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VisionEngine · A digiLab Product

Computer vision
you can trust,
adapted to your data.

Bring 10–100 images. We run a baseline model, ask you to review just 10 labels, and show a measurably better model — before you commit to anything. Built on the same uncertainty-first principles as the Uncertainty Engine.

Try VisionEngineWatch the 90-second demo
Detection and segmentation No credit card 5-minute first result
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First model in about 5 minutes.
The 90-second demo

See the loop in action

From raw images to a measured model — in minutes.

Read the demo description

Watch the loop

Upload → Baseline → Prioritise → Review → Train → Measure

How it works

From your images to a model you trust, in four steps.

The product is built around one promise: every loop should give you a measurably better model, without asking you to label a thousand images upfront.

01
Bring your images
10 to 100 is the sweet spot for a first run. JPG, PNG, TIFF. Connect a folder, an S3 bucket, or try our sample dataset to see the loop in action.
02
Run a baseline
Open-vocabulary detection or segmentation, with no training. The base model is also our benchmark — every later model is compared back to it.
03
Review just 10
We rank your images by uncertainty + novelty and ask you to fix the ten most informative predictions. Each loop is small, guided and measurable.
04
See improvement
Before you commit to anything, see how a model trained on your 10 corrections compares to the baseline. You decide if it earns the next step.
The trust contract

The same uncertainty-first principles as the Uncertainty Engine — for vision.

Uncertainty-aware
Every prediction carries a confidence + uncertainty score. The platform auto-accepts the safe ones and routes the rest to a human — by your trust threshold, not a single confidence number.
Honest baselines
If the base model isn't reliable yet, we say so — and offer a seed-label path instead of pretending bad predictions are good ones.
Active learning
We rank your unlabelled pool by what would teach the next model the most. Then we ask you to review only ten.
Measurable improvement
AP, precision and recall against baseline and the previous model — every loop. No vanity metrics.
Deploy with trust routing
When you're ready, your model ships behind a routing layer: auto-accept above your threshold, human review for the uncertain middle, reject the out-of-scope tail. Production feedback flows back into the loop.
Where teams use it

Designed for the hard, high-stakes vision problems engineers actually have.

Detection and segmentation are the two task types in VisionEngine. Measurements come as a derived output of segmentation when an image has a known scale.

Electronics QCelectronics
Electronics QC
Solder joints, scratches, missing pads
Aviation partsaviation
Aviation parts
Surface defects, fastener checks
Agricultureagriculture
Agriculture
Crop disease, weed segmentation
Energy infraenergy
Energy infra
Asset inspection, corrosion
Materials sciencematerials
Materials science
Grain boundary segmentation
Life scienceslife
Life sciences
Tissue and cell segmentation

Industry images used with attribution: AMagill (CC BY 2.0) · Jorge Láscar (CC BY 2.0) · Malcolm Neal (CC BY-SA 2.0) · Salem Ander (CC BY-SA 4.0) · Gael Guetard (CC BY-SA 4.0) · ZEISS Microscopy (CC BY 2.0) — via Wikimedia Commons and Openverse.

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

Try it on your own data.

Drop in your images. We'll show you what the baseline gets right, what it gets wrong, and exactly how much your ten corrections move the model. Then you decide.

Try VisionEngine