Top Video Annotation and Labeling Companies 2025

The data annotation and labeling market size was USD 0.8 billion in 2022 and may reach 33.2% CAGR by 2027. The deployment of AI-enabled systems shows remarkable expansion and the fundamental role is played by annotated data in training machine learning models.

The blog explores everything about video labeling, its types, processes, industries, a list of top video annotation companies, and why choosing the video annotation partner is key to success.

What Is Video Data Annotation?

The process of video annotation involves adding metadata or contextual information that converts unstructured video into a machine-readable format. This additional information makes training AI systems more seamless as it describes the content, quality, format, and other attributes of a video.

Here, data annotators take temporal references to track objects, actions, and events across multiple frames and time sequences. It is an intricate process as video varies from movie scenes, camera feeds, drones, navigations, and more, which increases the demand for specialized annotators who can do the following tasks:

• Frame-by-Frame Annotation: Continuous tracking of objects and events across the video timeline by annotators to ensure consistent analysis and labeling of each frame in a video clip.
• Object Tracking: To label dynamic objects that move, change size, or interact with other visual elements, annotators are required to find them across several video frames.
• Activity Recognition: Human actions, behaviors, and emotions are identified as activities and labeled by the annotators within specific time segments, enabling AI models to understand complex behavioral patterns.
• Scene Understanding: Contextual information about the environment, weather conditions, lighting, and spatial relationships between objects is captured to provide comprehensive scene analysis.

The skillsets and level of annotators is determined by the aforementioned competencies. Not to mention having an understanding of how annotation tools work for the implementation of AI-enabled systems that drive the demand for precisely labeled video is also crucial.

Top Video Annotation and Labeling Companies of 2025

Cogito Tech
Cogito Tech video annotation services focus on delivering high-quality training data services accurately, emphasizing regulatory compliance and data security. Industry leaders trust Cogito Tech’s for their precision, scalability, and compliance-driven approach to training data.

Financial Times has recognized Cogito Tech as the America’s fastest-growing companies for two consecutive years. As a result, it has become a go-to partner for top AI developers and Fortune 500 enterprises.

• Global workforce of skilled annotators and subject-matter experts
• Industry-compliant annotation processes
• High-accuracy specialized annotation services
• Custom workflow development
• Secure data handling and processing

Anolytics
With an emphasis on computer vision systems, Anolytics specializes in data annotation and labeling for AI model training.

• Ensure data security and privacy and adhere to GDPR, SOC 2, and HIPAA data requirements.
• Image, text, audio, video, and DICOM formats are among the supported data types.
• Bounding boxes, cuboids, lines, points, polygons, segmentation, and NLP tools are some of the labeling techniques used.
• Teams are managed by SMEs that give LLMs domain-specific training and fine-tuning datasets.

Scale AI
Scale AI has significant contracts in defense and enterprise applications. They combine advanced AI-assisted annotation tools with rigorous quality control processes.

• Advanced 3D annotation for autonomous driving applications
• Government and defense-grade security protocols
• Large-scale data processing infrastructure
• AI-assisted pre-annotation and quality validation

Labelbox
Labelbox offers a comprehensive platform-based approach that combines annotation tools with data management, model training, and performance monitoring capabilities.

• Integrated annotation and model development platform
• Advanced collaboration tools for distributed teams
• Extensive API and integration capabilities
• AI-assisted labeling and quality assurance

Appen
Appen leverages a global crowd workforce of over 1 million contributors to provide scalable annotation services across multiple languages and cultural contexts.

• Global workforce with 24/7 availability
• Multi-language and cross-cultural annotation expertise
• Established quality control processes
• Scalable infrastructure for large projects

iMerit
iMerit emphasizes ethical AI development and social impact while delivering high-quality annotation services. They specialize in complex, domain-specific annotation tasks.

• Medical imaging and healthcare annotation expertise
• Ethical AI practices and social impact focus
• Specialized training for domain-specific tasks
• Strong quality assurance and compliance protocols

CloudFactory
CloudFactory combines human expertise with automation to deliver scalable annotation services while maintaining quality and ethical standards.

• Human-in-the-loop automation approach
• Scalable workforce management
• Quality-focused processes
• Ethical AI development practices

V7 (formerly V7 Labs)
V7 provides advanced annotation tools specifically designed for medical imaging and autonomous systems, with strong emphasis on accuracy and compliance.

