Key Takeaways

• V7 is a strong AI annotation platform with best-in-class auto-annotation powered by SAM integration, excellent video labeling tools, and a growing presence in automotive. It is a great fit for teams that value AI-assisted labeling speed across multiple verticals.

• Kognic is purpose-built for autonomous driving with 7+ years of production-grade 3D/LiDAR, native sensor fusion, and Language Grounding capabilities for next-generation VLM/VLA models.

• Neither platform is universally "better" -- the right choice depends on what you are building and how central autonomous driving is to your annotation needs.

• If your primary use case is ADAS or autonomous driving annotation at production scale, the differences in 3D depth, multi-sensor fusion, AV-specific methodology, and automotive customer track record are significant.

Disclosure: Kognic is the publisher of this comparison. We have made every effort to be factual and fair, using only publicly available information about V7. We encourage you to evaluate both platforms firsthand.

Introduction

Annotation for autonomous driving is not the same as annotation for other AI applications. The data is multi-dimensional -- LiDAR point clouds, multi-camera arrays, radar returns, all synchronized across time. Quality requirements are set by safety standards, not just model performance metrics. And the annotation workflows themselves are undergoing a fundamental shift as the industry moves from traditional perception models to vision-language models that need to understand not just what is in a scene but why things happen and what a vehicle should do about it.

This comparison looks at two platforms that both have automotive relevance -- Kognic and V7 -- through the specific lens of autonomous driving and ADAS annotation. V7 is a genuinely capable platform with strong auto-annotation technology and automotive customers. Kognic is built exclusively for the autonomous driving domain. The question is not which platform is better in the abstract, but which one fits the specific requirements of your AV or ADAS program.

Company Overviews

Kognic

Kognic was founded in 2018 in Gothenburg, Sweden, with a single focus: annotation for autonomous driving and advanced driver-assistance systems. The platform has been purpose-built from day one for the specific requirements of AV development -- 3D point cloud annotation, multi-sensor fusion, sequential frame labeling, and safety-critical quality assurance.

Kognic has delivered over 100 million annotations and works with OEMs and Tier 1 suppliers including Qualcomm, BMW, Zenseact, Continental, Bosch, Kodiak, ZF, Einride, Gatik, and Jaguar Land Rover. The company holds ISO 27001 and TISAX certifications and is headquartered in Sweden as an independent company. In recent years, Kognic has reported 90% year-over-year revenue growth and a 3x increase in customer count.

In 2025 and 2026, Kognic expanded into Language Grounding -- vision-language annotation for VLM and VLA models -- adding Write, Edit, Rank, and behaviour annotation modes alongside its Chain of Causation methodology.

V7

V7 was founded in 2018 in London by Alberto Rizzoli and Simon Edwardsson. The company has raised approximately $36 million in funding, including a $33 million Series A led by Radical Ventures and Temasek in 2022. V7 employs roughly 70 to 80 people.

V7's core annotation product, Darwin, is a multi-industry AI data labeling platform supporting images, video, documents, DICOM medical imaging, and 3D point clouds. The platform is known for its AI-assisted annotation capabilities, particularly its integration of Meta's Segment Anything Model (SAM), which enables fast one-click segmentation and auto-tracking in video. V7 serves customers across healthcare (Bayer, Boston Scientific), automotive (Continental), and other verticals.

More recently, V7 has expanded beyond annotation into agentic AI workflows with V7 Go, a platform for automating document-heavy processes in finance, legal, and insurance. This represents a strategic diversification away from annotation as V7's sole product line.

Feature Comparison

Where V7 Excels

V7 has real strengths that are worth understanding, particularly if your annotation needs extend beyond autonomous driving.

Best-in-class auto-annotation. V7's SAM integration is genuinely impressive. The Segment Anything Model enables one-click instance segmentation on images, and SAM 3 adds text-based automatic detection -- create a class called "car" and it will automatically find and segment all cars in the image. For annotation workflows where segmentation speed matters, this is a meaningful productivity advantage. V7 reports that AI-assisted labeling can reduce manual annotation effort by up to 90% for certain tasks.

