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Generative AI is making its foothold in many industries, from healthcare to marketing, driving efficiency, boosting creativity, and creating real business impact. Organizations are integrating LLMs and other foundation models into customer-facing apps, internal tools, and high-impact workflows. But as AI systems move out of the lab and into the hands of real users, one thing becomes clear: Evaluation is no longer optional, it’s foundational. On a recent webinar hosted by Encord and Weights & Biases, industry experts Oscar Evans (Encord) and Russell Ratshin (W&B) tackled the evolving demands of AI evaluation. They explored what’s missing from legacy approaches and what it takes to build infrastructure that evolves with frontier AI, rather than struggling to catch up. Here are three key signs your AI evaluation pipeline is broken and what you can do to fix it. 1. You're Only Measuring Accuracy, Not Alignment Traditional evaluation frameworks tend to over-index on objective metrics like accuracy or BLEU scores. While these are useful in narrow contexts, they fall short in the real world where AI models need to be aligned with human goals and perform on real-world,complex tasks that have nuance.  Oscar Evans put it simply during the webinar:  “If we're looking at deploying applications that are driving business impacts and are being used by humans, then the only way to make sure that these are aligned to our purpose and that these are secure is to have humans go in and test them.” AI systems can generate perfectly fluent responses that are toxic, misleading, or factually wrong. Accuracy doesn’t catch those risks, whereas alignment does. And alignment can't be assessed in a vacuum. Fix it: Implement rubric-based evaluations to assess subjective dimensions like empathy, tone, helpfulness, and safety Incorporate human-in-the-loop feedback loops, especially when fine-tuning for use cases involving users, compliance, or public exposure Measure alignment to intent, not just correctness, particularly for open-ended tasks like summarization, search, or content generation 2. Your Evaluation Is Static While Your Model Evolves While models are constantly improving and evolving, many teams still run evaluations as one-off checks, often just before deployment, not as part of a feedback loop.  This creates a dangerous gap between what the model was evaluated to do and what it’s actually doing out in the wild. This is especially true in highly complex context or dynamic environments that require precision in edge-case deployment, such as healthcare or robotics.  “Evaluations give you visibility,” Russ from W&B noted. “They show you what’s working, what isn’t, and where to tune.” Without continuous, programmatic, and human-driven evaluation pipelines, teams are flying blind as models drift, edge cases emerge, and stakes rise. Fix it: Treat evaluation as a on par with training and deployment in your ML stack Use tools like Encord and Weights & Biases to track performance across dimensions like quality, cost, latency, and safety, not just during dev, but in production Monitor model behavior post-deployment, flag regressions, and create feedback loops that drive iteration 3. You're Lacking Human Oversight Where It Matters Most LLMs can hallucinate, embed bias, or be confidently wrong. And when they're powering products used by real people, these errors become high-risk business liabilities. Programmatic checks are fast and scalable, but they often miss what only a human can see: harmful outputs, missed context, subtle tone problems, or ethical red flags.  “There’s nothing better than getting human eyes and ears on the result set,” Russ noted. Yet many teams treat human evaluation as too slow, too subjective, or too expensive to scale. That’s a mistake. In fact, strategic human evaluation is what makes scalable automation possible. Fix it: Combine programmatic metrics with structured human feedback using rubric frameworks Build internal workflows, or use platforms like Encord, to collect, structure, and act on human input efficiently Ensure diverse evaluator representation to reduce systemic bias and increase robustness When done right, human evaluation becomes not a bottleneck, but a force multiplier for AI safety, alignment, and trust. Rethinking Evaluation as Infrastructure The key takeaway: AI evaluation isn’t just a QA step. It’s core infrastructure that not only ensure success of the models being deployed in the present but also those being developed for the future.  If you're building AI that interacts with users, powers decisions, or touches production systems, your evaluation stack should be: Integrated: built directly into your development and deployment workflows Comprehensive: covering not just accuracy but subjective and contextual signals Continuous: updating and evolving as your models, data, and users change Human-Centric: because people are the ones using, trusting, and relying on the outcomes This is the key to building future-ready AI data infrastructure. Not only will this allow high-performance AI teams to keep up with progress but also implement tooling that lets them move with it. Final Thought If your AI evaluation is broken, your product risk is hidden. And if your evaluation can’t evolve, neither can your AI. The good news? The tools and practices are here. From rubric-based scoring to human-in-the-loop systems and real-time performance tracking, teams now have the building blocks to move past ad hoc evaluation and toward truly production-ready AI.  Want to see what that looks like in action? Catch the full webinar with Encord + Weights & Biases for a deep dive into real-world evaluation workflows.