• Medical imaging annotation expertise
• Advanced AI-assisted annotation tools
• Regulatory compliance focus
• Integrated annotation and model development

Types of Video Annotation

• Bounding Boxes: In a video sequence, rectangular frames are drawn around objects of interest which are used to understand the size and location for object detection algorithms.
• Polygonal Segmentation: It is used to outline the exact contours of objects and pixel-level accuracy for semantic segmentation tasks.
• Keypoint and Landmark Detection: It is used to point at objects or human bodies to enable pose estimation, gesture recognition, and anatomical analysis.
• 3D Cuboid Annotation: It is used to find out depth information and spatial orientation in crucial AI-enabled systems for autonomous driving and robotics applications.
• Temporal Annotation: For video annotation, time-based labels are helpful in identifying when specific events occur and at what specific time intervals or frames in a video sequence. They detect action, events, scene transitions, and the behavior of the object or person over time.
• Attribute Labeling: Such as color, material, condition, or behavioral states in video content are required to know the characteristics of objects and rich contextual information for training AI models.

The above types of annotations are helpful for industrial applications, such as:

Autonomous Driving
The autonomous driving market was valued at US$170.22 billion in 2024 and is projected to reach US$668.64 billion by 2033, at a CAGR of 17.63% during the forecast period 2025–2033. Cutting-edge AI innovations fuel this upward trajectory and give rise to data annotation services, such as LiDAR annotation, video annotation, radar, and high-resolution camera feed annotation, to create comprehensive 360-degree environmental awareness.

Exposure to annotated training videos allows computer vision models to recognize and navigate around important road objects. Navigating obstacles and operating in chaotic real-world traffic conditions means computer vision-based models must be trained with annotated data that adds information and labels to videos to build consumer trust in self-driving systems.

Security and Surveillance
Videos from surveillance systems need highly accurate annotation to transform raw data into insightful, useful information. The complexities handled by video annotation companies in labeling raw video for security footage highlight its many uses because precise video tagging is essential for improving surveillance by analyzing threats and questionable activity.

Improved surveillance is the foundation for modern security infrastructures, maintaining safety in both the public and private sectors. The increasing focus on public safety and smart city initiatives drives demand for sophisticated video analytics.

Annotating videos improves the speed at which threats are detected. By giving AI systems accurate data, it speeds up the process of identifying facial recognition, behavioral analysis, anomaly detection, and addressing threats. Such swift detection is essential to safeguard places like commercial structures in urban areas.

Retail and E-commerce
Video tagging for e-commerce plays a critical role in analyzing, recognizing, and detecting objects in real-time, improving the shopping experience. Retail video annotation usually involves multiple intertwined tasks at different granularities.

With a single retail video, it is expected by data annotators to perform at least three distinct annotation tasks. First, annotate store-wide attributes throughout the entire clip, such as store section or crowd density. Second, annotate objects, including bounding boxes for consumers, products, and staff members. Last, per-frame annotation concentrates on computer vision applications in retail requiring detailed labeling of customer-product interactions and shopping behaviors.

In retail specifically, these methods have been explored for tasks like customer journey analysis, product interaction detection, and theft prevention.

Manufacturing
There is more to industrial video tagging than just tagging technical jargon. From basic assembly lines to intricate AI-integrated systems, the manufacturing industry has changed over time. An AI-powered approach has modified the production environment, making it more flexible and focused on accuracy. Human annotation is done to accurately identify, recognize, and pinpoint goods in real-time and improve the production process. This is why effective visual identification and video object labeling are essential.

Video annotation services for the manufacturing industry aids personalized quality control, error prediction, and part recognition. Analyzing industrial practices provides new information that improves product quality and boosts productivity. It greatly increases the effectiveness of video annotations, allowing for things like

• Recognition of Components
• Error Speculation
• Perceptible Inspection
• Demand Analysis
• Production Efficiency Optimization

Healthcare
The use of video annotation for healthcare adds necessary metadata to video frames. This approach improves surgical procedure analysis, strengthens patient monitoring, and encourages improved medical professional training. Acknowledging the surgical phase is a component of surgical video annotation.

For instance, the AI system can precisely identify, annotate, and time stamp each step of a knee replacement surgery, including incision, bone preparation, implantation, and wound closure. In order to achieve the best results during dental implant procedures, the AI system can monitor implant placement and occlusion. Like robotic surgery, this technology uses anatomical structures, surgical equipment, and surgical processes to monitor and enable automated or semi-automated surgical procedures.

For all this to happen, the Medial AI model needs to be trained on quality video training data. Thus, professional medical annotators add depth perception to surgical video annotation because they have a comprehensive understanding of what happens during surgery.

Why Choosing the Right Video Annotation Partner Matters

The quality of video annotation ensures that models learn from accurate, representative data. It directly correlates with the performance and reliability of AI models, making them robust and reliable. The trust factor comes from a professional video annotation partner.

