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We’re excited to introduce support for 3D, LiDAR and point cloud data. With this latest release, we’ve created the first unified and scalable Physical AI suite, purpose-built for AI teams developing robotic perception, VLA, AV or ADAS systems. With Encord, you can now ingest and visualize raw sensor data (LiDAR, radar, camera, and more), annotate complex 3D and multi-sensor scenes, and identify edge-cases to improve perception systems in real-world conditions at scale. 3D data annotation with multi-sensor view in Encord Why We Built It Anyone building Physical AI systems knows it comes with its difficulties. Ingesting, organizing, searching, and visualizing massive volumes of raw data from various modalities and sensors brings challenges right from the start. Annotating data and evaluating models only compounds the problem.  Encord's platform tackles these challenges by integrating critical capabilities into a single, cohesive environment. This enables development teams to accelerate the delivery of advanced autonomous capabilities with higher quality data and better insights, while also improving efficiency and reducing costs. Core Capabilities Scalable & Secure Data Ingestion: Teams can automatically and securely synchronize data from their cloud buckets straight into Encord. The platform seamlessly ingests and intelligently manages high-volume, continuous raw sensor data streams, including LiDAR point clouds, camera imagery, and diverse telemetry, as well as commonly supported industry file formats (such as MCAP). Intelligent Data Curation & Quality Control: The platform provides automated tools for initial data quality checks, cleansing, and intelligent organization. It helps teams identify critical edge cases and structure data for optimal  model training, including addressing the 'long-tail' of unique scenarios that are crucial for robust autonomy. Teams can efficiently filter, batch, and select precise data segments for specific annotation and training needs. 3D data visualization and curation in Encord AI-Accelerated & Adaptable Data Labeling: The platform offers AI-assisted labeling capabilities, including automated object tracking and single-shot labeling across scenes, significantly reducing manual effort. It supports a wide array of annotation types and ensures consistent, high-precision labels across different sensor modalities and over time, even as annotation requirements evolve. Comprehensive AI Model Evaluation & Debugging: Gain deep insight into your AI model's performance and behavior. The platform provides sophisticated tools to evaluate model predictions against ground truth, pinpointing specific failure modes and identifying the exact data that led to unexpected outcomes. This capability dramatically shortens iteration cycles, allowing teams to quickly diagnose issues, refine models, and improve AI accuracy for fail-safe applications. Streamlined Workflow Management & Collaboration: Built for large-scale operations, the platform includes robust workflow management tools. Administrators can easily distribute tasks among annotators, track performance, assign QA reviews, and ensure compliance across projects. Its flexible design enables seamless integration with existing engineering tools and cloud infrastructure, optimizing operational efficiency and accelerating time-to-value. Encord offers a powerful, collaborative annotation environment tailored for Physical AI teams that need to streamline data labeling at scale. With built-in automation, real-time collaboration tools, and active learning integration, Encord enables faster iteration on perception models and more efficient dataset refinement,  accelerating model development while ensuring high-quality, safety-critical outputs. Implementation Scenarios ADAS & Autonomous Vehicles: Teams building self-driving and advanced driver-assistance systems can use Encord to manage and curate massive, multi-format datasets collected across hundreds or thousands of multi-hour trips. The platform makes it easy to surface high-signal edge cases, refine annotations across 3D, video, and sensor data within complex driving scenes, and leverage automated tools like tracking and segmentation. With Encord, developers can accurately identify objects (pedestrians, obstacles, signs), validate model performance against ground truth in diverse conditions, and efficiently debug vehicle behavior.  Robot Vision: Robotics teams can use Encord to build intelligent robots with advanced visual perception, enabling autonomous navigation, object detection, and manipulation in complex environments. The platform streamlines management and curation of massive, multi-sensor datasets (including 3D LiDAR, RGB-D imagery, and sensor fusion within 3D scenes), making it easy to surface edge cases and refine annotations. This helps teams improve how robots perceive and interact with their surroundings, accurately identify objects, and operate reliably in diverse, real-world conditions. Drones: Drone teams use Encord to manage and curate vast multi-sensor datasets — including 3D LiDAR point clouds (LAS), RGB, thermal, and multispectral imagery. The platform streamlines the identification of edge cases and efficient annotation across long aerial sequences, enabling robust object detection, tracking, and autonomous navigation in diverse environments and weather conditions. With Encord, teams can build and validate advanced drone applications for infrastructure inspection, precision agriculture, construction, and environmental monitoring, all while collaborating at scale and ensuring reliable performance Vision Language Action (VLA): With Encord, teams can connect physical objects to language descriptions, enabling the development of foundation models that interpret and act on complex human commands. This capability is critical for next-generation human-robot interaction, where understanding nuanced instructions is essential. For more information on Encord's Physical AI suite, click here. 

This guide to video annotation tools for logistics breaks down the essentials of turning raw video data into actionable AI insights with real-world use cases, top annotation platforms, and key features that boost accuracy, automation, and operational efficiency. Artificial Intelligence (AI) is transforming the logistics industry by bringing in high levels of efficiency, safety, and operational insight like never seen before. As warehouses, distribution centers, and transportation networks largely depend upon visual data, i.e. from security cameras to drone footage, the need to accurately understand and make use of this data is very important. The logistics industry deals with an enormous amount of video data every single day, capturing crucial activities such as package handling, vehicle movements, inventory stocking, and workforce operations. To truly leverage the power of AI, this raw visual data needs to be transformed into structured insights that AI models can comprehend and analyze. This is where video annotation becomes important. Video annotation involves labeling of video data to identify and track objects, recognize actions, and highlight significant events. This annotated video data is essential for machine learning (ML) models.  The ML model trained on this data enables various tasks in logistics such as real-time asset tracking, predictive maintenance, automated inventory management, and compliance monitoring etc. So, picking the right video annotation tool is a crucial decision. The success of AI applications in logistics heavily relies on the accuracy of annotations, the efficiency of workflows, and how well the tool integrates with existing systems. The best annotation tools not only make the labeling process easier but also ensure high-quality annotations, support collaborative workflow management, and integrate smoothly with current logistics systems. Video annotation example in Encord What Is Video Annotation in Logistics? In logistics setup, video annotation is the process of labeling, tracking, and segmenting objects or events captured in video data. It transforms raw visual data into structured datasets that are suitable for training ML models. Logistics operations have specific annotation requirements, such as accurately tracking multiple moving objects (e.g., packages, vehicles, forklifts), monitoring vehicle flow in busy distribution centers, handling packages efficiently, and precisely detecting significant operational events. For example, a video stream can be annotated to track package handovers from one employee to another, identify loading and unloading sequences at docks, monitor inventory movement on warehouse shelves, and flag anomalies or potential safety hazards in real-time. The ML model trained on such annotated video data optimizes operational processes, enhances security measures, and reduces the human error that ultimately leads to a smarter and more efficient logistics process. Why Video Annotation Matters for Logistics AI In logistics, AI systems depend fundamentally on annotated video data.  To determine how effectively the AI model can perform in a real-world environment, the quality and accuracy of these annotations is very important. High-quality annotations ensure that AI models are trained well and can precisely detect, interpret, and predict critical logistics tasks. Such models lead to significant improvements in the efficiency of the logistics operations. The relationship between annotation quality and AI outcomes in logistics is direct and powerful for the following reasons: Accuracy of Object Detection: AI systems trained on accurately annotated videos can detect and track packages, vehicles, workers, and equipment etc. accurately. Even minor inaccuracies in labeling can result in misidentification or tracking errors, potentially disrupting the entire supply chain operation. Route Optimization: High-quality annotations enable AI models to accurately identify patterns in vehicle movements and identify and track delays, congestion, or inefficiencies in warehouse flows. Accurate annotation ensures the AI can recommend better routes and optimize vehicle scheduling to reduce or remove delays. Event Recognition: Clear and accurate annotation of key events (such as package drop-off, pick-up, delays, or security breaches) allows AI systems to learn and detect anomalies promptly and improve response times and operational reliability. Following are the common AI use cases in logistics that rely on accurate video annotations: Automated Parcel Tracking Annotated video data helps AI track packages throughout sorting centers, warehouses, and delivery vehicles. By accurately annotating each handover or movement, AI models become capable of real-time tracking. This improves tracking of package movements in the logistic workflow. It reduces errors due to manual tracking and ensures timely deliveries. Forklift and Vehicle Navigation Video annotations that involve annotation of navigation