robotics, AgTech, autonomous inspection, or any other field where computer vision needs to work in variable, high-noise environments, we’d love to hear from you. At Encord, we help teams: Curate and label edge-case data faster Build datasets optimized for robustness Evaluate and iterate models through tight feedback loops Deploy high-performing CV pipelines with compliance and scale in mind  👉 Book a demo

Generative models are being deployed across a range of use cases, from drug discovery to game design. The deployment of these models in real-world applications necessitates robust evaluation processes. However, traditional metrics can’t keep up with today’s generative AI. So we had Weights & Biases join us on a live event to explore rubric-based evaluation — a structured, multi-dimensional approach that delivers deeper insight, faster iteration, and more strategic model development. This article recaps that conversation, diving into the importance of building effective evaluation frameworks, the methodologies involved, and the future of AI evaluations.  Want a replay? Watch it here. Importance of AI Evaluations Deploying AI in production environments requires confidence in its performance. Evaluations are crucial for ensuring that AI applications deliver accurate and reliable results. They help identify and mitigate issues such as hallucinations and biases, which can affect user experience and trust. Evaluations also play a vital role in optimizing AI models across dimensions like quality, cost, latency, and safety. Traditional vs. Modern Evaluation Methods Traditional evaluation methods often rely on binary success/fail metrics or statistical comparisons against a golden source of truth. While these methods provide a baseline, they can be limited in scope, especially for applications requiring nuanced human interaction. Modern evaluation approaches incorporate rubric-based assessments, which consider subjective criteria such as friendliness, politeness, and empathy. These rubrics allow for a more comprehensive evaluation of AI models, aligning them with business and human contexts. Rubric-Based Evaluation Rubric-based evaluations offer a structured approach to assess AI models beyond traditional metrics. By defining criteria such as user experience and interaction quality, businesses can ensure their AI applications meet specific objectives. This method is customizable and can be tailored to different use cases and user groups, ensuring alignment across business operations.  Download our comprehensive rubric evaluation framework. Implementation and Iteration Implementing rubric-based evaluations involves starting with simple cases and gradually expanding to more complex scenarios. This iterative process allows for continuous improvement and optimization of AI models. By leveraging human evaluations alongside programmatic assessments, businesses can gain deeper insights into model performance and make informed decisions about deployment. Human and Programmatic Evaluations Human evaluations provide invaluable context and subjectivity that programmatic methods may lack. However, scaling human evaluations can be challenging. Programmatic evaluations, such as using large language models (LLMs) as judges, can complement human assessments by handling large datasets efficiently. Combining both approaches ensures a balanced evaluation process that mitigates biases and enhances model reliability. Key Takeaways The integration of rubric-based evaluations into AI development processes is essential for creating robust and reliable AI applications. By focusing on both human and programmatic assessments, businesses can optimize their AI models for real-world deployment, ensuring they meet the desired quality and performance standards. As AI technology continues to advance, the importance of comprehensive evaluation frameworks will only grow, driving innovation and trust in AI solutions.

Video annotation isn’t just image annotation at scale. It’s a different challenge that traditional annotation tools cannot support.  These frame-based tools weren’t designed for the complexity of video, treating video as a sequence of disconnected images. But this has a direct impact on your model performance and ROI, leading to slow, error-prone, and costly annotation. Encord changes that. Built with native video support, Encord delivers seamless, intelligent annotation workflows that scale. Whether you're working in surgical AI, robotics, or autonomous vehicles, teams choose Encord to annotate faster and at scale to build more accurate models. Keep reading to learn why teams like SDSC, Automotus, and Standard AI rely on Encord to stay ahead. The Problem with Frame-by-Frame Annotation Frame-by-frame annotation is often the case when using open-sourced or annotation tooling that is not video native. These tools break videos down into frames, leaving each frame to be annotated as individual images. However, this has negative consequences on the AI data pipeline.  First, it can cause a fragmented workflow. Annotators must work on individual images, losing temporal continuity. Plus, object tracking becomes manual and tedious as each video yields many frames.  There is also a higher risk of inconsistency across annotations. This is because when annotating by frame, some frames may be missed due to volume and the level of detail needed. Inconsistent annotations across frames can also arise when proper context is missing. For example, bounding box drift can take place when each bounding box is drawn independently. Objects are likely to change shape and size between the start and end of a video and with manual annotation, boxes may shift, lag behind, or fluctuate.  Additionally, a lack of temporal awareness leads to quality degradation. When annotators are not able to understand the relationship between frames over time, this leads to bounding box drift, noisy labels, extra QA effort and most detrimentally, poor model performance.  Finally, it can lead to increased time spent and cost incurred. Frame-by-frame annotation is more labor-intensive for both annotators and reviewers, as each video produces thousands of frames. Instead, repeated tasks could be automated with video-native tools. What Makes Encord Different for Video Annotation However, using a video native platform, like Encord, directly mitigates these challenges. A tool that has native video support allows for keyframe interpolation, timeline navigation, and real-time preview. All of which drive greater efficiency and accuracy for developing AI at scale. Built natively for video annotation  Within Encord, video is rendered natively, allowing users to annotate and navigate across a timeline. Annotators can directly annotate on full-length videos, not broken up frames. This not only saves time but also mitigates the risk of missed frames and bounding box drift. Video annotation within the platform also supports playback with object persistence across time. And to further improve efficiency across the AI data pipeline, Encord’s real-time collaboration tools can be used on entire video sequences.  Advanced object tracking & automation Encord also offers AI-assisted tracking to automatically follow objects through frames once it has been labeled in a single or keyframe. Encord uses SAM2 to predict the place of the bounding box across subsequent frames. This supports re-identification even when objects temporarily disappear (ex: occlusion). This reduces the time spent redrawing objects in each frame and helps maintain temporal consistency.  The platform also features interpolation, model-assisted labeling, and active learning. Interpolation is a semi-automated method where the annotator marks an object at key points, and the platform fills in the labels between them by calculating smooth transitions. This leads to massive timesavings and avoids annotator fatigue, without losing accuracy.  Additionally, the active learning integration uses a feedback loop that selects frames for human annotation. Encord Active flags frames or video segments where model predictions are low-confidence. Annotators are guided to prioritize these clips. And finally, the model learns from informative samples, not redundant ones. Maintain temporal context Temporal context is critical for accurate video annotation as it relates to how objects and scenes change over time. With temporal labeling built into the UI, users can annotate events, transitions or behaviors, such a person running or car breaking.  In Encord, annotators can view and annotate frames in relation to previous and future ones. With this timeline navigation and visualisation, users can view object annotations over a video’s entire duration. This provides more context on where an object appears or changes and it is helpful for labeling intermittent objects.  Additionally, this view helps track label persistence across frames, rather than labels that are created per frame. This reduces redundant work and supports smooth object tracking, and avoids annotation drift. Finally, annotators and reviewers can play the full annotated video back to verify consistency across frames.  Why Other Platforms Fall Short Summary: Traditional platforms were designed for image annotation and retrofitted for video. Encord was purpose-built for video. The ROI of Using Encord for Video Annotation Seamless, efficient, and intelligent video annotation workflows drive both direct ROI and model accuracy. The investment in a video native annotation tool pays off through: speed, accuracy, and scalability.  Faster training & deployment  As the platform supports smart interpolation, auto-generated labels, and label persistence across a video timeline, it reduces annotation time significantly. What does this mean for ROI? Faster training data pipelines means faster model development and time to market.  By switching to Encord’s video-native platform, the Surgical Data Science Collective (SDSC) accelerated their annotation workflows by 10x while improving precision and reducing error rates from 20% to nearly zero. Encord’s seamless video rendering, Python SDK integration, and automated quality control features like object tracking and label error detection allowed SDSC to annotate complex surgical procedures at scale. Increased model accuracy Frame sync video annotation leads to better data quality through contextual, consistent annotation. This is because no frames are missed with automated video labeling. Additionally, contextual, timeline-based annotation ensures that intermittent objects are detected accurately, such as those that come in and out of the video. Plus, the more accurate the initial annotations are, the fewer QA cycles and rework. Using Encord’s intelligent visual data curation tools, Automotus was able to reduce its dataset size by 25% while increasing model performance by 20%. Encord’s platform enabled Automotus to localize objects more accurately, iterate faster, and optimize performance. Greater ability to scale production Scale is what ensures you dominate the market and competition. With the ability to annotate thousands of video files with collaborative tools, templates, and automation, scaling becomes a simple next step.  However, scaling also requires a larger, more in-sync team. Which is why support for multi-user teams, audit trails, and version control are all key features.  With Encord, Standard AI transformed its ability to scale video annotation across millions of files, cutting project kick-off times by 99.4%, accelerating video processing by 5x, and saving over $600K annually. By unifying data curation, annotation, and evaluation into a single platform with robust API and SDK support, Standard AI empowered its entire team to collaborate seamlessly and iterate rapidly, leading to production-grade retail intelligence at scale. Key Takewaways Video annotation is not easily scalable using traditional or open-source data annotation tools. The key reason is they break videos into frames, which require tedious, mistake-prone frame-by-frame annotation.  However, for deploying precise computer vision models at scale, using a video native platform is key. Encord supports keyframe interpolation, timeline navigation, and real-time preview which drive greater efficiency, accuracy, and ultimately ROI.  Encord supports smart interpolation, auto-generated labels, and label persistence, reducing annotation time significantly. Faster training data pipelines means faster model development and time to market. And with frame sync and automated video labeling, higher quality training data can be accelerated without comprising accuracy or model performance.   Make the switch to a platform that actually understands video and is built to scale. Book a demo.

