Encord vs V7

Encord is a platform for labeling and managing visual data, with real strength in the hard cases: medical images, 3D and LiDAR, and long video where most tools start to struggle.

The annotation tools handle standard formats plus the ones healthcare teams actually deal with, including DICOM and NIfTI, with 3D viewing across the usual planes and controls radiologists expect. That focus shows up in the compliance too. Encord carries SOC 2 Type II, HIPAA, and GDPR, which is the baseline for anyone building AI on patient data.

Beyond drawing labels, the platform leans on active learning to make the labeling itself smarter. Instead of annotating everything evenly, you can prioritize the data where the model is weakest or least certain, which cuts how much you have to label to move accuracy. There are tools for finding and curating the right images and for spotting label errors before they poison training.

Teams tend to use it for:

• Medical imaging annotation with the compliance a clinical project requires
• 3D, LiDAR, and sensor data for robotics and autonomous systems
• Long or high-resolution video that needs consistent tracking
• Curating and cleaning large image sets so you label the data that matters

The company operates as Cord Technologies and works with a lot of healthcare and life-sciences groups, which is a useful signal if your data comes with regulatory weight.

Pricing starts with a free trial, then moves to paid plans and enterprise agreements scaled by usage, features, and the compliance needs of the deployment.

Best for computer-vision teams doing complex or regulated work, especially in healthcare, that need serious tooling and a real compliance story. Less of a fit for a team whose data is mostly text or who wants a simple general-purpose labeler for a small project.

V7 builds tooling for labeling visual data quickly, with automation doing more of the first pass so annotators spend their time correcting rather than clicking.

The labeling product, V7 Darwin, handles a wide range of formats: images, video with object tracking, medical imaging like DICOM, PDFs, and more. Auto-annotation proposes labels that a person then reviews, which speeds up the tedious parts of segmentation and tracking. Workflows route items through labeling and review stages so quality control is built into the process instead of bolted on later.

It leans into regulated and technical work. The platform carries HIPAA, SOC 2 Type II, and ISO 27001 compliance, which matters when the data is medical scans or anything privacy-sensitive. Healthcare, life sciences, and manufacturing teams are a big part of who uses it.

More recently V7 launched V7 Go, which points the same automation ideas at documents and knowledge work: pulling structured answers out of files, running them past a human when confidence is low, and feeding AI agents reliable data. It is a natural extension for teams whose data is part image, part paperwork.

Common ways teams use it:

• Segmenting and tracking objects across video for computer-vision models
• Annotating medical images with the compliance a hospital project requires
• Turning messy documents into structured, checked data with V7 Go
• Running human-in-the-loop review on model output before it ships

Pricing starts with a free trial, then moves to paid plans that scale by usage, features, and seats, with enterprise agreements for larger or regulated deployments.

Best for vision-heavy and document-heavy teams that want automation to carry the first pass and still keep a human in the loop. Less of a fit for a team whose entire need is text-only LLM feedback data, where a provider built around that work, like Surge, is a closer match.