Edge Case Coverage
The model’s ability to recognize objects needs experienced annotation teams to properly label edge cases and rare scenarios. Standardized annotation practices across video datasets ensure AI models make fine-grained distinctions between similar objects or action sequences. The right annotation partner ensures all these are crucial components for developing robust AI systems, i.e., capable of handling unexpected situations.

Cost, Scalability, and Time Considerations
Professional annotation services maximize the balance between quality and cost because annotation needs might range from thousands to millions of video frames. This complexity level along with managing large-scale projects means all more reasons to manage timeliness and quality which is provided by the right partner.

Data Security and Compliance
Sensitive information found in video data means handling it with care, along with ensuring safe storage and submission. Merely annotating data does not ensure model success but it should be according to industry regulatory standards such as for healthcare, finance, and driverless cars, and annotation partners must demonstrate that their training data are in compliance with industry-specific rules.

Human-in-the-loop
Even though annotation is becoming more automated, human expertise is still necessary. This is why, human-in-the-loop supervision can take care of edge cases, complex scenarios, and quality control that automated methods might miss. Thus, the human workforce offered by video annotation companies can employ a number of quality checkpoints for review.

Criteria for Ranking the Top Companies in 2025

Quality and Accuracy of Annotations

• They must offer pixel-perfect segmentation, accurate bounding boxes, and precise temporal labeling across diverse video content.
• They must provide uniform annotation quality across large datasets and multiple annotators training data for AI models.

Industry Expertise

• The industrial know-how of edge cases, challenges, and handling complex annotation tasks
• They must have expertise in 3D object detection, multi-object tracking, and temporal event recognition.
• Ability to develop specialized annotation workflows, custom tools, and industry-specific labeling schemas.

Data Security and Compliance

• The focus on compliance with international security standards must be at every stage of the annotation pipeline, especially for enterprise-level projects.
• They understand regional and industry-specific regulations upholding safety standards whenever applicable.

Turnaround Time and Scalability

• They have proven records to show that they have handled large-scale annotation projects within tight deadlines while maintaining quality standards.
• To scale up or down an AI project, they must use both automation tools and a human workforce.

Multilingual Proficiency

• Offering multilingual annotation is advantageous for global AI applications and a variety of market needs.
• The team should also show knowledge of local laws, cultural quirks, and geographic variations that could affect the need for annotations.
• They ought to have access to annotation teams located in various time zones.

Emerging Trends in Video Annotation

AI-Assisted Annotation Tools
Automated object detection can save annotation time and improve consistency. However, human annotators are required to later examine and modify the training data as needed. For example, applying AI models to identify and initially name common items in video frames and human annotators may focus on edge cases and quality enhancement.

Synthetic Data
Synthetic data is a viable option for training AI models as they are useful for the creation of specific scenarios that may be rare. However, the cleanness and accuracy that come from human-annotated data are unmatched. While synthetic data can be automatically annotated during generation, the need for manual annotation still is necessary to provide perfect ground truth labels.

Real-Time Labeling
The demand for real-time AI applications drives the development of real-time annotation capabilities that can annotate video streams in real time and adapt to changing conditions and requirements in real-time applications. This enables immediate feedback and continuous learning applications.

Multi-Sensor Data Fusion
Modern AI systems rely on data from multiple sensors, which necessitates expert annotation services that can handle complex multi-modal data integration. For example, an annotation method combines 3D point cloud data with video annotations to create a comprehensive environmental understanding of autonomous systems.

The Future of Video Data Annotation in 2025 and Beyond

Video annotation is at its pivotal moment in 2025, and support from the right video annotation provider is the key. Why? Because the applications of face recognition, navigational systems, and tightening security all depend on bettering the computer vision models for which accurate video labeling is important.

There is demand for accurately annotated video data from various industries, and this will only continue to grow exponentially. The fusion of AI-assisted annotation tools, synthetic data generation, human supervision, and real-time processing capabilities can help organizations achieve its goal in developing better AI/ML models. Organizations that combine human expertise with advanced automation will be best positioned to handle the need for scalable, reliable, and cost-effective annotation services.

Conclusion

Accurate video annotation is indeed necessary for developing computer vision projects that need human expertise and domain-specific knowledge. This is a matter of expertise for which organizations that acknowledge the strategic importance of high-quality annotation and invest in the right partnerships will be best positioned as the top business leaders.

Data scientists and AI startups are utilizing video labeling services in 2025 and will be doing so beyond to ease everyday work, which supports everything from autonomous vehicles navigating our roads to medical AI systems improving patient outcomes. Partnerships formed with video annotation companies today will shape the AI landscape of tomorrow; it is one of the most important decisions organizations or data engineers will make in their AI journey.

The absence of ​​context-rich video data can undermine your model performance. Choose Cogito Tech’s video annotation solutions for production-ready systems that can understand nuances, handle niche topics, and deliver more precise results.