When building computer vision models, many machine learning and data ops teams rely on a combination of open-source tools like CVAT and FiftyOne (Voxel51) to manage annotation and data curation. While this setup can work for small-scale projects, it quickly breaks down at scale, creating friction and inefficiencies.  CVAT handles manual annotation, while Voxel51 mainly powers dataset visualization and filtering. However, neither tool spans the full AI data development lifecycle, leading to fragmented workflows. As complexity increases, particularly with video, LiDAR, or multimodal data, so do the limitations.  In this article, we’ll explore the standard CVAT + Voxel51 workflow, highlight the key bottlenecks teams encounter, and explain why many AI teams are making the switch to Encord — a unified platform designed for scalable, secure, and high-performance data development. Typical Existing Workflow Stack: CVAT + Voxel51 CVAT and V51 make up different parts of the AI data development pipeline – annotation and visualization. Both of these are key drivers of successful AI development so let’s understand how these two tools play a role to support these processes.  In large scale AI pipelines, before data can be annotated, it needs to be curated. This includes visualising the data and filtering it in order to exclude outliers, get a deeper understanding of the type of data being worked with, or organising it into relevant segments depending on the project at hand. V51 supports this element of the workflow stack by providing interactive dataset exploration, using filters or similarity search. However, it only supports lightweight image labeling capabilities, with very limited automation. Which leads us to the next part of the AI data workflow.  CVAT is used for manual image annotation, such as creating bounding boxes and doing segmentation on visual data. The tool supports a range of annotation types, such as bounding boxes, polygons, polylines, keypoints, and more. It allows for frame-by-frame annotation, tracking, and managing large datasets. However, it does not support frame-based video annotation and only has basic timeline navigation, as it does not natively work with video.  However, since neither of these two tools are built to span the entire AI data pipeline, they need to be chained together. Outlining what this stack would look like, raw data would be visualised in Voxel51, to gain deeper understanding of distributions and edge cases. Then it would be loaded into CVAT for annotation. However then the data would need to be re-imported into V51 to evaluate annotation quality.  The challenge with this workflow is that it is fragmented. One tool handles curation while the other handles annotation. However, the data then has to be moved between platforms. This can lead to inefficiencies within the data pipeline, especially when developing models at scale or iterating quickly. Additionally, this fragmentation means there is no unified review workflow. Once a model is evaluated, the data then has to be handed off between tools, hindering accuracy improvements at scale. This could mean there is little clarity on version control as there is no central history or audit trail. The CVAT plus V51 workflow is also at the mercy of both tools’ generally sluggish UI. For example, 3D and video are not natively supported and other modalities like audio and text are lacking.  Additionally, for industries with high data security standards, because CVAT and V51 are open source, they are not SOC2 compliant and the lack of traceability can pose risks for those dealing with sensitive data. Why Teams Should Choose Encord TL;DR – Top 3 Reasons to Switch: Stop wasting time gluing tools together — unify your stack. Handle video, LiDAR, and multimodal data natively. Scale securely with built-in governance, QA, and automation. Here are the main reasons ML and data ops teams switch from CVAT + FiftyOne to Encord, based on customers we have engaged with: Unify their AI data pipeline Encord serves as the universal data layer for AI teams, covering the entire data pipeline, from curation and annotation to QA and active learning. This eliminates the need to glue together CVAT, V51, and others. With the CVAT plus V51 stack, an additional tool would also need to be used for model evaluation. By unifying their data pipelines, AI teams have been able to achieve faster iteration, less DevOps overhead, and fewer integration failures.  For example, Plainsight Technologies, an enterprise vision data company, dramatically reduced the cost and complexity of AI-powered computer vision across many verticals and enterprise use cases, including manufacturing, retail, food service, theft, and more. Native video support For complex use cases that require native video annotation, such as physical AI, autonomous vehicles, and logistics, native video support is key. Encord is built for annotating video directly rather than breaking it into frames that have to be annotated individually. When videos are annotated at the frame level, as if they were a collection of images, there is greater risk of error as frames can be missed. A tool that has native video support allows for keyframe interpolation, timeline navigation, and real-time preview. All of which drive greater efficiency and accuracy for developing AI at scale. Additionally, support for long-form videos (up to 2 hours, 200k+ frames) means that thousands of hours of footage can be annotated. This is often the case for physical AI training.  Built-In active learning and labeling  Traditional annotation workflows (like CVAT + Voxel51) are heavily manual. Encord, however, provides native active learning and automated pre-labeling for feedback-driven workflows. Instead of labeling every piece of data, Encord helps you prioritize the most high-impact data to label. You can intelligently sample based on embedding similarity, performance metrics, etc. Model integration in Encord allows users to plug in their own models to automate labeling and integrate predictions directly into the annotation workflow. These predictions can be used as initial pre-labels, which annotators only need to verify or correct, dramatically reducing time spent on repetitive tasks. Using pre-labeling to annotate large datasets automatically, then route those to human reviewers for validation reduces manual effort. It also allows for targeting the most impactful data, teams using Encord can reduce annotation costs, speed up model training cycles and increase annotation throughput. Scales with business needs Because Encord is an end to end data platform, data pipelines can scale with business needs and volumes of data. When it comes to increasing volumes of data, it supports millions of images and frames and up to 5M+ labels/project, whereas CVAT supports 250K - 500K. As a dataset grows, many tools (like CVAT or other open-source platforms) begin to lag, freeze, or break entirely. For example, teams risk the UI taking seconds (or minutes) to load a single frame or even crash during long video sessions or when using complex ontologies. However, Encord is purpose-built to handle large-scale datasets efficiently; it also has fast API calls for programmatic access (Python SDK, integrations) that return quickly even when querying huge data volumes. Therefore, your annotators don’t lose time waiting for images or tools to load. And, developers and MLOps teams can run queries or updates programmatically without performance bottlenecks Finally, faster iteration loops mean quicker time to model improvements. A key feature of Encord that allows for scaling model development is that it allows for 100+ annotators working concurrently, without degrading performance. At the same time, model training can run in parallel, leveraging continuously labeled data to update models faster. To keep teams organized and efficient, Encord includes workflow management tools like task assignment, progress tracking, built-in reviewer roles, and automated QA routing, making it easy to manage large, distributed labeling teams without losing oversight or quality control. Comparing CVAT, Voxel 51 & Encord for scaling data development: SOC2-Compliant with Full Traceability CVAT and FiftyOne are powerful tools, but they are not built for enterprise data governance or QA at scale. For example, they lack reviewer approval flows. CVAT, for instance, doesn’t have a built-in way to assign reviewers, approve/reject annotations, or track review status. However, this is key in enterprise settings to ensure high-quality outputs for downstream ML models. Without this, QA is manual, ad hoc, and hard to scale. Additionally, open-source tools aren’t SOC2 compliant and lack enterprise-grade security features. SOC2 is a rigorous standard for data handling, access controls and audit logging. It is essential for ML teams working with regulated data (e.g. healthcare, finance, defense). Therefore, teams might choose to switch to Encord for role-based access controls, SSO integration and SOC2 compliance when working in specific industries.  