routes, vehicle movements, obstacles, and worker paths are important for training AI powered autonomous forklifts and vehicles. Accurately annotated data enables safe navigation, operational efficiency, and smooth interactions between humans and machines. This helps to minimize workplace accidents and streamline the processes. Inventory and Shelf Monitoring Accurately annotated video data of warehouse shelves and storage areas allows AI models to accurately assess inventory levels, spot out-of-stock items, and automatically initiate replenishment workflows. These precise annotations enhance predictions, improve inventory accuracy, and help maintain optimal stock levels. Security and Safety Incident Detection Annotating incidents like unauthorized access, falls, collisions, or protocol breaches helps train AI models that can accurately identify and respond to these events. High-quality annotations ensure that AI systems can quickly alert personnel, helping to prevent accidents, theft, and reduce liabilities. Predictive Maintenance via Video Streams AI models can predict potential failures or maintenance needs before they arise. It requires annotated video data to monitor machinery conditions, vehicle performance, or environmental factors. Accurate annotations of such data is important for differentiating between normal operating conditions and early-stage anomalies. This helps to minimize downtime and lower maintenance costs. Key Features to Look for in a Logistics Video Annotation Tool When choosing a video annotation tool specifically for training AI models for  logistics applications, it is important to understand the unique demands of logistics workflows. The following are the essential features of a video annotation tool that has to be used to prepare dataset for training AI models for logistic applications. Multi-object and Multi-class Tracking In logistics, operational scenes often include multiple objects and classes simultaneously such as parcels, workers, forklifts, delivery vehicles, and various equipment moving dynamically in complex environments. A video annotation tool must: Enable simultaneous annotation and tracking of numerous objects across frames. Maintain consistent tracking IDs to accurately follow each object's trajectory. Provide easy labeling for different classes, clearly distinguishing between object types (e.g., “worker,” “forklift,” “parcel,” etc.). Allow quick review and adjustments to ensure consistent labeling, especially in crowded, fast-paced logistics hubs. Multi-Object Tracking in Encord Frame-accurate Event Tagging In logistics processes various events must be precisely recognized, such as package pickups, deliveries, or anomaly detection like accidental drops or equipment malfunctions. Therefore a video annotation tool should: Allow annotators to pinpoint exact frames where an event occurs. Facilitate precise temporal annotations to clearly define the start and end times of events. Offer visual timelines or video playback control to ease frame-accurate tagging. Event tagging in Encord using Dynamic Attribute Flexible Workflow Management Logistics operations generate large amounts of continuous video data from multiple cameras located at different points within facilities and across transportation routes. The video annotation tool must: Easily manage batch processing of thousands of video files or continuous video streams. Allow customizable workflows to suit different annotation tasks (e.g., simple object labeling versus detailed event annotations). Provide intelligent assignment of tasks, handling simultaneous inputs from multiple annotators seamlessly. Workflow in Encord Collaboration and Quality Assurance (QA) Features Annotation projects often involve distributed teams working remotely or from different locations. Thus, the tool should have strong collaborative capabilities: It should enable real-time collaborative annotation, allowing multiple annotators and reviewers to work concurrently without conflicts. Incorporate role-based access control so administrators can assign tasks, permissions, and project visibility. Include robust QA workflows, offering multi-stage annotation review, feedback loops, and clear audit trails. Provide version control and annotation histories to review edits, corrections, and improve accountability across teams. Scalability A typical logistics annotation project involves annotating potentially thousands of hours of video footage that demand substantial scalability. Therefore the video annotation tool must: Support handling of extremely large-scale datasets without performance degradation. Offer cloud-based scalability to expand annotation resources quickly when data volumes increase. Provide features to automate repetitive workflows. AI-assisted & Automated Annotation AI assistance can accelerate annotation tasks and improve accuracy. It also reduces manual workload for repetitive scenes. COnsidering this, the good video annotation tool should: Incorporate pre-trained models to automate annotation of common objects and scenarios. Enable active learning loops, where annotation feedback continually refines AI suggestions for improved accuracy. Include auto-tracking for objects moving predictably to reduce manual annotation time. AI-Assisted Annotation for All Your Visual Data in Encord Annotation Types  Training AI models for Logistics scenarios often require flexible annotation formats depending on the complexity and precision. Therefore a video annotation too must provide following annotation types: Bounding Boxes: Ideal for simple, consistent tracking of objects like parcels, vehicles, or equipment.  Polygonal Annotations: Essential for detailed shape annotation of irregularly-shaped packages, forklifts, equipment, or loading areas. Polyline Annotations: Useful for marking paths, conveyor belts, forklift trajectories, or route paths within warehouses or sorting centers. Key-point Annotation: Helpful for marking specific parts of objects or equipment (e.g., door handles, package labels, equipment parts for predictive maintenance). Keypoint annotation can also be used to annotate human workers to analyze their pose. Keypoint annotation in Encord Selecting a video annotation tool equipped with these comprehensive features ensures that logistics AI systems receive the precise, reliable, and actionable data needed for training a robust AI model to improve optimal operational performance and strategic advantage. Tools Overview The Best Video Annotation Tools for Logistics Encord Encord is a complete, enterprise-grade data annotation platform specifically built for managing large-scale, complex annotation tasks efficiently. It combines advanced automation, collaboration features, and a highly customizable workflow environment ideal for demanding logistics applications. Multi-object & Multi-class Tracking: Encord excels with robust multi-object tracking, maintaining precise IDs across frames, ideal for complex logistics scenarios involving numerous parcels, vehicles, and workers. Frame-accurate Event Tagging: Encord supports Dynamic Attributes annotations that enable accurate event identification (e.g., parcel handoffs, forklift movements, delivery drop-offs). Flexible Workflow Management: Encord offers workflow customization, task management, and easy integration into logistics systems, handling massive streams efficiently. Collaboration & QA Features: Encord provides strong team management, real-time collaboration, comprehensive review cycles, annotation history/versioning, and detailed QA workflows. Scalability: Encord is highly scalable cloud architecture capable of annotating thousands of hours of logistics footage efficiently. AI-assisted & Automated Annotation: Encord offers AI-assisted labeling, active learning, and auto-tracking capabilities to improve annotation efficiency. Annotation Types: Encord supports bounding boxes, polygons, polylines, keypoints, segmentation masks which are essential for  logistics annotation needs. AI-Assisted Video annotation in Encord CVAT (Computer Vision Annotation Tool) CVAT is an open-source data annotation platform with support for video annotation. It is popular among researchers and businesses due to its flexibility, user-friendly interface, and robust annotation capabilities. It is particularly suitable for teams requiring customizable solutions. Multi-object & Multi-class Tracking: CVAT supports multi-object tracking with interpolation between frames which is  suitable for moderately complex logistics use cases. Frame-accurate Event Tagging: CVAT offers detailed timeline controls for precise event labeling. Flexible Workflow Management: CVAT requires some setup for handling extensive datasets or complex logistics workflows. Collaboration & QA Features: CVAT supports basic collaboration with task assignment and review cycles. However, enterprise-level QA features may require custom setup. Scalability: CVAT has good scalability for medium-sized projects. The large-scale logistics operations might require custom infrastructure tuning. AI-assisted & Automated Annotation: CVAT offers basic auto-labeling via external integrations and support for automatic interpolation between keyframes. Annotation Types: CVAT provides bounding boxes, polygons, polylines, and segmentation masks which are flexible for diverse logistics scenarios. Video annotation in CVAT Supervisely Supervisely is a versatile AI data labeling platform emphasizing collaboration, automation, and ease-of-use. Known for its intuitive interface and robust tracking features, Supervisely is a good choice for teams looking to manage smooth annotation workflows. Multi-object & Multi-class Tracking:  Supervisely supports advanced multi-object tracking and labeling objects across video frames. Frame-accurate Event Tagging: Supervisely provides video timeline annotation tools for precise event marking like parcel deliveries or inventory checks. Flexible Workflow Management: Supervisely  has rich customization options  and workflow automation features for complex logistics operations as well as multiple concurrent projects. Collaboration & QA Features: Supervisely provides strong collaborative environment, task management, real-time annotation review, feedback loops, and QA. Scalability:Supervisely offers scalable cloud-based infrastructure to support large video annotation projects. AI-assisted & Automated Annotation: Supervisely has strong AI-assistance capabilities, including object detection models, tracking automation, and active learning integration. Annotation Types: Supervisely  supports bounding boxes, polygons, keypoints, polylines, segmentation masks. Video Annotation in Supervisely   Kili Technology Kili Technology is an advanced annotation platform emphasizing scalability, automation, and integration capabilities. Built for enterprise deployments, Kili excels in handling complex annotation workflows and integrates seamlessly with AI model training pipelines in logistics environments. Multi-object & Multi-class Tracking:  Kili  