V7 Labs provides an end-to-end AI training data platform designed to help machine learning teams create, manage, and annotate datasets for computer vision and other AI applications. The platform offers tools for image, video, and document annotation, including polygon, bounding box, keypoint, and instance segmentation tools. V7 supports automated labeling through AI-assisted annotation and model-in-the-loop workflows, reducing manual effort. It includes dataset versioning, quality review workflows, and team collaboration features. V7 Go, a newer product, focuses on document processing and AI workflow automation, allowing users to extract structured data from documents using AI agents without writing code. The platform is used by research teams, enterprises, and startups building computer vision pipelines, medical imaging tools, autonomous systems, and document intelligence applications. V7 integrates with popular ML frameworks and cloud storage providers to fit into existing data and model training workflows.
Target audience and deployment
- Startup
- SMB
- Mid-market
- Enterprise
- Cloud
- On-premise
- API
Key features
Use cases
- Annotate training data for computer vision models
- Automate document data extraction with AI agents
- Manage and version ML datasets
- Accelerate labeling with AI-assisted annotation
- Review and quality-control annotation outputs
- Build no-code AI document processing pipelines
Best for
- ML engineers who need to build and manage high-quality training datasets for computer vision models
- Data annotation teams who need to accelerate labeling with AI-assisted and automated workflows
- Enterprise teams who need to extract structured data from documents at scale without writing code
- AI researchers who need dataset versioning and collaboration tools for iterative model development
Integrations
Developer
GitHub, AWS S3, Google Cloud Storage, Azure Blob Storage
AI models included
OpenAI, Anthropic, Google Gemini
Other
Roboflow, Scale AI