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Why VisionEngineBETA

One label.
Not ten thousand.

Most computer-vision tools make big labelling jobs faster. We make them almost unnecessary. Draw a single box, let the model label the rest, correct only what it's unsure about — and watch it get measurably better each loop. Then run it without writing a line of code.

Start with one label →Watch the 90-second demo
Free during the open beta No credit card Your first model in minutes
Start with one label
First model in about 5 minutes.
The difference

Most tools make big labelling faster. We make it almost unnecessary.

The usual approach
Label thousands of images before you see a single result
Trust a single confidence number and hope
Stand up MLOps just to run the model
Guess which images to label next
The VisionEngine way
Draw one example — the model labels the rest in seconds
Trust measured improvement against the baseline, every loop
Run it from the browser, an API, or one CLI command
We rank the few images that will teach the model most

Already have a team labelling at scale? The big-dataset platforms are built for exactly that. VisionEngine is for everyone who isn't — and would rather not be.

Get started with one label

Draw one box. We label the rest.

Jet-engine inspection
Detection — one box, the rest found
Cell microscopy
Segmentation — same loop
One example teaches both detection and segmentation

Images: Jorge Láscar · ZEISS Microscopy · CC BY · via Wikimedia Commons

Trust you can measure

A confidence score is a feeling. A measured gain is a fact.

Street photos in
12
→
Model v1 · mAP50
0.995
One recorded run, held-out split~2 min end to end
Precision · recallreported every loop
Deploy and run without code

From a folder of images to a running model.

$ve run ./my-images
JSONCSVBounding boxesReportAPICLI
No ML code. No MLOps.
The 90-second demo

See the whole loop, start to finish.

Watch the loop

Upload → Baseline → Prioritise → Review → Train → Measure

And a few more reasons

Built for the hard problems, by people who study uncertainty.

Built by uncertainty experts
Co-founded by Prof. Tim Dodwell, a Professor of Uncertainty Quantification. Treating doubt as a first-class signal is the whole idea, not a bolt-on.
Detection and segmentation
Boxes for finding objects, pixel-precise masks for measuring them — the same loop and the same trust contract for both.
Review only what teaches most
We rank your unlabelled images by uncertainty and novelty, then ask you to review just the handful that will actually move the model.
An audit-ready history
Every label change and model version is recorded — the action, the contributing model, counts and timestamps. Nothing is a black box.
Your data stays yours
Run in our cloud during the open beta; on-prem or VPC deployment is available for production teams.
Free during the open beta
Bring up to 200 images per workflow and run the whole loop end to end, free during the beta.
From the Journal

Notes on building vision you can trust.

Browse the Journal →
Active Learning

Open-Vocabulary Detection: Zero-Shot Defect Finding

Open-vocabulary detection finds defects you never trained for—describe them in plain words and get an instant inspection baseline, zero labels needed.

7 min read · 2026-07-23Read →
Active Learning

Active Learning Explained: Train With Fewer Labels

How active learning uses uncertainty sampling to train an inspection model with the fewest expert labels — a guide for engineers.

6 min read · 2026-07-22Read →

Start with one label.

Drop in a handful of images, draw one box, and see a measured model before you commit to anything.

Start with one label →