supports multi-class labeling effectively and its multi-object tracking capabilities are good for logistics tasks with moderate complexity. Frame-accurate Event Tagging:Kili has precise temporal annotation capabilities for accurately labeling logistics events. Flexible Workflow Management: Kili supports highly customizable workflows and APIs for integration with model training pipelines. Collaboration & QA Features: Kili has extensive collaborative features, including detailed project and task management, annotation reviews, and annotation-quality analytics. Scalability: Kili  is highly scalable and is designed specifically for enterprise-grade annotation tasks for large-scale annotation projects. AI-assisted & Automated Annotation: Kili offers robust auto-labeling, automated annotation via built-in or custom AI models which improves annotation efficiency. Annotation Types: Kili has rich support for bounding boxes, polygons, segmentation masks, and other logistics-relevant annotation formats. Video annotation in Kili Label Studio Label Studio is an open-source, highly customizable annotation tool supporting multiple data formats. It appeals especially to teams seeking flexibility and custom integration options, making it ideal for logistics scenarios with specific workflow needs. Video annotation in Label Studio Multi-object & Multi-class Tracking: Label Studio supports multi-class labeling. However, tracking across video frames requires plugins or additional customization. Frame-accurate Event Tagging: Label Studio has good temporal tagging capabilities. It is especially suitable for simpler event annotations. The highly precise frame-level annotation requires custom setups. Flexible Workflow Management: Label Studio facilitates data labeling workflows by enabling teams to efficiently manage projects, configure interfaces, and leverage machine learning to automate labeling tasks. Collaboration & QA Features: Label Studio has basic collaboration built-in, with task assignment and reviews; advanced QA features need additional configurations. Scalability:  Label Studio's  scalability support is enhanced by its compatibility with technologies like Kubernetes and Helm and integration with storage solutions like Azure Blob Storage (ABS). These integrations enable easy scaling of the Label Studio platform to handle large datasets and annotation tasks. AI-assisted & Automated Annotation:  Label Studio utilizes AI-assisted labeling through its ML Backend integration, enabling interactive pre-annotations. Annotation Types:  Label Studio supports different annotation types, including bounding boxes, polygons, polylines, and keypoints, offering good logistics annotation coverage. Labelbox Labelbox is an industry-leading annotation solution known for its powerful AI-assisted labeling, enterprise-level scalability, and collaboration. Designed to accelerate the annotation lifecycle, Labelbox suits logistics enterprises aiming for efficiency, scalability, and accuracy. Video annotation in Labelbox Multi-object & Multi-class Tracking: Labelbox provides robust multi-object and multi-class annotation, supporting effective tracking of logistics-specific scenarios like warehouse movements. Frame-accurate Event Tagging:  Labelbox has precise event annotations with detailed frame-level tagging tools, ideal for logistics event recognition tasks. Flexible Workflow Management: Labelbox is highly flexible with customizable workflows, suitable for integration with complex operational data pipelines. Collaboration & QA Features:  Labelbox provides excellent team collaboration, version control, annotation reviews, and detailed QA processes, aligning with logistics operation needs. Scalability:  Labelbox has a strong scalability feature capable of handling extensive logistics annotation projects without performance issues. AI-assisted & Automated Annotation:  Comprehensive AI-powered annotation and tracking tools significantly improving annotation speed and accuracy. Annotation Types:  Offers a wide range of annotation types (bounding boxes, polygons, polylines, keypoints, segmentation masks) fully compatible with logistics use cases. Labellerr Labellerr is a user-friendly, accessible annotation platform designed to simplify the labeling process for medium-scale data projects. Its straightforward interface and moderate AI-assistance make it suitable for smaller to mid-size logistics operations. Video annotation in Labellerr  Multi-object & Multi-class Tracking:  Labellerr provides solid multi-class labeling support for complex scenes as well as multi-object tracking effective for simpler scenarios. Frame-accurate Event Tagging: Labellerr has good support for temporal tagging, suitable for clearly defined logistics events. It may lack advanced timeline controls for very complex scenarios. Flexible Workflow Management:  Labellerr supports workflow management for mid-scale logistics applications; might need additional configuration for very complex pipelines. Collaboration & QA Features:  Labellerr provides basic to moderate collaboration and QA features, suitable for small to medium-sized annotation projects. Scalability:  Labellerr is scalable for medium-sized logistics operations. It may require additional infrastructure for large-scale datasets. AI-assisted & Automated Annotation:  Labellerr supports AI assisted annotation to speedup data annotation tasks.. Annotation Types:  Labellerr Supports essential annotation types (bounding boxes, polygons, keypoints) suitable for a range of logistics annotation needs. Video Annotation for Logistics: Key Takeaways This blog highlights how video annotation plays a crucial role in building effective AI systems for logistics operations. Here are the core insights from this blog: Video Annotation Transforms Visual Data into Insights: In logistics, large volumes of video data from cameras are collected daily. Annotating this data is essential for training machine learning (ML) models that enable automation, tracking, and operational intelligence. Logistics-Specific Use Cases Require Precision: Applications such as real-time parcel tracking, forklift navigation, inventory monitoring, security incident detection, and predictive maintenance all rely on high-quality, frame-accurate video annotations. Annotation Quality Impacts AI Performance: Accurate annotations directly influence object detection, event recognition, and route optimization, enabling AI to make precise predictions and reduce errors in logistics workflows. Choosing the right annotation tool is essential for building accurate and scalable logistics AI systems, and the tools listed offer various strengths tailored to different logistics needs.

This guide to data annotation tools for Generative AI breaks down how teams can improve model accuracy and align LLMs with human values. It also explains how to scale AI projects with the right platforms and workflows. Today, around 72% of companies use Gen-AI in at least one business function. This number is almost triple the share just three years ago. However, over half of artificial intelligence (AI) initiatives never reach production. Many Gen-AI pilots fail due to incomplete, biased, or poorly labeled data. AI teams need structured feedback loops like Reinforcement Learning from Human Feedback (RLHF) to train safe, high-performing models. They also require specialized data-annotation platforms like Encord, which can manage multimodal data, annotation at scale, and automated quality checks.  This article explains what data annotation means and outlines the six must-have features of a modern annotation platform. We will also compare the best data annotation tools for Generative AI. What Is Data Annotation in the Context of Generative AI? Data annotation means adding human-readable labels to raw text, images, audio, video, or documents so a model can learn from them. In generative AI, the model's quality, safety, and ethics depend on how people label “what’s in the data” and “which output is better.”  Unlike traditional supervised learning, which uses labels to classify one correct category. Gen-AI annotation reflects more complex human judgment. It is about encoding human preferences, safety rules, and multimodal context to teach models how to think, not just what to see. Why High-Quality Annotation Determines Gen AI Success Quality data annotation drives the success of generative AI projects. Accurate, diverse datasets ensure AI models deliver reliable, safe outputs. Models can generate hallucinations, biases, or irrelevant results without precise data labeling, undermining their effectiveness. Accurate annotation offers the following benefits: Alignment & RLHF: Human preference labels guide LLMs and multimodal AI systems toward helpfulness and safety. These labels help AI experts to fine-tune model performance, ensuring their outputs match human values in diverse use cases. They also let teams develop and ship reliable AI models faster. Bias control: High-quality labeled datasets prevent harmful or skewed outputs. Unbiased annotation processes categorize data types to reduce risks of bias and keep the labeling process fair and traceable for teams. Model generalization: Without quality-labeled training datasets, hallucination rates increase, and models may struggle with generalization. This occurs when LLMs face rare prompts and multimodal models need fine-grained object detection and pixel-level semantic segmentation. Annotation Challenges in Generative AI Generative AI projects require robust data annotation, but several challenges complicate the process. Addressing them can help build high-quality datasets for AI models. Scale & Velocity: LLMs and multimodal AI models consume terabyte-class datasets. Manual data labeling cannot keep pace, causing pipelines to stall and model updates to lag. Teams need automation and batch workflows that stream high-volume, real-time input through a single data annotation platform. Multimodal Complexity: Modern use cases mix text, images, video, audio, LiDAR, and PDFs. Each data type requires different annotation types. Managing different editors or file formats encourages version drift and slow project management. Quality Assurance: Ensuring quality data is tough when annotation errors occur. Labeled datasets can degrade without rigorous quality control, causing poor model performance. Human-in-the-loop workflows and active learning help maintain accuracy by flagging issues in real-time. Security & Compliance: Annotated medical scans, chat logs, and financial docs often contain Personally Identifiable Information (PII) and Protected Health Information (PHI). GDPR, HIPAA, and SOC 2 rules demand encrypted storage, audit trails, and on-premise deployment options. Cost Pressure: RLHF, red-teaming, and human-in-the-loop review can incur significant costs. Without AI-assisted labeling and usage-based pricing, annotation costs can quickly escalate, draining resources before AI applications reach production. Key Features to Look For in an AI Annotation