Example · Quality
Visual inspection on the line
A camera checks each item for damage or missing parts, and a person confirms every reject.
Computer vision
Computer vision trained on your own images.
We build vision models that inspect, detect, count and read. They're trained on photos and video from your own operation, then deployed where the work happens: a phone, a camera on the line or your cloud.
What we build
Eyes for the checks your team does by hand.
We measure accuracy on your own images, including the hard cases, before anything goes live.
- Image classification and defect detection
- Object detection, counting and tracking in video
- OCR and document reading for forms, IDs, labels and receipts
- Segmentation for measurements and quality checks
- Labelling pipelines and active learning
- Deployment to mobile, edge devices or the cloud
Example projects
Where it pays off.
These are examples of first projects, not client case studies. We prove it on your own data first.
Example · Field work
Photo inspection app
Staff photograph a site or asset, and the app checks it on the spot, even offline.
Example · Retail & logistics
Shelf and pallet counting
Counts items from photos or video and flags gaps against what your system expects.
How it runs
From sample images to a model in production.
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01Day 0 · 30 min
Audit
A free call about the problem, your data and your constraints. You leave with a ranked plan.
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022 weeks
Prototype
A model trained on a sample of your images, with an accuracy report on the cases that matter.
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03Before go-live
UAT & sign-off
Your team tests it on real cases and we fix what they find. Nothing goes live until you sign off.
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04To ~day 90
Ship
Deployment to your devices or cloud, monitoring, and a loop for labelling new images and retraining.
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05Ongoing
Scale
We extend it, tune it and train your team, then hand it over when you're ready.
Built to be trusted
Accuracy you can check yourself.
Measured on your cases
Accuracy is reported on your own images, hard cases included, not on a public benchmark.
People confirm the edge cases
Low-confidence results go to a reviewer, never straight into your records.
Privacy by design
Faces and number plates can be blurred before anything is stored.
FAQ
Straight answers.
Anything else? Ask us on the audit call, or just email.
How many images do we need?
Often a few hundred labelled examples per class are enough to start, and pre-trained models help us get going with less. We look at your data in the audit and tell you what's realistic.
Can it run without internet?
Yes. Smaller models can run on phones, cameras or edge boxes, and sync once they're back online.
Do you label the data?
We set up the labelling pipeline. We can do the labelling ourselves while your team reviews a sample, or train your staff to do it.
Does it work on video?
Yes. It can detect, track and count objects in live or recorded video.
Show us what your team checks by eye.
A free 30-minute call. You leave with a ranked plan, whether or not you work with us.