V7 Darwin
Use cases
V7 powers more than 300+ commercial AI projects.
In healthcare, logistics, manufacturing, and more.
Before vs after
Before V7 Darwin
Manual, slow labeling
Hours spent drawing boxes, masks, and keypoints by hand.
Inconsistent quality
Annotation errors and no standardization across team members.
Scattered workflows
Switching between tools for annotation, reviews, and approvals.
Zero visibility
No tracking of progress, productivity, or dataset quality.
Limited file support
Struggling with videos, DICOMs, microscopy, or 3D formats.
After V7 Darwin
Automated labeling
AI-assisted tools complete annotations in seconds — including complex shapes.
Consistent quality
Built-in QA, consensus stages, and annotator performance tracking ensure precision.
Unified workflows
Multi-stage workflows with automation, roles, and logic-based routing.
Complete visibility
Full project dashboards, metrics, and review stats to monitor everything in real time.
Supports any file
Seamlessly label any format — from time-lapse microscopy to multi-camera footage.
Automated labeling
Use an intuitive interface and AI-assisted tools to turn complex labeling tasks into a few simple clicks and adjustments. Work with any size dataset and file type, from videos, PDFs, and architectural drawings to specialized medical formats like SVS or DICOM.
Workflows
V7 allows you to design multi-stage review workflows to orchestrate your labeling process. Assign roles, tasks, and manage project completion. Use conditional logic and automations to always route data to the right stages or team members.
“Visibility on metrics in V7 is very helpful to us, and it's something we didn’t have in our internal solution.”
Andrew Achkar
Technical Director at Miovison
“V7 is great. The API is very straightforward to use, so we can easily get data into our system.”
David Soong
Director, Translational Data Science at Genmab
“We conducted extensive research of annotation tools and ultimately chose V7.”
Maleeha Nawaz
Manager of Quality and Data Curation at Imidex
Created training data batches
95% faster
Developed AI models
10x faster
Maleeha Nawaz
Manager of Quality and Data Curation at Imidex
"We conducted extensive research of annotation tools and ultimately chose V7 as it fits our business needs best due to its customizable workflows, simple setup, consensus stages, and the ability to monitor annotator performance and project progress."
Mark Robson
Technical Specialist at MTC
"We were looking for an annotation tool that would be much faster, and V7 sped up our labeling 9-10x compared to VGG. The appeal of using V7 is that it’s commercial off-the-shelf, very intuitive, and easy to use for non-technical people involved in our project."
Sped up tumor detection
in digital pathology images
Built AI for sorting and
segregating nuclear waste