Tool Given the challenges in data annotation, we must be cautious when selecting a platform. The best annotation tools streamline workflows, improve scalability, and ensure model performance in diverse AI applications. Below are some features to prioritize when choosing an annotation tool. RLHF Support: Look for platforms that support RLHF. It enables annotators to rank outputs, score safety, and generate reward signals for fine-tuning LLMs more efficiently. Multimodal Editors: Modern AI systems combine different data formats. A strong platform handles all data types, from bounding boxes and polygons in image annotation to pixel-level semantic segmentation. It also supports text annotation for natural language processing (NLP) and 3-D point-cloud labels for autonomous driving. AI-assisted Labeling & Active Learning: Look for the tool that supports AI-powered annotation to predict labels, auto-draw boxes, or suggest classes, so human annotators focus on edge cases. This automation cuts costs on large datasets while boosting scalability. Collaboration & Quality Control: High-quality data requires reviewer consensus and real-time metrics dashboards. Look for task routing, comment threads, and role-based permissions that help data scientists, domain experts, and QA stay aligned. Secure Infrastructure: Data security is non-negotiable. Platforms must meet SOC-2 and GDPR standards, providing on-premise or cloud-based options to protect sensitive AI data, especially in regulated fields like healthcare. SDK / API & Cloud Integrations: Scalable tools provide APIs and SDKs for seamless integration with model pipelines. This helps in automation, supports Python-based workflows, and streamlines data management for end-to-end model training. Tools Overview Best Data Annotation Tools for Generative AI Many annotation platforms now bundle multimodal editors, RLHF workflows, and active-learning automation so you can push large datasets through a single, secure pipeline. Below, we cover the best annotation tools that address the unique demands of data for generative AI. Encord – Multimodal Data Platform Built for RLHF Encord is a multimodal labeling tool that unifies text, image, video, audio, and native DICOM within one data annotation platform. This lets AI teams label their data all in a shared, user-friendly interface. Analyze and annotate multimodal data in one view Encord Image Annotation Encord’s image toolkit lets you draw bounding boxes, polygons, keypoints, or pixel-level semantic-segmentation masks in the same editor. It uses model-in-the-loop suggestions from Meta-AI’s SAM-2 to automate the labeling. Auto-labeling reduces the annotation time by roughly 70% on large datasets while maintaining 99% accuracy. Every label is saved, so active-learning loops in Encord Active can flag drift or low-quality labels before they get used in training data. Image annotation using Encord Encord Video Annotation Encord streams footage at native frame rates for video pipelines. It then applies smart interpolation to propagate labels forward and backward. This process means you do not need to label each frame by hand, yielding 6 times faster labeling throughput.  Built-in advanced features include multi-object tracking, scene-level metadata, and automated pre-labeling to maintain high quality for gen AI training data. Meanwhile, background pre-computations allow annotators to scrub long clips without latency spikes.  Video annotation using Encord Encord Text Annotation On the NLP side, Encord supports annotations such as entity, intent, sentiment, and free-form span tagging. More importantly, it adds preference-ranking templates for RLHF so teams can vote on which LLM response is safer or more helpful.  Encord text annotation integrates SOTA models such as GPT4o and Gemini Pro 1.5 into annotation workflows. This integration speeds up document annotation processes, improving the accuracy of text training data for LLMs.  Text annotation using Encord Encord Audio Annotation Encord’s audio module lets you slice, label, and classify waveforms for speech recognition, speaker diarization, and sound-event detection. Its AI-assisted labeling uses models like OpenAI Whisper to pre-label audio data, pauses, and speaker identities, reducing manual effort.  Paired with foundation models such as Google’s AudioLM, it accelerates audio curation. This allows a faster feed of high-quality clips into generative pipelines. Audio annotation using Encord Learn how to automate data labeling Scale AI – Generative-AI Data Engine Scale AI offers a comprehensive Generative-AI Data Engine that supports end-to-end workflows for building and refining large language models (LLMs) and other generative AI systems. The platform includes tools for RLHF, synthetic data generation, and red teaming, essential for aligning models with human values and ensuring safety.  Its synthetic-data module generates millions of language or vision examples on demand.  This helps improve the detection of the rare class for object detection or multilingual NLP.  Scale AI’s expertise in combining AI-based techniques with human-in-the-loop annotation allows for high-quality, scalable data labeling. This approach meets the demands of complex generative AI projects.  Scale AI synthetic data Kili Technology – Hybrid Human-Plus-AI Labeling Kili Technology combines human expertise with AI pre-labeling to achieve a balance of speed and accuracy that suits Gen AI’s demanding annotation tasks. It supports various data types, including text, images, video, and PDFs, and provides customizable annotation tasks optimized for quality.  A key feature is the use of foundation models like ChatGPT and SAM for AI-assisted pre-labeling, which accelerates the annotation process. Kili Technology also emphasizes collaboration with machine learning experts.  It provides tools for quality control, ensuring that the annotated data meets the high standards required for generative AI. Its flexible on-premise deployment options cater to industries like finance and defense, where data security is critical. Model-based labeling in Kili Appen Appen is a leading provider of data annotation services, offering high-quality datasets for training generative AI models. It supports a vast, vetted workforce that delivers richly annotated data across text, image, audio, and video modalities.  Appen's workforce ensures multilingual support, reducing cultural bias in NLP outputs. It also offers differential privacy options to protect personal data.  Additionally, Appen provides pre-labeled datasets and custom data collection services, tailored to specific use cases in generative AI, such as sentiment analysis and content moderation. Multimodal data annotation in Appen Dataloop – RLHF Studio & Feedback Loops Dataloop provides an enterprise-grade AI development platform with robust data annotation tools for generative AI. Dataloop’s RLHF studio enables prompt engineering. This allows annotators to offer their feedback on model-generated responses to prompts. It supports various data types, including images, video, audio, text, and LiDAR, and offers drag-and-drop data pipelines for efficient data management.  Dataloop integrates with multiple cloud services and offers a marketplace for models and datasets. This makes it a comprehensive solution for generative AI projects. Its Python SDK allows for programmatic control of annotation workflows, enhancing automation and scalability. Dataloop AI data annotation Amazon SageMaker Ground Truth Plus Amazon SageMaker Ground Truth Plus data labeling service supports the creation of high-quality training datasets for generative AI applications. It supports customizable templates for LLM safety reviews, dialogue ranking, and multimodal scoring. Tight identity and access management (IAM) and VPC peering ensure your data remains secure within your cloud environment. When labeled, assets automatically fill up in S3. This starts SageMaker processes for retraining models or checking for bias. The system uses active learning to reassess low-confidence labels, and metrics dashboards display accuracy and recall rates. Amazon SageMaker ground truth image annotation Which is the Best Data Annotation Tool for Generative AI? Among the platforms we covered above, Encord stands out for turning complex, multi-step Gen-AI annotation workflows into a single, secure workspace. Its support for multimodal data annotation within a single platform makes it a better choice for teams working on generative AI projects. It also eliminates the need for multiple tools and reduces workflow complexity. Encord's integration of RLHF workflows enables teams to compare and rank outputs from generative AI models and align them with ethical and practical standards. Whether it’s improving model behavior or meeting compliance needs, RLHF makes Encord a standout choice. Encord supports seamless cloud integration with major cloud storage providers such as AWS S3, Azure Blob Storage, and Google Cloud Storage. This allows teams to efficiently manage and annotate large datasets directly from their preferred cloud environments. Encord's developer-friendly API and SDK enable programmatic access to projects, datasets, and labels. This facilitates seamless integration into machine learning model pipelines and enhances automation. Encord SDK Moreover, Security is another area where Encord is a better choice. It is SOC2, HIPAA, and GDPR compliant, offering robust security and encryption standards to protect sensitive and confidential data. Learn how to improving data quality using end-to-end data pre-processing techniques in Encord Active Final Thoughts Data annotation tools are vital for building a generative AI application. They help create high-quality datasets that power models capable of producing human-like text, images, and more. These tools must manage large datasets and diverse data types to ensure AI outputs are reliable and aligned with human expectations.  Below are key points to remember when selecting and using data annotation tools for generative AI projects. Best Use Cases for Data Annotation Tools: The best data annotation tools excel at preference ranking, training models with human feedback, red-teaming models with challenge inputs, and enhancing model transparency. These functions are essential for developing safe, effective, and interpretable generative AI systems. Challenges in Data Annotation: Generative AI annotation comes with difficulties such as rapidly managing large-scale datasets, processing multimodal data, maintaining consistent data quality over time, ensuring security and regulatory compliance, and controlling costs. Addressing these challenges is essential for successful AI model deployment. Encord for Generative AI: Encord features a multimodal editor, RLHF support, and secure AI-assisted workflows. Other tools such as Scale AI, Labelbox, Kili, Appen, Dataloop, and SageMaker also provide strong capabilities. The best choice depends on your data types, project scale, and workflow needs.