Multimodal & 3D native support When it comes to AI development, multimodal capabilities are crucial across a number of different use cases. For example, in surgical video applications, data is required across video, image and documents for maximum context on not only surgery but also the patient’s medical history. For teams requiring 3D, video or other modalities, a platform that can support multiple allows for more accurate and streamlined workflows.  Businesses with complex use cases also require a platform that can handle multi-camera, multi-sensor projects (e.g. LiDAR + RGB).  Multiple angles can also be annotated within one frame, providing annotators with additional context without having to switch between tabs to improve efficiency.   For example, Archetype’s Newton model uses deep multimodal sensor fusion to deliver rich, accurate insights across industries — from predictive maintenance to human behavior understanding. Using Encord, Archetype 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. QA, Review, and Annotation Analytics When it comes to scaling AI data pipelines, having more annotators is step one. However, this requires management and QA, especially if annotation is outsourced. Having built-in review workflows ensures that annotations are correct, especially in cases where annotators need industry-specific knowledge to label successfully. Not only can users build review workflows in Encord but these workflows can be automated using agents. Reviewers can be assigned to certain tasks and leaders can assess annotators’ work through the analytics dashboard. Additionally, label accuracy metrics and consensus scoring are built in, flagging low-consensus annotations for QA. Resolution of discrepancies happen directly in the platform, allowing for quick iteration and maximum accuracy. Most open-source tools like CVAT don’t offer this – you have to build it yourself with scripts or custom QA layers. Encord gives you this out of the box, making quality management and scale possible without reinventing infrastructure. Faster Onboarding and Migration Encord supports direct imports from the most common open-source formats, so teams can migrate quickly without re-labeling or writing custom scripts. This includes but is not limited to: CVAT (XML, JSON) and COCO (JSON). You can upload your existing labels as-is, and Encord will automatically convert them into the internal format with matching ontology and label structure.  There is also no need to write scripts or use third-party conversion tools as Encord includes a visual ontology mapping tool (to match your old classes to the new schema) as well as annotation converters that handle anything from bounding boxes to 3D cuboids. It also supports multi-class, multi-object, and nested hierarchies. For example, if you’ve labeled images in YOLO format, you can import that straight into Encord, and continue working immediately.  Another key reason using Encord beats the CVAT plus Voxel 51 stack is that it offers dedicated onboarding support (especially for mid-size and enterprise customers) and hands-on help to migrate your data. This reduces friction and helps your team become productive within days. Key Takeaway The combination of CVAT and Voxel51 has served many ML teams well for early-stage experimentation, but it comes with trade-offs: disconnected workflows, limited scalability, and manual QA overhead. As teams grow and use cases become more complex, particularly involving video, 3D, or multi-sensor data, this stack hinders scaling. Encord offers a step-change improvement by unifying annotation, curation, review, and automation in one secure platform, removing the need to use multiple tools, write custom scripts, or manually manage QA processes. Teams switching to Encord can achieve 10–100x improvements in throughput, better model iteration speed, and far less operational complexity. If you're hitting the ceiling of open-source tooling and need an AI data platform that can scale, it's time to consider a switch.   Curious how Encord compares for your current stack? Book a demo.

This guide to AI Annotation Platforms with the Best Data Curation presents the top AI data curation platforms, breaking down their key features and best usage. Machine learning engineers and data practitioners spend most of their time sorting out unstructured data. And after all their effort, they end up with data full of mistakes. But with the help of AI annotation platforms, engineers can train SuperData to produce high-quality, error-free data faster with less manual labor.  Why are AI annotation platforms needed for developing AI models? In easy terms, data annotation helps ML algorithms understand your commands. AI annotation platforms train your AI models with clear and accurate data. Since AI can process properly labeled data more efficiently, this leads to increased operational efficiency and reduced costs. It is hard for AI programs to separate one set of data from another. Let's say you fed your ML model with thousands of images of pencils. But that is not enough. You have to label the data (the images), showing which parts of the photos contain the pencils. This helps AI identify how pencils look. If you label the images as 'pens', not 'pencils,' your model will identify those pencils as pens. This is how labeling audio, text, video, images, and all sorts of data can help train AI models.  Image 1 - Master Data Annotation in LLMs: A Key to Smarter and Powerful AI!  AI annotation platforms prevent the risks of models underperforming or producing biased results. Moreover, curating data with AI platforms helps engineers reduce their manual effort and train AI faster. AI annotation platforms help label data, allowing algorithms to understand and differentiate data accurately   What to Look for in a Data Annotation Platform? To select the best AI data annotation platforms, consider these seven key elements.  Prioritization Automation: The AI data curation platform should be able to automate the data prioritization tasks, including filtering, sorting, and selecting the most valuable and relevant data from large datasets. It should also offer automation tools (e.g., SAM, GPT-4o) to speed up annotation.  Customizable Visualization: The AI should offer customizable visualization in the form of tables, plots, and images. This helps you spot biases, edge cases, and outliers by visualizing data.  Model-Assisted Debugging: Look for model-assisted debugging features like confusion matrics. Debugging features help you spot errors like false positives and negatives. Multiple Modality: Look for platforms that can handle various data types and annotation formats, such as text, medical imaging formats, videos, bounding boxes, segmentation, polylines, and key points.  User-friendly interface: The platform’s interface should be a simple and configurable UI that is easy to navigate, even for non-technical users. It should also help you design automated workflows and integrate with necessary platforms.  Streamlined Workflows: The tool should let you create, edit, and manage labels and annotations. Additionally, it should let you integrate into machine learning pipelines and storage systems like cloud storage, MLOps tools, and APIs. Team Collaboration: Look for features that allow team members to collaborate and share feedback on data curation projects.  Modern image labeling tools are essential for building high-quality AI training datasets. They offer advanced annotation features, AI-assisted automation, and robust quality control to efficiently handle large and complex datasets.   Top AI Annotation Platforms for Best Data Curation Here are the top 8 data annotation platforms for efficiently and effectively curating data. Encord Image 2 - Encord  Encord is a unified, multimodal data platform designed for scalable annotation, curation, and model evaluation. It lets you index, label, and curate petabytes of data.  