Welcome to the first edition of ‘Meet the Encord Eng Leads’, a mini series where we sit down with the team behind the code to learn more about life (and engineering) at Encord. In this edition, we’re chatting with Rad, one of Encord’s founding engineers and now squad lead for our physical AI tooling team. From real-time 3D visualisation to multi-sensor fusion and robotics infrastructure, Rad’s team is working on some of the most exciting engineering problems in the AI space. We dive into what it’s like to build cutting-edge tools, the problem his squad is solving and the future of Encord!  We are also hiring for a number of engineering roles in UK & SF! You can find them here: https://encord.com/careers/ or reach out to Kerry for more info.  So Rad, let’s kick things off! What’s your role at Encord, and what are you currently working on? Rad: I joined Encord as the founding engineer 4 years ago, and over time I’ve worked across a wide range of projects. These days, I lead a squad focused on what we call physical AI tooling — building out the platform capabilities to support robotics, autonomous vehicles, and other embodied AI systems. What kind of problems are you solving right now? Rad: We’re tackling problems at the intersection of user experience, ML infrastructure, and data tooling. Think: how do you visualize millions of data points in a way that’s actually useful? How do you build labeling workflows that feel like magic but scale like enterprise software? It’s a mix of product thinking, technical architecture, and a healthy respect for browser performance limits. That means a lot of hands-on work with 3D sensor data — think LiDAR, radar, and multi-camera setups — and fusing those inputs into coherent scene reconstructions. We’re essentially building infrastructure to enable machines to perceive and reason about the real world. It’s not just about parsing pixels anymore; it’s about helping users create high-quality datasets and training pipelines for AI systems that interact physically with their environment. It’s exciting because most models today still operate in digital-only domains — text, audio, static images. But the physical world is where AI gets really interesting (and useful). Helping move the field from sci-fi to real-world impact — whether that’s safer self-driving cars or smarter home robotics — is incredibly rewarding engineering. And why does working on this problem space excite you? Rad: It’s the frontier of AI. Everyone’s focused on large language models, but what happens when those models need to drive a car or fly a drone? Suddenly, clean data, spatial awareness, and real-time feedback loops matter — a lot. That’s our domain. It’s messy, complex, and you can’t just throw more compute at the problem. You need better tools, better data, and thoughtful engineering. That’s what we’re building. So, what originally drew you to Encord? Rad: A few things: the mission, the people, and the chance to work on some very non-trivial engineering problems. We’re enabling the future of AI — helping teams working on everything from autonomous vehicles to surgical robotics. And during the interviews, it was clear this wasn’t just a smart team — it was a kind one, too. High standards, low ego. That's rare. Surgical robotics! Wow. Could you also tell us a bit about your squad? Rad: Curious, high-trust, and delightfully nerdy. We move fast, but we’re thoughtful. Everyone’s got strong opinions, but there’s no ego — just a shared desire to build great stuff. Debugging a race condition feels like a team sport, and shipping something weirdly performant gets you Slack kudos and probably a meme. It’s a good mix of serious engineering and not taking ourselves too seriously. Sounds pretty awesome. What advice would you give to someone thinking about joining the team? Rad: Be curious, be proactive, and bring your whole self. If you love solving hard problems, collaborating with smart humans, and shipping things that matter — you’ll fit right in. Oh, and don’t be afraid to jump into a conversation or share an idea. Initiative is always welcomed here. What excites you most about the future of Encord? Rad: The size of the problem we’re solving. AI is changing fast, but data tooling hasn’t caught up — especially for teams building multimodal, physical-world systems. We’re not just filling a gap; we’re building entirely new infrastructure that will become table stakes in the next few years. It feels like we’re still early — and that’s exciting. The things we’re building now are going to shape how future AI systems get trained.  And lastly, one word to describe life at Encord? Rad: Alive. In the best way. It’s fast-paced, challenging, and full of people who genuinely care. You’re never just clocking in — you’re building something that could shape the future of AI. That’s pretty cool. You can connect with Rad here. And keep your eyes peeled for the next episode! 