This Guide to the Best Video Annotation Tools for Robotics explains about what is video annotation, and essential features and popular video annotation tools that help turn raw video streams into meaningful ground-truth datasets fueling high-precision robotic-vision models. In the fast-growing field of robotics, processing video data and generating insights from it is crucial. A robotic system often uses live camera feeds to see and understand things around it. Therefore, a robotic system must be properly trained using the live video data so that it can understand its surroundings better. This training enables the robot to perceive and navigate the world effectively. The training that allows the robotic system to understand its environment, relies on high-quality video annotation. The video annotation involves labeling objects, people, and actions in  a video that act as ground truth data for training machine learning models. The machine learning models use this data to learn about their environment. Video Annotation in Encord Therefore, video annotation is essential for training ML models that help robotic systems to understand its environment and perform actions. The video annotation provides well-labeled examples that give robots their “eyes,” enabling capabilities such as self-driving cars, surveillance drones, activity recognition systems, and more. Without properly annotated videos, even the most advanced ML models would struggle with basic perception tasks. High-quality annotations have a real impact; for instance, precise video labeling can directly improve the safety and efficiency of industrial robots by enabling more accurate object recognition. Defining Robotics in the AI World Robotics is a branch of engineering and computer science that focuses on creating, building, and using robots. Robots are machines that perform complex tasks automatically or semi-automatically.  In the earlier days Robots used to perform repetitive, rule-based tasks. Robotics is now being combined with Artificial Intelligence (AI). Today, AI enabled robots are capable of perceiving, reasoning, learning, and adapting to dynamic environments. Because of the use of AI, robots are now evolving towards truly intelligent machines. Figure Robot, powered by AI, capable of performing human like task How Does AI Improve Robotics? AI plays an important role in Robotics. AI gives robots "smart" abilities with the help of Machine learning (ML) trained on annotated datasets that act as a brain to these machines. These ML models are used to enable various capabilities such as: Mobile robots in a warehouse Allow Robots to sense their surroundings: It Helps robots to detect and identify objects, environments, or people. For example, a robot arm locating a component on an assembly line. Allow Robots to understand and respond to humans: With Natural Language Processing (NLP), robots can understand humans and communicate with them. For example, voice-controlled home assistants or healthcare bots understand user queries and answer accordingly. Allow Robots to learn from experience: Reinforcement learning enables Robots to learn optimal strategies through trial and error. For example,  a robotic dog learning to climb stairs. What is Video Annotation in the Context of Robotics? Video annotation is the process of labeling key elements (e.g. objects, actions, or regions of interest) in a video stream so that machine learning models can learn to interpret and understand the visual world. In the field of robotics, this process is foundational for enabling perception, decision-making, and autonomy. Why Does Video Annotation Matter in Robotics? Robots depend on visual data from cameras to "see" and "understand" their environment. However, raw video data is just a piece of pixels until it is transformed into structured data that robots can learn from. Video annotation provides that necessary structure, turning unlabelled data into rich training datasets by identifying what is in the frame, where it is, and what is happening. Without annotated data, AI models can not be trained to recognize objects or comprehend what is going on in a video stream, making video annotation a vital step in creating intelligent robotic systems. When there is no annotated data, AI models cannot be trained to recognize objects or understand things in a video stream, making video annotation an essential stage of developing intelligent robotic systems.  How Video Annotation Transforms Raw Footage into Actionable Data? There are various types of annotation used in robotic applications: Object Detection (Bounding Boxes): This involves drawing rectangular boxes around significant objects (like humans, animals, or items) in each frame and assigning labels. It helps robots detect and respond appropriately. For instance, in a robotic arm on an assembly line, bounding boxes label different components (like screws, chips, or PCBs) so the robot can identify and pick the right part. Object Tracking: This technique assigns persistent IDs to objects as they move across frames, allowing robots to understand motion and behavior over time. For example, in autonomous delivery robots, object tracking helps follow the movement of pedestrians or vehicles, enabling the robot to predict motion and avoid collisions. Semantic Segmentation (Pixel-Level Labels): Here, each pixel in the frame is labeled according to its class (like road, sidewalk, pedestrian, or obstacle). This provides ultra-fine perception, allowing robots to navigate their environments with greater accuracy. Imagine training a warehouse robot to recognize and pick fragile items. Annotated video data helps it learn the visual differences between a glass bottle and a plastic bottle. The robot tracks their positions as a conveyor belt moves, and manipulates them delicately using a gripper. Without labeled video data, the robot would not understand what is there and how to handle or act. Key Features in a Video Annotation Tool for Robotics Selecting a video annotation tool with features specifically aligned with robotics applications is important to build effective AI robotic systems. Here are essential features that is must for annotation tools to meet the demanding requirements of robotic systems: Multi-Frame Object Tracking Object tracking feature enables annotators to identify an object in one frame and track it across subsequent frames. By maintaining continuity in annotations, it minimizes manual work and guarantees both consistency and accuracy when labeling moving objects. For example, an autonomous drone that monitors vehicles on a bustling highway relies on multi-frame object tracking to reliably predict vehicle paths, ensuring safe navigation. 3D Annotation Support 3D annotation is all about tagging objects with depth information and spatial coordinates (X, Y, Z). This often involves using LiDAR data or stereo camera footage, which helps create annotations that accurately reflect the real-world positions and sizes of objects. Take autonomous vehicles, for example, annotators use 3D bounding boxes around pedestrians, cars, and traffic signs to provide the vehicle with a precise understanding of distances, which is vital for safe driving in traffic. Automation & AI Assistance Automation and AI assistance in annotation tools uses machine learning algorithms to predict annotations that significantly speed up the labeling process. Human annotators then verify, correct, and refine these AI-generated annotations, enhancing workflow efficiency and ensuring high accuracy. For example, when training a robot for picking items in a warehouse, AI-assisted annotation can automatically create bounding boxes around known products. Human annotators can quickly check or modify these annotations, which greatly accelerates the training process. Collaboration Features Annotation in robotics often requires teamwork where multiple annotators are working on the same dataset. Collaboration features like shared annotation projects, commenting, version control, task assignments, and real-time updates are essential for keeping everyone on the same page and enhancing productivity. Imagine a team of annotators working together on a dataset for autonomous vehicles. They use built-in communication tools and task management features to quickly clear up any uncertainties, making sure that the labeling of complex traffic situations stays consistent. Integration Capabilities Data integration refers to how well an annotation tool can connect with existing data pipelines, machine learning workflows, cloud storage solutions, and model training frameworks. Smooth integration simplifies processes, enhances data accessibility, and accelerates the development of AI models. For example, a robotics team might link their annotation tool directly with TensorFlow or PyTorch, allowing labeled datasets to flow seamlessly into their model training pipelines. This setup enables them to iterate and deploy robotic vision models more efficiently. Scalability Scalability is all about the ability of a tool to manage larger datasets and more complex annotation needs without losing performance or usability. Scalable tools keep things running smoothly, are easy to use, and organize data effectively, even as projects expand significantly. Take, for example, an autonomous robotics startup that starts with a small pilot dataset and then quickly ramps up to millions of frames for deployment. A scalable annotation tool can handle this growth with ease, allowing the team to maintain both quality and productivity. Overview of Top Tools Top Video Annotation Tools for Robotics Selecting the right video annotation tool is important for developing a high performance robotic system that makes use of AI. Robots rely extensively on accurately annotated visual data for various tasks. The choice of annotation tool directly impacts model accuracy and real-world applicability. In this detailed comparison, we evaluate the top video annotation platforms against essential criteria including multi-frame tracking, 3D annotation, automation capabilities, collaboration, integration flexibility, and scalability. Encord Encord is an all-in-one data annotation and management platform designed for advanced annotation tasks including robotics. It handles complex, multimodal datasets and provides a comprehensive environment for teams that require high precision and sophisticated annotation methods. Video Annotation in Encord Multi-Frame Object Tracking: Encord makes it easy to track moving objects across frames  in a video stream and enhances annotation efficiency when it comes to video annotation. 3D Annotation Support: With strong support for 3D annotation, Encord is essential for achieving accurate robotic perception. Automation & AI Assistance: Thanks to its advanced AI-driven automation, Encord dramatically cuts down on the manual annotation workload. Collaboration Features: Encord enables real-time collaboration, complete with user management, feedback loops, and a detailed version history. Integration Capabilities: Encord integrates smoothly into existing ML pipelines, cloud storage solutions, and third-party platforms. Scalability: Encord is built to scale effortlessly, whether you're working on small experimental projects or tackling large enterprise-level annotation tasks. CVAT (Computer Vision Annotation Tool) CVAT is a widely-used, open-source annotation tool. It is known for its flexibility and customizability. It is popular among robotics teams requiring specific workflows and direct control over their annotation processes. Tracking harvester movement across farms in CVAT Multi-Frame Object Tracking: CVAT supports an efficient multi frame object tracking feature across video sequences which is essential for consistent annotation. 3D Annotation Support: CVAT provides good support for 3D annotations (e.g., LiDAR and point clouds), though customization or plugins may be needed. Automation & AI Assistance: CVAT supports automation including auto-annotation and integration with external AI models. Collaboration Features: CVAT has team collaboration functionalities allowing team members to work on annotation projects. Integration Capabilities: CVAT is highly customizable. It can be easily integrated via APIs, supporting a broad range of workflows. Scalability: CVAT is scalable to large-scale projects, though may require additional engineering resources for extensive use. keylabs.ai Keylabs.ai is simple and easy to use and good for robotics teams that want to annotate data quickly with minimal setup. It has good performance with a balanced set of features. Video annotation in keylabs.ai Multi-Frame Object Tracking: keylabs.ai includes object tracking capabilities suitable for a variety of robotic applications. 3D Annotation Support: It also supports 3D annotation, including LiDAR and point cloud data. Automation & AI Assistance: With AI assisted annotation, keylabs.ai streamlines the process.. Collaboration Features: The platform is built for collaboration, making it ideal for small to medium-sized teams. Integration Capabilities: keylabs.ai integrates with popular ML frameworks. Scalability: It is well-suited for mid-sized annotation projects, though it may not be the best fit for very large enterprise workflows. Supervisely Supervisely is known for its full-fledged, developer-friendly environment for robotics teams that need advanced customization, many integrations and complex annotation workflows. Supervisely Video Annotation Toolbox 3.0 Multi-Frame Object Tracking: Supervisely tracks objects across frames and helps to annotate moving objects precisely. 3D Annotation Support: Supervisely is good at annotating complex 3D data types for detailed robotic tasks. Automation & AI Assistance: Supervisely has full automation and AI-driven labeling assistance. Collaboration Features: Supervisely has robust collaboration and task management. Integration Capabilities: Supervisely integrates with many ML tools, cloud storage and data platforms. Scalability: Supervisely is designed for large-scale robotic annotation projects. Kili Technology Kili Technology serves enterprise-level annotation requirements by integrating advanced annotation methods with solid project management. It is especially well-suited for teams that emphasize quality assurance and structured annotation workflows. Video Annotation in Kili Technology Multi-Frame Object Tracking: Kili provides reliable tracking features for consistent video annotation. 3D Annotation Support: Kili offers 3D annotation capabilities but with some limitations compared to top-tier platforms. Automation & AI Assistance: Kili has a strong AI-driven labeling assistance tool that speeds up annotation productivity. Collaboration Features: Kili features comprehensive collaboration and management tools that are suitable for larger teams. Integration Capabilities: Kili enables effective integration into enterprise workflows and machine learning environments. Scalability: Kili is designed to scale efficiently to extensive datasets and team sizes. basic.ai Basic.ai has simple and fast annotation tools for robotics teams that need to deploy fast and easy to use. It’s perfect for teams that need basic annotation features without setup. Video Annotation in Basic.ai Multi-Frame Object Tracking: basic.ai has good multi-frame annotation. 3D Annotation Support: basic.ai has basic 3D annotation. Automation & AI Assistance: basic.ai has AI assisted annotation automation. Collaboration Features: basic.ai has basic collaboration. Integration Capabilities: basic.ai integrates with common ML and data workflows. Scalability: basic.ai is good for small to medium annotation workloads. When choosing a video annotation tool for robotics multimodal data handling, collaboration needs, integration flexibility and scalability parameters should be considered. Encord and Supervisely are great for multimodal data, advanced tracking and enterprise features. CVAT is good for custom and open-source environments, while keylabs.ai, Kili Technology and basic.ai are good for teams of all sizes and project complexity. Encord is the best of these, it’s a full data management platform with additional features like advanced automation and integration. Key Takeaways Video annotation is crucial for training AI powered robots. It involves labeling objects, actions, and areas in video footage, which is essential for developing machine learning and computer vision models that enable machines to "see" and understand their environments. Here are the key takeaways from this blog: Video annotation helps robots learn: Labeling videos teach robots to recognize objects and understand actions, making them smarter and safer. AI enhances robot intelligence: With AI, robots can now detect, learn, and make decisions rather than just following preset rules. Robotics requires specialized annotation features: Annotation tools need to support capabilities like tracking moving objects, 3D labeling, and automation to be effective in robotics. Choosing the right tool is vital: The quality of the annotation tool directly impacts the development of a robust ML model, which in turn affects the robot’s performance in real-world environments. Encord is a top choice: Encord stands out with its strong automation, collaboration features, video annotation support, and seamless integration with AI workflows, making it an excellent option for robotics applications.