Image 3 - Multimodal Data Annotation Tool & Curation Platform | Encord  It supports images, video, text, audio, and DICOM, making it ideal for teams working across computer vision, NLP, and medical AI. Encord also supports various integrations to connect your cloud storage, infrastructure, and MLOps tools. You can even find useful model evaluation tools inside Encord. Special Features Human-in-the-loop labeling use cases Natural language search AI-assisted labeling (SAM-2, GPT-4o, Whisper) Rubric based evolution Seamless cloud integration (AWS, Azure, GCP) Label editor SOC2, HIPAA, and GDPR compliance  According to G2: 4.8 out of 5. Most reviewers gave Encord a positive rating for its robust annotation capabilities and ease of use. Some users also praised Encord's collaboration and communication tools.  Best for: Generating multimodal labels at scale and curating data with enterprise-grade security. Labellerr  Image 4 - Labellerr Labellerr is an AI-powered data labeling platform offering a comprehensive suite for annotating images, videos, text, and more. Its customizable workflows, automation tools, and advanced analytics enable teams to produce high-quality labels at scale rapidly. Labellerr supports collaborative annotation, robust quality control, and seamless integration with popular machine learning frameworks and cloud services. Special Features Automated and model-assisted labeling Customizable annotation workflows and task management Quality assurance with review and feedback loops Collaboration tools for teams and vendors Version control and centralized guideline management Multi-format and multi-data-type support (images, video, text, audio) Real-time analytics and performance dashboards Secure, enterprise-grade deployment According to G2: 4.8 out of 5. Labellerr is highly rated for its ease of use, efficiency, and strong customer support. Users highlight its ability to handle complex projects and automation that reduces manual effort, though some mention occasional performance lags with large files and a desire for improved documentation. Best for: Enterprises and teams needing scalable, collaborative, and automated annotation workflows across diverse data types. 3. Lightly  Image 5 - Lightly AI Lightly is a data curation platform designed to optimize machine learning workflows by intelligently selecting the most relevant and diverse data for annotation. Rather than focusing on manual annotation, Lightly uses advanced active learning and self-supervised algorithms to minimize redundant labeling and maximize model performance.  Its seamless integration with cloud storage and annotation tools streamlines the entire data pipeline, from raw collection to training-ready datasets and edge deployment. Special Features Smart data selection using active and self-supervised learning Automated data curation for large-scale datasets Integration with existing annotation tools and cloud storage API for workflow automation Data distribution and bias analysis Secure, on-premise or cloud deployment options According to G2: 4.4 out of 5. Users praise Lightly for its intuitive interface, time-saving automation, and ability to reduce labeling costs and effort significantly. Some reviews note a learning curve for advanced features and desire more onboarding resources. Best for: Teams seeking to optimize annotation efficiency, reduce redundant labeling, and accelerate model development, especially for large, complex visual datasets. 4. Keylabs Image 7 - Keylabs  Keylabs is an advanced annotation tool and operational management capability that prepares visual data for machine learning. The platform meets the needs of various industries, including aerial, automotive, agriculture, and healthcare. It presents a range of annotation types, including cuboids, bounding boxes, segmentations, lines, multi-lines, named points, and skeleton meshes. Special Features ML-assisted data annotation It can be installed anywhere  User roles and permission flexibility  Project management Workforce analytics According to G2: 4.8 out of 5. Many users love Keylabs for its unlimited video annotation length, unmatched quality control, and customization capabilities. However, some might find the lack of natural language processing limiting.  Best for: Companies looking for a tool for annotating images and videos with higher quality control.  5. CVAT  Image 7 - CVAT  CVAT helps you annotate images, videos, and 3D files for a range of industries, including drones and aerial, manufacturing, and healthcare and medicine. It supports diverse annotation types such as bounding boxes, polygons, key points, and cuboids, enabling precise labeling for complex computer vision tasks.  With AI-assisted features like automatic annotation and interpolation, CVAT significantly speeds up the labeling process while supporting collaborative workflows and integration with popular machine-learning pipelines. Special Features Image Classification Object Detection Semantic and Instance Segmentation Point Clouds / LIDAR 3D Cuboids Video Annotation Skeleton Auto-Annotation Algorithmic Assistance Management & Analytics According to G2: 4.6 out of 5. According to reviewers, it's an easy-to-use AI data annotation tool with many annotation options. However, some felt that the learning curve can be time-consuming. Best for: Training computer vision algorithms. 6. Dataloop Image 8 - Dataloop  Dataloop helps you collaborate with other data practitioners and build AI solutions. Here, you will find a large marketplace of models, datasets, ready-made workflow templates, etc. It supports multimodal data annotation, including images, video, audio, text, and LiDAR, and can be integrated with other data tools and cloud platforms. You can also train, evaluate, and deploy ML models.  Image 9 - Data Annotation | Dataloop  Special Features API call for pipeline design  Complete control over data pipelines Marketplace with hundreds of pre-created nodes Real-time human feedback Industry-standard privacy and security  According to G2: 4.4 out of 5. Reviewers find this tool highly versatile and scalable, supporting various annotation types. However, some are dissatisfied with its steep learning curve and fewer customization options. Best for: Building AI solutions with easy-to-use and versatile features. 7. Roboflow Image 10 - Roboflow    Roboflow lets you build pipelines, curate and label data. Also, train, evaluate, and deploy your computer vision applications. Roboflow Annotate offers an AI-powered image annotation that accelerates labeling with tools like Auto Label and supports various annotation types. It offers seamless collaboration and integrates with Roboflow’s Internal systems, Edge and Cloud Deployment, and Training Frameworks. Special Features Industry-standard open-source libraries Countless industry use cases Enterprise-grade infrastructure and compliance According to G2: 4.8 out of 5. Many reviewers prefer Roboflow for its outstanding UI and customer service. One reviewer stated that the box prompting features have become outdated. Best for: Building computer vision applications using a free, open-source platform.  8. Segments.ai Segments.ai is a multi-sensor data annotation platform that allows simultaneous labeling of 2D images and 3D point clouds, improving dataset quality and efficiency. It offers advanced features like synced tracking IDs, batch mode for labeling dynamic objects, and merged point cloud views for precise annotation of static scenes.  With machine learning-assisted tools and customizable workflows, Segments.ai helps teams accelerate labeling while maintaining high accuracy across robotics and autonomous vehicle applications. Special Features 1 click multi-sensor labeling Fuse information from multiple sensors Real-time interpolation ML-powered object tracking Simple object overview  Dynamic objects labeling with Batch Mode Auto-labeling with ML models According to G2: 4.6 out of 5. Reviewers positively rated Segments.ai for having many segmentation annotation features. However, some say that finding the multi-sensory annotation requires a bit of a learning curve. Best for: Enterprises wanting faster multi-sensor labeling features.  Final Thoughts: Which Data Curation Platform Should You Choose?  Choosing the right data curation platform depends on your team’s size, industry, and specific needs. While the mentioned tools offer solid annotation and curation features, basic platforms no longer meet the demands of today’s complex AI projects. So, when your projects demand scalability, top-tier data quality, and seamless collaboration, especially for high-stakes AI applications, Encord outperforms the rest. Its purpose-built platform combines advanced automation, multimodal support, and enterprise-grade security to accelerate workflows without compromising accuracy. Ready to elevate your AI projects with the most scalable and secure data curation platform? Discover how Encord can accelerate your annotation workflows and boost model accuracy today.