In the early days of computer vision, machine learning infrastructure was relatively straightforward. You collected a dataset, labeled it, trained a model, and deployed it. The process was linear and static because the models we were building didn’t need to adapt to changing environments. However, as AI applications advance, the systems we're building are no longer just models that make predictions. They are agents that perceive, decide, act, and learn in the real world. As model performance continues to exponentially improve, the infrastructure needs to be optimized for dynamic, real-world feedback loops. For high performance AI teams building for complex use cases, such as surgical robotics or autonomous driving, this future-ready infrastructure is crucial. Without it, these teams will not be able to deliver at speed and at scale, hurting their competitive edge in the market.  Let’s unpack what this shift really means, and why you need to rethink your infrastructure now, not after your next model hits a wall in production. What Model-Centric Infrastructure Looks Like In traditional ML workflows, the model was the center. Whereas surrounding infrastructure, like data collection, annotation tools, evaluation benchmarks, was all designed to feed the training process. That stack typically looked like this: Collect a dataset (manually or from a fixed pipeline) Label it once Train a model Evaluate on a benchmark Deploy  But three things have changed: The tasks are getting harder – Models are being asked to understand context, multi-modal signals, temporal dynamics, and edge cases (ex: robotics applications) The environments are dynamic – Models are no longer just processing static inputs. They operate in real-world loops: in hospitals, warehouses, factories, and embedded applications. The cost of failure has gone up – It's not just about lower accuracy anymore. A brittle perception module in a surgical robot, or a misstep in a drone’s navigation agent, can mean real-world consequences. Why We Are Shifting from Models to Agents An agent isn’t just a model. It’s a system that: Perceives its environment (via CV, audio, sensor inputs, etc.) Decides what to do (based on learned policies or planning algorithms) Acts in the world (physical or digital) Learns from its outcomes The key here is that agents learn from outcomes.  Agents don’t live in the world of fixed datasets and static benchmarks. They live in dynamic systems. And every decision they make produces new data, new edge cases, and new sources of feedback. That means your infrastructure can’t just support training. It has to support continuous improvement, or rather a feedback loop. What Agents Demand from Infrastructure Here’s what AI agents that are operating in real world, dynamic environment demand from training infra: 1. Feedback Loops Rather than a stack with a one-way flow (data → model → prediction), agents generate continuous feedback. They need infrastructure that can ingest that feedback and use it to trigger re-training, relabeling, or re-evaluation. 2. Behavior-Driven Data Ops The next critical datapoint isn't randomly sampled, it’s based on what the agent is doing wrong. The system needs to surface failure modes and edge cases in order to automatically route them into data pipelines. 3. Contextual Annotation Workflows For agents operating in multimodal environments (e.g. surgical scenes, drone footage, or robotic arms), you need annotation systems that are aware of context. This is why a tool like Encord’s multilingual editor is helpful, allowing different views of a single object to be annotated simultaneously.  Encord HTIL workflow 4. Real-Time Evaluation & Monitoring Where the real challenge with agents and complex models lie is when they are productionized. This is where failures and edge cases often come to the surface. Therefore, AI infra must be evaluated and monitored in real-world conditions.  5. Human-in-the-Loop, Where It Matters Your human experts are expensive. Don’t waste them labeling random frames. Instead, design your workflows so that humans focus on critical decisions, edge-case adjudication, and behavior-guided corrections. How to Use Encord to Build AI Infra for CV Agents At Encord, we’re building the data layer for frontier AI teams. That means we’re not just another labeling tool, or a dataset management platform. We’re helping turn raw data, model outputs, agent behaviors, and human input into a cohesive system. Let’s take some complex computer vision use cases to illustrate these points: Closing the Feedback Loop An AI-powered surgical assistant captures post-op feedback. That feedback is routed through Encord to identify mislabeled cases or new patterns, which are automatically prioritized for re-annotation and model update. Surgical video ontology in Encord Behavior-Based Data Routing An autonomous warehouse robot team uses Encord to tag failure logs. These logs automatically trigger active learning workflows, so that the most impactful data gets labeled and reintroduced into training first. Contextual, Domain-Aware Labeling In computer vision for aerial drone surveillance, users annotate multi-frame sequences with temporal dependencies. Encord enables annotation with full temporal context and behavior tagging across frames. Agricultural drone CV application Dynamic Evaluation Metrics Instead of relying on outdated benchmarks, users evaluate models live based on how agents perform in the real environment. Why This Matters for AI/ML Leaders If you're a CTO, Head of AI, or technical founder, this shift should be on your radar for one key reason: If your infrastructure is built for yesterday’s AI, you’ll spend the next 18 months patching it. We’re seeing a growing split: Companies that invest in orchestration and feedback are accelerating. Companies still on static pipelines are drowning in tech debt and firefighting. You don’t want to retrofit orchestration after your systems are in production. You want to build it in from the start, especially as agents become the dominant paradigm across CV and multi-modal AI. The AI landscape is moving faster than most infrastructure can handle. Therefore, you need infrastructure that helps those models learn, adapt, and improve in the loop. That is where Encord comes in: Build agent-aware data pipelines Annotate and evaluate in dynamic, context-rich environments Automate feedback integration and retraining triggers Maintain human oversight where it matters most Adapt infrastructure alongside AI advancement Key Takeaways The AI systems of tomorrow won’t just predict, they’ll act, adapt, and improve. They’ll live in the real world, not in your test set. And they’ll need infrastructure that can evolve with them. If you’re leading an AI team at the frontier, now’s the time to modernize your infrastructure. Invest in feedback, automation, and behavioral intelligence. Learn how Encord powers future-ready AI infrastructure →

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.