TLDR; With recent questions around the alignment of recent models like GPT-4.1, we introduce the concept of distributed alignment, where organizations beyond frontier labs assume responsibility for alignment of AI systems through robust monitoring and control systems.   In the summer of 1859, Edwin Drake had a dream. He saw an opportunity that he thought others had missed: using techniques from salt well drilling to extract a different substance from the earth - oil. He had invested thousands of dollars into a machine to drill and was now testing it out near Titusville, Pennsylvania. It was not going well.  For months, he has driven iron pipes through layers of stubborn gravel, fighting collapsed walls and bedrock, progressing at an excruciating three feet per day. Local farm hands had taken to calling his operation "Drake's Folly" and his financial backers had already abandoned him, leaving him to resort to taking out loans to stay afloat. Drake was on the brink of failure.  But on arrival to work the morning of August 28, one of Drake's drillers peered down and witnessed something miraculous: crude oil, rising up the shaft. As Drake rushed to the site, they began pumping the mysterious black liquid into an old bathtub – a humble vessel for what would become the lifeblood of the industrial age.  Besides being emblematic of many future startups’ struggles against impossible odds in the face of ridicule and doubt, this moment would unleash forces that, for better or worse, would reshape power dynamics in society and ultimately fuel the next century of technological progress. Before Drake's innovation, petroleum was a niche product, and whale oil lit the lamps of America. Within decades, petroleum would power the second industrial revolution, enabling unprecedented mobility, manufacturing, and economic growth. A person born at the time of Drake’s discovery would have started their life in a world of horse-drawn carriages and candlelit rooms and ended it in an era of space travel and nuclear power.  Today we stand at a similar inflection point. The culmination of decades of innovation in model architectures, compute infrastructures, frontier data processes, and human feedback combined with the accessible, easy-to-use form factor of a humble chatbot has opened the door to a renaissance in Artificial Intelligence. Just as Drake's drill struck oil, we have struck a new kind of intelligence well. 2022 was the clear demarcating line for this wellspring with the release, and subsequent rapid adoption, of ChatGPT. In the time since, thousands of new startups have popped up to capture the rivulets of this intelligence well. It is difficult to throw a rock in San Francisco today without hitting an AI founder. The previous technological paradigm,the Big Data Era, saw the rise of a common cliche - data is the new oil.  We are witnessing a new regime change, a renaissance in real-time, which is propelling the creation of a much more potent commodity - intelligence. We are entering the Intelligence Age where intelligence is the new oil. The Bottleneck Yet, despite all this progress, the economy remains ill-equipped for the mass adjustment to AI technology. According to Gartner, 85% of AI projects ultimately fail to deliver on their promises, with McKinsey reporting that only 10% of organizations achieve significant financial benefits from AI investments. In a world of abundant intelligence why are there these gaps?  When John D. Rockefeller founded Standard Oil in 1870 (over a decade after Drake’s discovery), the automobile was still decades away from mass production. Most of oil's eventual applications were undiscovered, existing only as unrealized potential. Yet Rockefeller still understood something fundamental: the future belonged to those who could not just extract oil but refine and distribute it effectively. Standard Oil's dominance came not just from controlling oil wells, but from mastering both the pipeline of distribution to end users and the complex process of turning crude oil into useful products. We are missing similar ingredients now. While hundreds of LLM wrapper startups have emerged as preliminary distribution vessels for this raw intelligence, we are still only beginning to develop a more robust infrastructure that also incorporates effective guardrails.  We have the raw intelligence, but we are still only developing the infrastructure and distribution for refining this intelligence into economic and social utility with minimal harm. The missing key is a concept that is not new in AI parlance but is often misunderstood and underestimated. To make this new form of intelligence truly useful and effective, we must solve the fundamental alignment problem.  Infinite, Infinitely Intelligent Interns  In its simplest terms, alignment ensures AI systems reliably pursue and achieve intended objectives while respecting human values and safety constraints. While often portrayed as an abstract concept relevant only to future scenarios of potential conflict between humanity and Artificial General Intelligence(AGI), alignment is fundamentally about something more immediate and practical. It is about ensuring AI systems, at any level of capability, are trustworthy, reliable, and genuinely useful for their intended purposes. Just as a well can produce unrefined crude oil that's too contaminated for practical use, an unaligned AI system – regardless of its raw intelligence – can be unreliable, unpredictable, or even counterproductive. To understand alignment's practical importance, consider AI's promise: providing every organization, business, and individual access to what amounts to a (functionally) infinite number of (functionally) infinitely intelligent interns. Even considering just one such intern reveals critical challenges. Would any organization hire a highly intelligence intern who also: Gets your coffee correct 99% of the time but occasionally serves you battery acid by mistake Confidently presents incorrect information 15% of the time, not from malice but from sincere misunderstanding Asked to schedule a meeting with a busy executive, orchestrate a company-wide fire drill to ensure the executive is free at the desired time May secretly be plotting your demise No organization would hire such a person despite any level of intelligence. Raw intelligence alone is insufficient—it must be refined through alignment to become trustworthy and useful. In more formal terms, alignment is typically considered across a spectrum of dimensions: Technical Alignment: Ensuring accuracy and reliability while minimizing hallucinations and errors.  Value Alignment: Guaranteeing that AI systems understand and pursue intended goals while respecting human values and preferences.  Robustness: Maintaining consistent and reliable behavior across diverse contexts and previously unseen scenarios. Safety: Preventing harmful outcomes through comprehensive safeguards against misuse, manipulation, and unintended consequences. A couple of years ago, I wrote about the POC to production gap in AI. I highlighted many of the same concerns that (now) legacy AI systems had in bridging the gap to meet production-level standards. The evolution of AI capabilities in the subsequent years has only broadened those concerns. Two years ago, technical alignment and robustness dominated the conversation. Today, as AI systems approach or exceed human performance in many domains, value alignment and safety have become equally critical. We're discovering that no matter their performance capabilities, humans have specific preferences for AI behavior and (surprise, surprise) would also prefer not to be harmed in the process of using AI. And as AI systems get better and better, alignment could be the most important problem we must solve. The Imperatives of Alignment Alignment is often seen in a context where it is motivated by resolving potential existential risk from AGI. Some of the field's leading researchers have published sobering estimates of what they call “P(Doom)” - the probability that advanced AI systems could lead to human extinction. Anthropic's Paul Christiano suggests this probability could be as high as 35%, while Deepmind's Stuart Armstrong places it at 40%.  Moving down the probability of catastrophic doom for humanity is perhaps the highest moral imperative you can imagine. However, this framing may also do us a disservice. Focusing exclusively on the long-tailed risk misalignment poses to humanity risks overlooking the immediate, concrete challenges that demand our attention today. It may also, paradoxically, hinder our progress towards lowering P(Doom).  An oil refiner in Drake’s time couldn't have conceptualized the complex dynamics of climate change or foreseen its global implications. Yet they certainly understood the immediate dangers of toxic fumes, industrial accidents, and local environmental damage. Had the early petroleum industry developed more rigorous safety protocols and pollution controls to address these immediate concerns, these practices might have naturally evolved into stronger environmental safeguards. It is not inconceivable that the compounding effects of early safety cultures and responsible industrial practices could have naturally altered the trajectory of climate change.  Similarly, by solving today's tangible alignment challenges, we build the foundation for addressing more profound future risks. We should thus consider the immediate social imperatives for alignment. As AI systems become increasingly embedded in the fabric of society – making decisions in healthcare, criminal justice, education, and social services – misaligned systems could amplify existing issues and create new forms of social harm. These include areas such as: Information Ecosystem Pollution: As models become more sophisticated, misaligned systems could flood our information and media ecosystem with deepfakes and plausible-sounding but subtly misleading content, making it increasingly difficult for humans to distinguish truth from machine-generated fiction. Erosion of Social Trust: With the above pollution, it might be increasingly difficult for humans to trust any information or interaction Homogenization: Unaligned AI systems, in their drive for efficiency and optimization, might inadvertently homogenize human expressions, language usage, and artistic creation. Soon everything will sound like it’s from a machine.  These and other social considerations are hugely important risks and challenges to get right in our transition to an AI-powered society. However, while we often think of the potential downsides of AI, we should equally consider the upsides of getting AI right. There is also a strong economic imperative for alignment which is often overlooked.  Quantifying alignment's economic impact presents a challenge similar to measuring energy's contribution to GDP – it's so fundamental that it becomes difficult to isolate. Just as the difference between refined and unrefined oil marked the difference between transformative energy and mere combustible liquid, alignment determines whether AI becomes a transformative technology or remains a fascinating but limited tool.  