V7 Labs provides an automated "programmatic labeling" platform that segments and labels images to speed development of computer-vision models, claiming it can bootstrap automation from roughly 100 human‑annotated examples. The company has strong traction in medicine and science and is used by more than 300 customers including GE Healthcare, Paige AI and Siemens, with growing interest from tech and industrial companies. V7 says its platform reduces the time engineering teams spend managing training data and routes unclear cases back to humans for review. CEO Alberto Rizzoli says ARR grew three‑fold in 2022, though he declined to disclose absolute revenue figures. The startup will use the new funding to hire more engineers and build out business operations, with an emphasis on expanding in the U.S. V7 positions itself as an automation-first alternative to manual labeling providers and pitches its tooling as infrastructure for faster AI productization. V7 Labs provides a browser- and cloud-based computer vision platform that automates image and video data pipelines, labeling, dataset versioning, and model training and deployment. The platform claims to accelerate creation of high-quality training data by 10–100x and supports thousands of annotations per image and millions of images per dataset. Users can train, deploy and run vision models within the platform with minimal DevOps, and labeling can be automated without prior training data. V7 is used by about 100 customers, including Tractable, GE Healthcare and Merck, and is growing fastest in medical imaging because it supports DICOM annotation and HIPAA compliance. The company says it will soon add tools to audit models and their training sets to debug, test data quality, discover failure cases and reduce unwanted bias. Financially, V7 has raised a $3M seed round to further develop and scale its platform.

Total raised

$36M

Funding rounds

2

Latest round

Series A

Latest activity

Nov 2022