V7 Darwin is a data labeling and training data platform for machine learning teams. Its annotation suite handles images, video, PDFs, architectural drawings and specialized medical formats such as DICOM and SVS. AI-assisted tools like Auto-Annotate and SAM2 segment complex objects in a few clicks, and V7 says annotation can run about 10 times faster. Workflows are built visually without code, and on-demand expert labeling services supplement in-house annotators.
Darwin is for AI research labs, enterprise ML teams, data operations managers and annotation vendors who treat dataset creation as their biggest bottleneck. Healthcare and pharma groups label pathology and radiology data, insurers label damage inspection images, and logistics, automotive and construction companies label video and documents for computer vision and document models. BPO and consulting firms use it to manage teams of labelers. Developers connect projects through an SDK, an API and storage integrations into their existing pipelines, while project leads configure review stages and consensus rules to keep quality high.
Typical projects include segmenting lesions in CT scans, digital pathology slides and radiology images, detecting items on assembly lines, and inspecting assets or property damage for insurance claims. Traffic analytics teams annotate video frames, and document workflow teams label PDFs for extraction models. Workflows can combine annotation, review, consensus and logic steps, add a model-in-the-loop to pre-label data, and trigger webhooks when work completes. Teams also run RLHF and RLAIF pipelines for frontier models, turning months-long labeling projects into weeks with human experts reviewing the output.