Strong video annotation tools. Auto-Track provides AI-powered object tracking across video frames, handling objects moving in and out of the frame and automatically generating keyframes. For teams labeling video data, V7's interpolation between keyframes and auto-tracking capabilities reduce the frame-by-frame labeling burden significantly. This is relevant for ADAS use cases involving camera-only perception.

Automotive presence through Continental. V7 is not absent from automotive. Continental uses V7 for vehicle type recognition, make and model identification, and OCR number plate reading -- scaling from 35,000 images per week to approximately 200,000. This is a legitimate production automotive use case, even if the scope is narrower than full AV perception annotation.

Accessible entry point. V7's starter plan at $29 per month lets teams evaluate the platform and begin labeling without an enterprise sales cycle. For teams exploring annotation tools or working on smaller-scale projects, this lower barrier is genuinely useful.

Medical imaging depth. V7 has deep expertise in DICOM annotation and medical imaging, with oblique views for 3D medical data and specialized tools for healthcare workflows. Teams working across both automotive and medical verticals may find V7's healthcare capabilities relevant.

Where Kognic Excels

When the annotation requirements are specifically for autonomous driving perception, planning, or end-to-end models, the differences become significant.

Purpose-built for autonomous driving. Kognic's entire platform, team, and roadmap exist to solve the annotation challenges specific to AV development. This is not a general-purpose tool with automotive added as one of many supported verticals. Every design decision -- from the point cloud rendering engine to the quality assurance framework to the annotation workflow structure -- has been shaped by the requirements of teams building vehicles that need to operate safely in the real world.

Production-grade 3D and LiDAR depth. Kognic has been building 3D point cloud annotation tooling for over seven years. Mature 3D tooling means fast and accurate cuboid placement, efficient multi-frame sequential labeling, support for dense and sparse point clouds across different LiDAR sensors, and multi-LiDAR workflows for vehicles running more than one scanner. V7 offers 3D capabilities with AI Sense pre-labeling for a limited set of object classes in 3D (cars, pedestrians, bicycles), but the depth of Kognic's 3D tooling -- refined over thousands of production annotation projects with major OEMs -- represents a different level of maturity.

Native sensor fusion workflows. Modern AV perception stacks fuse data from cameras, LiDAR, and radar. Kognic handles synchronized multi-sensor annotation natively -- annotators see the same object across sensor modalities and label it in a unified workflow. This ensures consistency between the 3D cuboid in the point cloud and the 2D bounding box in the camera image. V7's strengths are concentrated in 2D image and video annotation; multi-sensor fusion is not a documented core capability.

Language Grounding and Chain of Causation. As the industry shifts toward vision-language models and vision-language-action models, annotation requirements are changing fundamentally. Models need to learn not just what is in a scene but why things happen and what the vehicle should do about it. Kognic's Language Grounding capability provides Write, Edit, Rank, and behaviour annotation modes specifically designed for this new class of models. The Chain of Causation methodology prevents hindsight bias by controlling what annotators can see at each stage, producing cleaner reasoning data for model training. V7 does not offer comparable capabilities for driving-specific reasoning annotation.

Proven AV customer base at scale. Kognic works with more than ten named OEMs and Tier 1 automotive suppliers -- Qualcomm, BMW, Zenseact, Continental, Bosch, Kodiak, ZF, Einride, Gatik, and Jaguar Land Rover. V7's documented automotive work centres on Continental's parking detection and OCR use case. For teams selecting an annotation partner for a safety-critical AV program, the breadth and depth of Kognic's automotive customer base signals production-level maturity across the full range of AV annotation requirements.

AV-specific quality assurance. Kognic has built over 90 quality checker applications purpose-designed for autonomous driving annotation. These check for the kinds of errors that matter in AV data: incorrect cuboid orientations, inconsistent track IDs across frames, labels that violate physical constraints, and dozens of other domain-specific quality rules. V7 provides collaborative review workflows and quality stages, but the depth of domain-specific quality tooling for autonomous driving is where a specialist platform differs from a generalist one.