Alignment dictates how we use AI. The application of a technology inherently governs its eventual economic impact. For instance, instead of incorporating oil into economic processes, we could have just extracted it and used it to create a massive bonfire. How much GDP would that have produced?  In his essay Machines of Loving Grace, Dario Amodei argues for many of the long-term benefits of AI. He foresees a world where AI has the potential to be the dominant force in economic life and, with that, lift billions in the world out of poverty. Even Bernie Sanders, far from what you would call a “tech bro, " recognizes AI's potential in improving economic reality. In an interview with the New York Times, he states:  “In terms of artificial intelligence, in terms of robotics, etc, we truthfully have the capability now of creating a society where all of our people have a decent standard of living.” We don’t have to look just at the pie-in-the-sky to understand the economic impact of alignment. IBM's survey of business leaders reveals that 78% cite lack of trust in AI systems as a major barrier to adoption. Deloitte's State of AI report, 63% harbour concerns about AI reliability. McKinsey's analysis suggests successful AI alignment could unlock $13 trillion in annual economic value by 2030 through improved decision-making, increased productivity, and new product innovations.  With all this at stake, how do we get alignment right for this new Intelligence Age? The Next Era of Alignment The imperatives of alignment are not lost on the current leaders of the AI wave. Sam Altman, CEO of OpenAI, has said: “Even if we stopped all AI progress today, the existing technology has sufficient capabilities to fundamentally transform our economy and society – we just haven't figured out how to properly implement and control it yet.” Demis Hassabis of Google DeepMind has noted that “the gap between AI's theoretical capabilities and its practical implementation remains substantial…In a world of increasingly abundant intelligence, what remains scarce is our ability to reliably harness and direct it." This is not just lip service. AI Labs are investing significant portions of both their budget and talent pool into alignment research. This, however, also presents a challenge. The current paradigm of AI alignment research concentrates the responsibility for alignment in the hands of a few labs, creating a precarious situation akin to having oil refinement controlled by a single entity. While companies like OpenAI, Anthropic, and DeepMind have made commendable commitments to safety, they face inherent tensions in their position. The AI Laboratory Dilemma AI labs face an impossible balancing act. They must: Race to maintain competitive advantage in an increasingly crowded field Satisfy investor demands for rapid progress and market share Ensure rigorous safety standards and alignment protocols Navigate complex geopolitical pressures and regulatory environments Game theory tells us this situation is unstable. In a competitive landscape where being first can mean market dominance, the pressure to prioritize capabilities over safety becomes nearly irresistible. Even if 99% of labs maintain strict alignment protocols, it takes only one actor choosing to prioritize speed over safety to force others to follow suit or become irrelevant.  The labs themselves cannot possibly anticipate all use cases and deployment contexts for their models. An AI system that appears well-aligned in laboratory conditions may exhibit unexpected behaviors when deployed in specific industry contexts or novel situations. We must consider alternative solutions and demand a more distributed approach to alignment.  The Case for Distributed Alignment Just as the oil industry evolved from vertically integrated monopolies to a diverse ecosystem of specialized players, AI alignment must become a distributed responsibility. This requires developing robust tools and frameworks that enable organizations deploying AI to implement and maintain alignment independently of the original model providers, ultimately placing stewardship with the users, not the creators of the models.  In the case of distributed alignment, where you may not have access to the underlying model and its internal parameters, you can still follow fundamental principles to mitigate potential alignment issues.  We should know what AI knows: Distributors of AI systems should thoroughly understand and audit additional data, prompting, and context they feed into the system. This corresponds to Input Monitoring and Control. We can call this Data Alignment. We should know what AI does: Every decision and action made by an AI system should be monitored. This corresponds to Output Monitoring and Control. We can call this Eval Alignment. Every AI system should have a human boss: Every AI system should have a human control system where human judgement can correct, override, and terminate AI action. This corresponds to Human Monitoring and Control. We can call this Agentic Alignment. These principles should be followed throughout the entire AI distribution chain. Consider this in a practical scenario: a healthcare organization deploying a third-party AI diagnostic tool. Without access to the underlying model, the organization could implement input controls by curating patient data to ensure relevance and quality, output controls by comparing AI-generated diagnoses against established benchmarks and ground truth, and human oversight by requiring a doctor to validate and sense check AI recommendations and sending back errors for correction. This would allow the healthcare organization to have some form of alignment over the system even without accessing the behind-the-scenes model. In an ideal world we would have AI models that were 100% accurate, 100% honest, 100% explainable, and 100% controllable. Unfortunately, we do not have such guarantees. Without control of the underlying model, we must think beyond models and instead to AI systems. The development of a framework of distributed alignment can still contribute to the robustness, interpretability, and control of these AI systems that a model is powering. We must develop tools to improve our observability and to continually course-correct AI with strong protocols and human oversight. In future articles we will cover these controls in more detail and how they can be operationalized with concrete strategies, actions, and tools.  Regardless of implementation details, the thrust of distributed alignment is that the responsibility for aligned AI systems should not just sit with the AI labs, it should also sit with AI application companies, end users, government agencies, and intermediate infrastructure providers. Only with a distributed approach will we have a chance of practically aligning AI. Making Distributed Alignment Work We would be remiss if we did not also mention the role of regulation in this framework. While some sort of AI regulation is almost inevitable(and certainly needed), we cannot expect that regulation will be a panacea to this issue. AI technology is moving faster than any other technology in history and regulation moves at the speed of, well, government. The complexity of the technology and the inherently fragmented nature of regulation will also make it extremely difficult to enforce consistently and uniformly. We must consider these frameworks with future regulations in mind, but we cannot wait for them in place before addressing the problem. The ultimate success of distributed alignment depends on creating an ecosystem where: Organizations have access to user-friendly alignment tools that automate tasks while keeping humans in the loop for auditing. Best practices and metrics for alignment are standardized and understood across industries Regular auditing and certification processes exist Regulatory frameworks support and enforce alignment standards Just as the oil industry developed standard safety protocols and quality metrics, the AI industry needs to develop standard alignment protocols that can be implemented and verified across different contexts and applications.  The goal isn't to remove responsibility from AI labs but to create a robust system of checks and balances where alignment is maintained through multiple layers of oversight and control. This distributed approach to alignment will ensure that AI systems remain safe and beneficial as they become more deeply integrated into every aspect of society. The Opportunity Ahead  The cat is now out of the bag with AI development and we can’t put it back. It is now incumbent on us to also recognize the urgency of the moment. This recognition demands that we understand alignment not as a distant concern for some Terminator-like future, but as an immediate and pressing challenge that will define our era. The current tenor of the conversation is around compute infrastructure and successively larger models from foundation model providers. But we may be missing the forest for the trees. We are on the verge of unleashing an infinite army of infinitely intelligent interns into a world without the institutional infrastructural or guardrails to absorb them safely or effectively. Unlike human employees who are governed by constructs such as management principles, legal frameworks, and self-inforced social norms developed over thousands of years, we have to figure this all out in the space of a few years. In this effort, we have the opportunity to really interrogate ourselves and our society deeply in the process.  A common dynamic we have observed in various facets of society is what I call “Money-ball-ification”. This refers to the Michael Lewis book where he described how baseball was transformed by introducing quantitative methods and statistical analysis - to great success. We have seen this application of statistics and math to increasingly more areas such as finance, elections, and even content creation. In a world of AI systems governed by computer scientists and engineers, we will now have to do this on a global social scale.  In an effort to align AI systems, we must grapple with deep questions of ethics, values, and human nature that philosophers have pondered for millennia. Deep moral questions will come into the purview of technology, and we have the chance to not only positively affect the direction of technology but also to improve our understanding of ourselves.  For AI, the stakes could not be higher. Like in the industrial age accelerated by Drake and others, in the Intelligence Age to come, we must navigate downside risks, social harms, and perhaps dizzying upside potential. The world will become unrecognizable from what it looks like today. It is on us to ensure these systems are aligned towards the greatest good. We must take the responsibility of alignment as a collective action.  Our task is to ensure that when future generations look back on this moment, they see not just the rise of the Intelligence Age but the moment when humanity chose to take conscious control of its technological destiny. The time for that choice is now.