Introduction: The Hidden Edge in the AI Race This guide to AI data infrastructure breaks down why it’s the real competitive advantage for your AI initiatives, showing how strong pipelines, automation, and feedback loops drive speed, accuracy, and cost efficiency. Artificial intelligence (AI) and machine learning innovation now rely more on data infrastructure than just building smarter models. Leading tech companies like Microsoft, Amazon, Alphabet, and Meta are investing in AI infrastructure. Total capital expansion is projected to grow from $253 billion in 2024 to $1.7 trillion by 2035. That investment signals the actual value is in scalable, performant, and secure data foundations. A MIT Sloan Management Review study found that organizations with high-quality data are three times more likely to gain significant benefits from AI. Managing your AI data infrastructure well and deploying AI with ready AI datasets takes you far beyond your peers in terms of speed, cost, and AI-driven decision-making accuracy. In this article, we will discuss why your AI data infrastructure matters more than model tweaks and how it drives scalability and operational efficiency. We will also go over how feeding enriched data back-to-back into training through feedback loops accelerates model improvements, as well as how infrastructure investments reduce costs and time-to-market.  Why Data Ops Is Not Just a Cost—It’s a Strategic Asset An effective way to improve data infrastructure is by adopting DataOps. DataOps is a set of agile, DevOps-inspired practices that streamline end-to-end data workflows, from ingestion to insights.  Many leaders view data operations as a sunk cost or a side project to support AI models. This perspective limits their AI strategy. In reality, Data Ops is a strategic asset that drives competitive advantage across your AI initiatives. Treating data like code transforms how teams develop AI systems.  When you adopt data-as-code principles, your workflows become: Modular: Data pipelines and workflows are broken down into clear, manageable components. This way, you can swap pipelines or models without having to rebuild your system. Versioned: Just as software engineers version their code, dataset versioning allows you to track changes and maintain full audit trails. Additionally, it helps compare model performance across iterations. Tools like DVC (Data Version Control) enforce version tracking for datasets, just like Git for code. Reusable: Once you codify a pipeline, you can reuse it in AI projects. This reduces duplication of effort and speeds up deployment. The Winning Loop Explained Successful AI initiatives rely on a winning loop that few competitors can replicate. This loop turns data operations from a support function into a strategic growth engine. Here are the loop steps: Capture Live Signals from Real-World Usage Real-world usage generates immediate insights like clicks, transactions, chat logs, and sensor readings. Capture these live signals in real time to align models with actual user behavior. Triage and Enrich Data with Human- and Model-in-the-Loop Workflows Raw captured data is rarely perfect. The data pipeline can use automation features present in tools like Encord. Its active filtering functionality can help filter out, categorize, and pre-process extensive datasets.  In addition, human-in-the-loop (HITL) and model-in-the-loop (MITL) workflows further refine this data for quality. Humans resolve edge cases, ambiguities, and context-specific labels, while AI tools streamline scalable enrichment and annotation tasks. This combined approach optimizes data quality while controlling costs. Feed Data Back into Training, Fine-Tuning, and Evaluation Once data is cleaned and enriched, it flows into versioned pipelines for training, fine-tuning, and automated evaluation. DataOps enforces Continuous Integration and Continuous Delivery (CI/CD) for data, automated tests, schema validation, and real-time Quality Assurance (QA) before the data enters production. Fig 1. Continuous Feedback Loop Drives Competitive Advantage Pro Insight: Teams running this loop continuously build compounding value. They don’t just rely on new algorithms to drive results. Instead, their AI systems evolve daily with fresh, relevant data. Meanwhile, competitors who focus only on tweaking models face diminishing returns without a strong data infrastructure to support ongoing learning. Human-in-the-Loop Machine Learning (HITL) Explained Why Competitors Can’t Copy Your Data Flywheel Having a strong data infrastructure built on an efficient DataOps workflow gives your team a lasting edge. It enables faster iteration, better quality control, and domain-specific learning that compounds over time.  While competitors may replicate your models, algorithms, AI tools, or frameworks, they cannot recreate your data flywheel. This system captures real-time signals, efficiently enriches the data, and feeds it back into the model pipeline.  Fig 2. Data Flywheel and How it Works Datasets as Intellectual Property Treating datasets like intellectual property assets helps you secure a competitive advantage. Companies with high data valuation often have market-to-book ratios 2–3 times higher, reflecting a premium on data quality, ownership, and governance. Internal tools and processes for data versioning, annotation, and lineage are proprietary and provide your organization with a hidden edge. Edge Cases Are Unique to Your Domain Every AI system encounters edge cases, those rare, unexpected scenarios that models struggle with. Only your team understands the full context behind edge-case exceptions, the frequency, and the business impact. Over time, your feedback loops and triage processes effectively capture and resolve these edge cases, empowering you to leverage AI capabilities for deeper domain adaptation. This leads to fine-tuned models deeply aligned with your operations, workflows, and customer interactions. For competitors, if they want to replicate your data, they may require your organizational knowledge and historical context. Real-World Example: A lead data scientist we spoke to expressed the workflow complexity surrounding edge cases and feedback. This extends beyond basic data operations and instead requires knowledge reflected in the company’s datasets, tools, and processes.  How Strong Data Infrastructure Reduces Cost and Time-to-Market A well-built AI data infrastructure delivers measurable savings and accelerates deployment. It replaces fragmented, manual efforts with repeatable systems that enable scalable efficiency across teams and projects. Faster Project Onboarding According to a study, organizations using data pipeline automation saw 80% reductions in time to create new pipelines, moving fast from concept to live data. When data pipelines, workflows, and versioning are standardized, new AI initiatives get up and running quickly. Teams don’t waste weeks rebuilding ingestion scripts or annotation processes. Instead, they use existing modular frameworks to start experimentation and model training sooner, aligning with business needs efficiently. Smarter Annotation at Scale Manual data labeling becomes a bottleneck as datasets grow. Having a tool like Encord AI data management in your data stack automates data triage and integrates human-in-the-loop (HTIL) annotation seamlessly.  Additionally, it deploys model-in-the-loop pre-labeling to reduce manual effort. This optimized annotation process improves data quality and speeds up training cycles. It ensures your AI applications remain accurate and reliable as workloads increase. Fig 3. Human-in-the-Loop Automation Reuse Models and Datasets Across Products An AI data infrastructure that is versioned and reusable enables teams to use existing datasets and pretrained models for new use cases. This reduces duplication of work, lowers operational costs, and shortens time-to-market for AI-powered features across products. High Marginal Costs Without Infrastructure Without a scalable data infrastructure, every new AI project faces high additional costs. Labeling costs increase linearly with customer growth unless automation is used. QA and feedback pipelines struggle to scale, which causes deployment delays and increased operational risk. This is not sustainable for companies looking to grow AI capabilities throughout their ecosystem. CEO Insight: These needs were validated by a CEO of a firm in the computer vision space, who expressed that scaling past the competition has a high marginal cost. However, there is a competitive advantage in building a data labeling infrastructure. Without it, the cost to support each customer remains high, hurting cost efficiency. The Full Guide to Automated Data Annotation Moving from Prototype to Production-Ready AI Experimentation is easy, but scaling AI to production brings a lot of challenges. Many teams build impressive proof-of-concept models. These models perform well in controlled environments but fail when exposed to real-world requirements such as auditability, consistency, and compliance.  