Key Differences: A Deeper Look

Auto-Annotation Strength vs. Domain Depth

V7's SAM-powered auto-annotation is genuinely strong for 2D segmentation tasks. If your workflow involves labeling objects in camera images or video frames, V7's tooling will accelerate the process. But autonomous driving annotation involves more than 2D segmentation. It requires 3D cuboid annotation in point clouds, multi-sensor consistency, temporal tracking across long sequences, and increasingly, reasoning annotation that captures why driving decisions are made. Auto-annotation speed on 2D tasks is valuable, but it does not address the full complexity of AV annotation workflows.

Kognic's autolabeling approach is different -- it integrates LLMs directly into VLA annotation workflows, generating text proposals that human annotators then edit, rank, or quality-assure. This addresses the new frontier of annotation where language and action meet perception, rather than optimising for speed on traditional labeling tasks.

Strategic Direction: Diversification vs. Vertical Focus

V7 is diversifying. The launch of V7 Go -- an agentic AI platform for document workflows in finance, legal, and insurance -- signals that annotation is no longer V7's sole strategic focus. This is not inherently negative; it may make V7 a more sustainable business. But for an autonomous driving team evaluating annotation partners, it raises a practical question: where will V7's engineering investment go over the next two to three years? A platform with two distinct product lines serving different markets will necessarily split its development resources.

Kognic's roadmap is moving deeper into autonomous driving. Language Grounding, Chain of Causation, behaviour annotation, and LLM integration for autolabeling are all investments in the specific capabilities that next-generation AV models require. For teams whose annotation needs will evolve as VLMs and VLAs mature, a partner whose entire roadmap is aligned with that evolution is less likely to fall behind.

When to Choose Which

Choose V7 if:

• You need fast 2D auto-annotation. V7's SAM integration and AI Sense pre-labeling are among the best available for accelerating image and video segmentation tasks.
• Video annotation is your primary workflow. Auto-Track and SAM 3 auto-tracking provide strong video labeling capabilities for camera-based ADAS use cases.
• You work across multiple verticals. If your team labels medical imaging, documents, and automotive data, V7's breadth lets you use one platform for diverse projects.
• You want to start small. The $29 per month starter plan lets you evaluate and begin labeling without enterprise-level commitment.
• Your automotive use case is camera-only and 2D. For image classification, 2D object detection, or OCR tasks in automotive, V7 is a capable choice.

Choose Kognic if:

• Autonomous driving or ADAS is your primary use case. The platform, team, roadmap, and customer base are built around this domain.
• You need production-grade 3D and LiDAR annotation. Mature 3D tooling with multi-LiDAR support and efficient sequential labeling, refined over seven years.
• Sensor fusion is a requirement. Native multi-sensor annotation with synchronized camera, LiDAR, and radar workflows.
• You are building or training VLM/VLA models. Language Grounding capabilities (Write, Edit, Rank, behaviour annotation) and Chain of Causation methodology are designed specifically for this.
• Quality and safety are non-negotiable. Over 90 AV-specific quality checker apps, ISO 27001 and TISAX certifications, and guided annotation workflows.
• You want a proven AV partner with depth. Verifiable reference customers across major OEMs and Tier 1 suppliers with over 100 million annotations delivered.

Conclusion

V7 and Kognic are both strong annotation platforms, but they are solving different problems at different depths. V7 has built impressive AI-assisted annotation technology -- particularly its SAM integration and video auto-tracking -- and serves multiple industries including automotive. For teams that need fast 2D labeling, work across verticals, or prioritise auto-annotation speed, V7 is a legitimate option.

Kognic is built exclusively for autonomous driving. If your team is developing perception, planning, or end-to-end models for autonomous vehicles, the differences in 3D/LiDAR maturity, sensor fusion, AV-specific methodology, reasoning annotation, quality assurance, and customer track record are not incremental -- they are structural. These are the capabilities that determine whether your annotation pipeline can support the models you are building today and the ones you will need to build in eighteen months.

Both platforms deserve serious evaluation. The best decision is to test each against your specific requirements -- your data formats, your sensor configuration, your model architecture, and the quality standards your program demands.

This comparison was published by Kognic and uses only publicly available information about V7 as of March 2026. We encourage readers to contact both Kognic and V7 directly for the most current product information and to request demos tailored to their specific use cases.