The Hidden Cost of Fragmented Data Operations Enterprise AI teams are drowning in data—but starving for insights. While individual projects might be hitting their output targets, organizations lack the helicopter view needed to optimize operations, demonstrate ROI, and make strategic decisions about their AI data infrastructure investments. The result? Decision-makers flying blind, resources misallocated, and opportunities for optimization missed across the board. Organization Analytics changes that—today. What's New: Your Command Center for AI Data Operations Organization Analytics transforms Encord from a project-level tool into an enterprise-grade command center. Instead of jumping between projects to piece together performance data, teams now get comprehensive visibility across their entire AI data operation from a single, intuitive dashboard. Encord Organization Analytics Dashboard Key Capabilities: Immediate Strategic Overview Homepage analytics widget surfaces vital metrics the moment you log in Color-coded trend indicators show percentage changes at a glance Monitor active users, tasks submitted, annotation time, and labels created organization-wide Near Real-Time Operational Intelligence Interactive charts and graphs reveal performance patterns across all projects and teams Advanced filtering by project, user groups, collaborators, ontology, project tags and date ranges Quality monitoring to pinpoint outliers and bottlenecks before they impact deliverables Enterprise-Grade Reporting Comprehensive analytics dashboard with interactive visualizations Advanced data export capabilities for external analysis and reporting Flexible filtering and customization to meet diverse stakeholder needs Built for Scale Optimized performance handling thousands of projects, thousands of team members Millions of tasks and hundreds of millions of labels with sub-second load times Why This Matters Now As AI models become more complex and data requirements explode, the gap between project-level execution and strategic oversight becomes a critical bottleneck. Organizations investing millions in AI data infrastructure need visibility into: Resource allocation efficiency: Which teams and projects deliver the highest output and ROI? Quality consistency: Where are annotation standards slipping across the organization? Capacity planning: How do you scale annotation operations to meet growing model demands? Organization Analytics doesn't just answer these questions—it transforms them into operational and competitive data advantages. Real Impact from Early Adopters "Organization Analytics gives me the high-level overview I need without drowning in details. I can now see exactly how much time we spent in Encord, how many labels have been created, and how many projects have been completed—all from a single dashboard." — Head of AI, Computer Vision Startup "The ability to compare team performance across projects with detailed metrics has been invaluable. We can now easily identify which workflows are most effective and quickly see where resources are needed most." — ML Engineering Lead, Enterprise AI Company "Before Organization Analytics, I spent hours gathering status updates from each team instead of solving problems. Now I have full visibility into project and team status’, can track individual annotator productivity, and can identify quality issues as they happen." — DataOps Manager, Retail AI Platform Designed for Every Stakeholder AI Executives & Decision-Makers Strategic resource allocation across the organization Clear ROI assessment on annotation infrastructure investments Budget justification with aggregate impact visibility Enterprise Admins & ML Engineers Resource optimization across multiple projects Team performance comparison with derived metrics such as rejection rates and avg time per task & label Cross-project workflow effectiveness analysis Project Managers & DataOps Managers Real-time workload coordination Quality control monitoring across annotation pipelines Annotator and project performance tracking How It Works Organization Analytics aggregates data across your entire Encord Organization, applying intelligent filtering and visualization to surface the insights that matter most. The system automatically calculates derived metrics such as rejection rates, time per task, and time per label, providing meaningful comparisons across different project types and team structures for accurate trend analysis. Encord Organization Analytics filtering interface Advanced filtering capabilities let you drill down into specific segments—whether you need to compare performance across different ontologies, analyze trends for specific user groups, or assess the impact of recent workflow changes. Get Started Today Organization Analytics is available now for all Encord customers through organization administrators. The feature automatically aggregates historical data from January 1st, 2025, so you'll see immediate value from day one. Schedule a demo to see Organization Analytics in action. Ready to transform your AI data operations from reactive to strategic? Your comprehensive view of AI data pipeline performance and annotation project success is just one click away. Learn more: Organization Analytics Documentation Schedule a personalized demo Contact our enterprise team

In our latest webinar, the Encord team explored the rapidly evolving world of data-centric AI—with a special focus on world models and hands-on insights from real annotation workflows. From theoretical frameworks to practical demos, we highlighted how teams can build more intelligent, responsive AI pipelines using Encord. 5 Key Takeaways Here are 5 key takeaways from the session: 1. World Models as a Foundation for AI Reasoning We unpacked how world models allow AI to simulate and plan in virtual representations of reality—critical for fields like robotics and autonomous systems. These models enable AI to go beyond raw data and reason about cause, effect, and context. 2. Feedback Loops & the Human-in-the-Loop Advantage Whether through active learning or model-in-the-loop workflows, integrating human review helps accelerate iteration cycles and improve training data. Our engineers demonstrated how Encord supports these dynamic feedback loops for continuous model refinement. 3. Smart Automation with Precision Tools like consensus workflows, ontology management, and real-time collaboration help teams automate at scale—without losing control over data quality. A live demo showed how Encord balances efficiency and oversight in complex annotation tasks. 4. Metrics That Matter Beyond accuracy scores, the team emphasized tracking labeler consistency, annotation complexity, and review turnaround to better evaluate data workflows and model performance. 5. Cross-Team Collaboration & Customer-Driven Innovation The session wrapped with a look at how collaboration between product, ops, and data science teams leads to more aligned objectives and better outcomes. We also highlighted new features inspired directly by user feedback—underscoring our commitment to building alongside our community. Watch The Full Event Did you miss our webinar or want to re-watch to ensure you did not miss any insights? Watch the full webinar below.

Whether you're training a computer vision model, labeling datasets for healthcare AI, or building autonomous vehicle systems, image annotation is the backbone of your project’s success. With the surge of multimodal AI applications in 2025, choosing the right image annotation tool is more important than ever. In this article, we'll break down the best AI image annotation tools, compare their key features, and help you determine which platform best suits your workflow - whether you're working with millions of images or specialized data like DICOM files. Why Image Annotation Tools Matter for AI AI models are only as good as the data they’re trained on. Annotation tools are essential for transforming raw visual data into labeled datasets that machine learning models can understand. The right platform will: Reduce manual labeling time Improve label accuracy and consistency Support diverse data types (e.g., images, video, geospatial, medical) Offer collaboration, QA, and integration with your ML pipeline With hundreds of tools on the market, choosing the best image annotation software for your use case can be difficult. Below, we break down the top tools that stand out in 2025. Top AI Image Annotation Tools Compared 1. Encord – Best for Multimodal Annotation and Enterprise-Grade Workflows Encord stands out in 2025 as the most comprehensive annotation platform for teams working with complex or high-volume datasets. Whether you're labeling medical imagery, satellite data, or video, Encord offers unmatched support for multimodal annotation and production-level MLOps integration. Why Encord is a Top Image Annotation Tool: Supports all major data types: Images, video, DICOM, geospatial, audio, and documents Customizable annotation workflows with expert review and QA tools Built-in model evaluation and monitoring to improve data quality iteratively Programmatic access via robust APIs and SDKs Secure and compliant (SOC2, HIPAA, GDPR) Example Use Case: Stanford Medicine's healthcare teams use Encord to annotate DICOM data and build AI models for clinical imaging, benefiting from role-based controls and high-precision tools. 2. V7 – Powerful UI and Automation V7 offers fast, AI-assisted annotation with an intuitive user interface. It supports both manual and automatic labeling and provides powerful workflow automations for model-assisted labeling. Built-in model training and versioning Highly visual, collaborative interface Strong QA features and customizable pipelines Ideal for image, video, and document data While V7 is great for visual annotation workflows, it may lack some of the deeper evaluation tooling Encord offers for model testing and curation. 3. CVAT – Top Open-Source Image Annotation Tool CVAT (Computer Vision Annotation Tool) is the go-to choice for teams seeking an open-source solution with complete control over their annotation pipeline. Built by Intel, it offers robust manual annotation tools and plugin support for custom functions. Free and community-supported Designed for technical teams comfortable with self-hosting Works well for image and video annotation Limited support for non-visual modalities (e.g., audio, documents) Best suited for internal teams with dedicated DevOps resources. 4. Labelbox – Well-Rounded With Model Integration Labelbox combines data labeling, model integration, and analytics under one roof. It supports various file types and integrates with leading cloud services and ML tools. End-to-end annotation and training workflows Active learning support Usage-based pricing that can scale quickly Strong SDK and API support Labelbox is a solid middle-ground option between Encord’s enterprise-grade platform and open-source tools like CVAT. 5. Roboflow – Developer-Friendly with Built-In Preprocessing Roboflow is popular among developers and ML researchers thanks to its simple interface and strong dataset management tools. While its core focus is on preprocessing and deployment, it includes image annotation features. Auto-labeling using pre-trained models Public dataset hosting and export Great for prototyping models quickly More limited in advanced QA or multimodal support 6. Lightly – Best for Data Curation (Not Annotation First) While Lightly isn't an annotation tool in the traditional sense, it helps teams select the most informative data for labeling by using self-supervised learning. It integrates with annotation tools but focuses on optimizing training datasets. Useful for reducing labeling volume Prioritizes complex examples and underrepresented classes Works alongside platforms like Encord or Labelbox Use Lightly as a complement to your annotation pipeline. How to Choose the Best Image Annotation Tool for Your AI Project When deciding which tool is right for you, consider the following: If you're building models in healthcare, robotics, or any multimodal environment, a tool like Encord provides annotations and full data curation, QA, and model evaluation capabilities. Final Thoughts: What’s the Best Image Annotation Tool in 2025? The best AI image annotation tool depends on your project size, domain, and required precision. For large-scale enterprise teams working with complex datasets—especially those involving medical imagery, video, or multimodal inputs—Encord leads the way in 2025. With its all-in-one approach to data curation, annotation, QA, and evaluation, Encord empowers ML teams to move faster, reduce labeling errors, and confidently put models into production. Whether you're a startup looking for automation or a hospital labeling DICOM scans for diagnostics, investing in the right annotation tool will save you time, improve model performance, and future-proof your AI pipeline.