Without a strong, end-to-end data infrastructure, moving from prototype to production becomes a bottleneck. To overcome these challenges, teams must adopt repeatable, automated pipelines as the foundation of their infrastructure. Here’s why repeatable pipelines matter:  Consistent Quality: Repeatable data pipelines ensure that the same standards are applied across training, validation, and deployment. This consistency is crucial for maintaining model performance when working with diverse and evolving datasets. Regulated Industry Compliance: Industries such as healthcare and finance require traceable and auditable AI systems. Without automated and versioned data workflows, proving compliance becomes manual, slow, and prone to errors. Faster Iteration Cycles: Automated pipelines reduce time spent on repetitive tasks. Engineers can quickly update models, test them with fresh data, and deploy improvements. This agility gives companies a strong competitive edge. Researcher Testimony:  This is exactly what was described by a researcher at a firm developing agentic AI. While his team had working models, they did not have repeatable data infrastructure. This put them at a competitive disadvantage. Winning with Data—Not Just Models AI innovation is often framed as a race to build smarter generative AI models. Yet, the true differentiator is how your team uses AI data infrastructure to keep models learning, accurate, and ready for production.  A data-focused approach, supported by automated pipelines and strong data management, turns raw datasets into a strategic asset. This boosts AI adoption across industries such as healthcare, finance, and supply chain.  Teams that invest in strong AI data infrastructure build a data flywheel. This flywheel becomes an engine of continuous improvement, embedding domain expertise into every cycle.  Key advantages of data-centric infrastructure include: Faster Iteration: Automated data pipelines reduce turnaround time for model updates. Teams move from weeks to days in deploying improvements, staying ahead in competitive markets. Higher Model Accuracy: Fresh, high-quality, on-distribution data improves predictive performance. Models stay aligned with evolving user behavior and edge cases unique to your ecosystem. Lower Deployment Costs: Reusable pipelines, versioned datasets, and streamlined labeling reduce infrastructure costs. Teams avoid rebuilding from scratch for each use case, maximizing operational efficiency. Fewer Failures in Production: Repeatable, audited workflows ensure that models work as expected when exposed to real-world demands, regulatory requirements, and customer interactions.  Build Your Data-Centric Infrastructure with Encord Encord offers a unified platform to manage, curate, and annotate multimodal datasets. It serves as a direct enabler of robust AI data infrastructure in production settings. Unified Data Management & Curation (Index) Encord handles images, video, audio, and documents within a single environment for in-depth data management, visualization, search, and granular curation. It connects directly to cloud storage, including AWS S3, GCP, Azure, and indexes nested data structures.  Fig 4. Manage & Curate Your Data AI-Assisted and Human-in-the-Loop Annotation Automated labeling offloads repetitive tasks, then humans review and correct edge-case outputs. This HTIL workflow boosts annotation accuracy by up to 30% and speeds delivery by 60%, ensuring data quality at scale. Encord Active monitors model performance, surfaces data gaps, finds failure modes, and helps improve your data quality for model retraining loops. Data‑Centric Curation & Dataset Optimization Encord applies intelligent filters to detect corrupt, redundant, or low-value datapoints. Encord index led to a 20% increase in model accuracy and a 35% reduction in dataset size for Automotus.  Business Impact & Results Encord users report tangible outcomes, including $600,000 saved annually by Standard AI and a 30% improvement in annotation accuracy at Pickle Robot. Its use of the Index alone cut dataset sizes by 35%, saving on compute and annotation costs. Conclusion AI success at scale comes from data infrastructure. Tech companies are investing to scale their infrastructure because data pipelines, governance, and real-time systems deliver growth, productivity, and long-term profitability.  When you treat data as a strategic asset, applying versioning, modular pipelines, and automated workflows, you gain: Faster iteration and deployment, outpacing competitors Higher accuracy and robustness, driven by quality datasets Lower costs and failure rates, thanks to reusable, scalable systems Defensible differentiation, through proprietary feedback loops and domain edge cases If you want a lasting competitive edge, focus on your AI data infrastructure. Scalable pipelines, automated workflows, and strong governance are the foundation your AI strategy needs. 

This guide to video annotation tools for healthcare breaks down how AI teams can create quality annotated video datasets for building accurate healthcare computer vision systems. Every year, hospitals add tens of millions of gastrointestinal endoscopy videos to their archives. A single 15-minute procedure produces around 27,000 high-definition frames, creating a large amount of visual data. However, most of this valuable footage remains unused because converting it into reliable AI requires detailed, frame-by-frame data labeling.  However, manual labeling is slow and costly, and mistakes can put patient safety at risk. Annotating just 100,000 medical images can cost over $1.6 million and take the effort of 20 people working for a whole year. Therefore, healthcare teams need video-annotation platforms like Encord that do more than draw boxes. These platforms must adhere to medical imaging standards, such as DICOM, and ensure the protection of patient health information.  This article will define healthcare AI and the unique demands of video annotation in clinical settings. We will review the best video annotation tools for healthcare, focusing on platforms that create high-quality annotated videos for clinical AI systems.  What Is Healthcare AI? Artificial intelligence (AI) in healthcare refers to the use of machine learning models to assist medical decision-making, diagnose diseases, and plan treatments. These models process medical data and give medical professionals important insights. This helps improve health outcomes and deliver better patient care. One of the biggest advances in healthcare AI stems from computer vision. This technology can detect objects in endoscopy videos. It can also identify organs in CT scans, which makes diagnosis faster and more accurate. Fig 1. Computer Vision in Healthcare The Importance of Video Annotation for the Success of Healthcare Model Video annotation is the process of applying descriptive tags, labels, or masks to specific objects in each frame of a video. This process converts raw video into high-quality annotated datasets that serve as the ground truth for training and validating vision models. This approach helps in the following ways: Drives Model Accuracy: Precise annotations ensure computer vision models learn from reliable data. Poor labels lead to unreliable predictions that healthcare cannot afford, and AI models struggle to meet reliability and safety standards. For example, a mislabeled polyp in a colonoscopy video can cause the model to miss a real polyp in future cases. Enables Auditability and Bias Checks: Quality-annotated data provides the foundation for auditability. This helps regulators understand and trace how an AI model arrived at a particular decision. It also helps detect bias, ensuring that AI algorithms do not repeat the existing biases found in the original data.   Challenges Unique to Healthcare AI Building AI for healthcare is complex. Unlike general computer vision tasks, medical vision models face strict regulatory, technical, and ethical constraints. Common challenges include: Protected Data: Medical data contains PHI, which is highly sensitive. It is subject to strict global regulations, such as HIPAA and GDPR. Any data annotation platform must handle this information carefully and follow these regulations. High-Stakes Accuracy: A single misprediction can affect treatment, patient safety, and outcomes. This requires high-precision annotations, often needing pixel-level accuracy. Techniques like panoptic or instance segmentation are used to achieve this. Limited Expert Time: Annotating medical videos requires domain experts, such as radiologists, pathologists, or ophthalmologists. Their time is limited, making large-scale annotation slow and expensive. Regulatory Scrutiny: Healthcare AI is subject to regulatory oversight, such as the FDA, when used in diagnostics. Medical AI systems must ensure traceability, bias control, and incorporate human override mechanisms. Annotation platforms must provide transparent workflows, quality control, and complete documentation. These challenges slow AI development and increase risk. Purpose-built video annotation tools, like Encord, help address them by enabling secure labeling and supporting regulatory compliance. Fig 2. Encord's DICOM Annotation Tool Key Features to Look for in Video Annotation Tools for Healthcare When selecting video annotation tools for healthcare, consider key features tailored to the unique needs of medical applications. This ensures high-quality training data and efficient workflows. The list below highlights some crucial features a video annotation tool should have. Support for Medical Image Formats: Look for a video annotation platform that supports standard medical imaging formats, like DICOM and NIfTI. It should also handle 3D and 4D volumetric data to enable annotation across spatial and temporal dimensions. Ability to Handle Large Video Files: Medical videos are often large and high-resolution. Tools should efficiently manage large file sizes and high frame counts without performance degradation to ensure a smooth annotation process. Collaboration Features: Effective annotation requires input from multi-disciplinary healthcare experts. Look for a tool that supports collaborative workflows, allowing teams to coordinate annotation tasks and reviews efficiently. Clinician-Centric User Interface: Look for a tool with a user-friendly interface for medical professionals. Features like intuitive timelines, measurement overlays, and voice annotations for quick feedback can enhance usability by simplifying complex annotation features. Automated and Active Learning Features: AI-assisted and pre-labeling can speed up the annotation process. The human-in-the-loop approach combines the speed of automation with the precision of human quality control, ensuring high-quality datasets for machine learning models. Secure Deployment Options: Medical data demands robust security. Choose tools that provide on-premises, virtual private cloud (VPC), or fully managed cloud solutions to protect sensitive information. Compliance with Healthcare Regulations: Annotation platforms must meet HIPAA and FDA standards to protect patient data and ensure legal compliance throughout the annotation process. Evaluating tools against these features can help in selecting a video annotation platform that aligns with the specific needs of your healthcare AI projects. Top Video Annotation Tools for Healthcare: Overview Top Video Annotation Tools for Healthcare The complexity of medical video data and the need for robust annotation workflows require teams to have platforms that combine automation, collaboration features, and compliance-ready infrastructure. Below is a list of video annotation tools commonly used in medical applications.  Encord Encord is a collaborative AI data annotation platform that provides enterprise-grade solutions for complex, regulated AI projects, especially in the healthcare domain. The platform's objective is to accelerate the deployment of medical AI products by allowing the development of high-quality training datasets. Encord Supported Medical Imaging Formats and Video Formats Encord supports standard medical imaging formats, including DICOM and NIfTI. It offers 3D viewing options across sagittal, axial, and coronal planes, with window levels configurable via Hounsfield units, a standard in radiology. Encord also supports various video formats and resolutions. This ensures versatility across a range of medical video sources.    Fig 3. Medical Data 3D View in Encord Encord Supported Annotation Types Encord supports various annotation types for detailed medical labeling. These include polygons for irregular shapes, key points for specific anatomical landmarks, and bounding boxes for object detection. It also supports rotatable boxes for objects in different orientations, polylines for linear structures, and classifications for video segments. A valuable feature for medical imaging is its support for Primitives (skeleton templates). These are essential for specialized annotations of template shapes, such as 3D cuboids and rotated bounding boxes, used to capture an object's three-dimensional structure from video. Encord also offers panoptic segmentation, enabling pixel-level labeling of both countable objects and amorphous regions. Fig 4. Encord Pixel-Perfect Labeling Encord AI-Assisted Labeling Capabilities Encord helps automate the annotation workflow through AI-assisted labeling. This includes automated object tracking and interpolation. These techniques intelligently fill in annotations between video frames, reducing the manual effort required across long sequences. Encord also integrates Meta AI’s Segment Anything Model (SAM), allowing instant, pixel-perfect segmentation masks. Moreover, it uses a micro-model approach and Models-in-the-Loop. This allows users to seamlessly integrate their custom models for pre-labeling datasets.  Fig 5. Instantly Segment Anything in Encord Learn how to annotations medical data Encord Security and Compliance Encord strictly follows compliance standards, such as HIPAA and GDPR. It uses military-grade encryption, and all your information is encrypted at rest with AES-256. The annotation platform offers flexible deployment options, including VPC and on-premises solutions, to meet the needs of organizations with strict data protection requirements. Continuous security monitoring and multi-layered, role-based access controls are also in place.  Supervisely Supervisely is a computer vision platform that focuses on surgical video annotation and DICOM file management. It supports multi-planar labeling across coronal, sagittal, and axial planes, providing a comprehensive view for annotators.  Supervisely's video labeling tools enable automatic object tracking, detection, and segmentation on videos by using state-of-the-art (SOTA) neural networks. It integrates advanced models, including Meta AI's SAM, MixFormer, RITM, ClickSeg, and EiSeg. These models allow annotators to provide real-time feedback for correcting model predictions, streamlining the process. Supervisely’s privacy features, such as data encryption and data anonymization, align with healthcare regulations. It supports deployment on all major cloud providers (AWS, GCP, Azure) with flexible configurations. Its user-friendly interface makes it ideal for annotating surgical videos for AI-assisted surgery, surgical robotics, and clinical research. OHIF OHIF is an open-source platform for viewing and annotating medical images, including video data. It supports various medical imaging formats, including DICOM. It uses Cornerstone3D for its strong annotation features, enabling tasks such as surgical video analysis and diagnostic imaging.  OHIF features an advanced video viewport for imaging workflows, supporting HTML5 video streams. This enables precise frame-by-frame analysis and longitudinal video annotations. Its web-based interface provides easy access and real-time collaboration among medical professionals, enhancing workflow efficiency. OHIF also introduced AI-powered Labelmap Assist for quickly extending an existing segmentation to the next or previous slice using the Segment Anything (SAM) model. Fig 8. Automatic Labelmap Slice Interpolation Labellerr Labellerr is a video annotation platform known for precision, scalability, and AI integration. It provides tools and solutions for the healthcare and biotechnology sectors. It has a strong focus on reliable data annotation for medical AI applications while adhering to regulations like HIPAA and GDPR. Labellerr offers DICOM annotation tools and supports both 2D and 3D medical image formats, including DICOM and NIfTI. It also works with 2D formats like X-rays and CT scans. The platform handles various types of video data, ensuring broad applicability across different medical video sources. Fig 9. Labeller Bitmask Annotation The platform speeds up the annotation process through its AI assistance and smart feedback loop, leading to 10 times faster labeling. Automated labeling is applied to tasks such as identifying tumors, fractures, and organ structures in medical scans, reducing manual effort and improving accuracy. CVAT  CVAT is an open-source image and video annotation tool. Its open-source architecture allows customization. This allows users to adapt it for specific medical imaging files like DICOM or NIfTI, which are not natively supported but can be integrated.  CVAT supports a variety of annotation types, including bounding boxes, polygons, and skeletons, with interpolation for efficient labeling of video frames. As an open-source solution, CVAT provides users with a high degree of control over their data and environment. However, self-hosting CVAT in a highly secure, airtight server environment (e.g., without internet access) can present significant challenges. Fig 10. CVAT Video Annotation Kili Technology Kili's video annotation tool supports all common video formats and annotation types. This includes bounding boxes, polygons, and semantic segmentation, suitable for medical videos. Its data management supports help teams manage long videos with over 100,000 frames and multiple objects per frame.  The platform’s AI-driven workflows use its Model-in-the-loop feature, which enables users to connect their models and generate pre-annotations. It also enhances segmentation tasks with foundation models such as SAM. Fig 11. Kili's Video Annotation Tool Kili Technology is SOC2 and HIPAA certified, ensuring privacy and protection against bias. It provides secure data storage on its platform or connects to popular cloud providers like Azure, Google, or AWS. The platform also supports Single Sign-On (SSO) for easy and secure access. RedBrick AI RedBrick AI is a SaaS application for medical image viewing and video annotation. It helps healthcare AI teams create quality outcomes datasets. The tool supports all radiology modalities, including X-ray, CT, MRI, and Ultrasound, as well as 2D and 3D formats. Additionally, it supports video formats and medical data formats like DICOM, NRRD, NIfTI, and MP4. RedBrick AI includes an Auto Annotator, an automatic segmentation tool powered by Meta AI's SAM. It helps generate 2D and 3D segmentation masks for hundreds of structures on CT and MR images. Fig 12. RedBrick AI Labeling Its Mask Propagation Tool allows users to annotate a single slice and then propagate that mask across a defined range of slices, making volumetric annotation much faster. RedBrick AI follows HIPAA standards, supporting radiology AI teams in building training datasets for diagnostics. 3D Slicer 3D Slicer is an open-source platform for 3D medical image and video annotation, widely used in research. It supports DICOM, NIfTI, and medical videos, with modules for time-series annotation like surgical procedures. The tool supports DICOM standard interoperability, including 2D, 3D, and 4D images, segmentation objects, and structured reports. It also provides volume rendering, surface rendering, and slice display, enabling medical data to be presented in various ways. Additionally, 3D Slicer integrates AI-assisted annotation tools that can automatically segment anatomical structures using pre-trained or custom models. It supports NVIDIA Clara AI-based automatic segmentation, along with a  MONAI plugin for 3D volumes segmentation annotation. Fig 13. 3D Slicer AI-assisted Segmentation Why Encord Stands Out While all the listed tools above offer valuable capabilities for healthcare AI, Encord is particularly noteworthy. Its tailored features directly address the strict demands of medical video. Unlike many general-purpose annotation platforms, Encord is optimized explicitly for medical imaging. It offers native DICOM and NIfTI support, which is essential for managing complex multidimensional data in medical imaging.  Encord is an enterprise-grade platform built to manage petabytes of medical data and multiple users without performance degradation. Its project management features make managing complex annotation project pipelines easier. The platform’s powerful tools enable high-throughput video labeling at scale.  Encord offers collaboration tools that allow annotators, reviewers, and domain experts to work while maintaining strict access controls. It is SOC 2 Type II, HIPAA, and GDPR compliant, and uses military-grade encryption. This commitment to security ensures confidence in developing AI solutions in regulated healthcare environments.   Learn how to automate video annotation Key Takeaways Video labeling tools are important for creating trustworthy healthcare AI systems. They help convert raw video data into quality annotated datasets that power machine learning algorithms for diagnostics, treatment planning, and clinical automation. These tools must handle complex medical imaging formats, support collaboration, and comply with healthcare regulations to ensure safe AI deployment. Below are key points to remember when selecting and using video annotation tools for healthcare AI projects. Best Use Cases for Video Annotation Tools: The most effective video annotation tools in healthcare are used for surgical video analysis, segmentation in radiology, and training models for medical robotics. These use cases demand pixel-level accuracy, expert-driven labeling, and audit-ready documentation. Challenges in Healthcare Annotation: Healthcare video annotation presents challenges. These include managing large-scale datasets, ensuring PHI security, and meeting HIPAA and FDA compliance. Tools must also support 3D and 4D data and streamline annotation workflows. Encord for Healthcare AI: Encord supports various medical imaging formats. Its AI-assisted approach to labeling videos helps teams optimize speed and consistency in producing annotated datasets. Other tools, such as Supervisely, V7, Kili, Labellerr, and RedBrick AI, also offer strong healthcare-focused capabilities. The best choice depends on your data types, project scale, regulatory needs, and workflow priorities.

In a joint session hosted by our ML Lead Frederik and Neuron’s Head of AI, Doogesh, we pulled back the curtain on what it really takes to turn machine learning (ML), large language models (LLMs), and neuroscience-powered insights into systems that drive measurable ROI. Below are the key takeaways from this insight-packed webinar. How to go from ML models to measurable impact Most companies already use ML in production, but not all of them are extracting business value. This webinar provided a blueprint for bridging that gap: Align model behavior with business goals: Models are only as good as their outcomes. Ensuring alignment with ROI and implementing human-in-the-loop are critical. Unify models into VLMs: Combine Claude, GPT, and proprietary neuroscience models into vertically integrated vision-language systems (VLMs) for performance that scales. Move from suggestion to action: Battle-tested recommendation systems become the foundation for guiding GenAI tools development. Neurons AI: Science-powered ad recommendations Doogesh, Head of AI at Neurons, introduced the audience to how they deliver unlimited AI-driven ad recommendations backed by neuroscience. Here’s how Neurons has used AI to transform advertising performance: Pre-test ads instantly: No more waiting on A/B testing; get results in seconds. Visual Attention Prediction: Eye-tracking and heatmaps predict what grabs users' focus. Optimize messaging and recall:  Get early signals on engagement, recall, and cognitive load. Benchmarking across industries: Track performance against industry-specific standards for more accurate insights. How neuroscience powers better ML Through tests like the Visual Memory Game (VMG) and Fast Response Test (FRT), Neurons collects high-quality data on: Memory: Identifying what users remember from fast image streams. Engagement: Reaction speed shows how compelling a visual is. Focus: Area-of-interest (AOI) heatmaps reveal content clarity. Cognitive Load & Demand:  EEG and entropy-based metrics quantify complexity. This rich dataset feeds into their CIS score—a single, weighted measure that correlates strongly with ROI.   ✅ Key Insight: CIS scores can serve as evaluation metrics in RL setups for autonomous recommendation systems. Industry & segment specificity matters Doogesh emphasized that not all ads, or audiences, are created equal: Different industries and demographics respond to different visual cues Models trained on generalized data underperform unless tuned to specific audience behaviors Benchmarking across segments and geographies helps ensure accurate and trusted recommendations Industry segmentation for benchmarking Annotation of advertisement using image annotation tool, Encord Models trained on generalized data underperform unless tuned to specific audience behaviors. Blueprint for VLM-powered recommendation systems Frederik and Doogesh then provided some a generalized framework for building actionable recommendation engines: Step 1: Build production-grade ML models Let them serve as the foundation for understanding what drives value today. Step 2: Feed insights into a recommender system LLMs act as judges, vibing off human edits, customer feedback, and content scoring systems like CIS. Step 3: Create agentic systems Use data and evaluation pipelines to enable iterative, autonomous systems that learn, act, and improve with minimal human input. “What activities that add value today also generate data for tomorrow’s AI?” From Evaluation to Autonomy As performance benchmarks stabilize, we move closer to autonomous agents that: Generate, evaluate, and adapt creatives Collect real-time feedback like click-through rates Train on their own outputs to self-improve. But this shift hinges on: Strong content scoring systems (like CIS), Reliable evaluation pipelines, and Rich historical data for training. Final Framework: A 3-Step Rocket to ROI 1. Build ML that Works Use neuroscience data and LLMs to create foundational models. 2. Use Models to Guide Recommendations Extract insights, benchmark, and personalize at scale. 3. Turn Recommendations into Agentic Tools Close the loop with systems that act autonomously and improve over time. Closing Thoughts The Encord x Neurons collaboration shows a future where ML systems are no longer siloed black boxes, but value-generating engines deeply embedded into business strategy, backed by science, powered by LLMs, and guided by ROI.