Solution 04

Computer vision inspection

Consistent visual inspection on the line, running at the edge so a network outage never stops production.

Outcome

Consistent QA coverage

Outcome

Edge deployment

Outcome

Retraining loop from rejects

Inside the solution

Trained on your product set

Defect classes defined with your quality team, using your own good and reject samples.

Inference runs on the line, not in the cloud, so latency and connectivity never gate throughput.

Verdicts trigger rejection gates and land in your MES with the image attached for traceability.

Operator overrides become labelled data, so accuracy improves with use instead of decaying.

We specify the camera, optics and lighting, because that is where most vision projects actually fail.

Defect rates by shift, line, batch and supplier, which usually pays for the project on its own.

Scope of delivery

Indicative timeline

4 weeks feasibility, then 10-14 weeks to a production line, per station.

Questions we are asked

How many sample images do you need?: For a first model, a few hundred good samples and as many rejects as you have, per defect class. We can augment scarce defect classes, and the feasibility study tells you where the data is too thin.
What accuracy is realistic?: For well-defined surface and assembly defects, above 98% detection with a low false-reject rate is a common target. Subtle cosmetic classes need more data and honest thresholds.
Does it work with our existing cameras?: Sometimes. Resolution, frame rate and lighting stability decide it, and the feasibility study answers this before you spend on hardware.
What happens if the network goes down?: Nothing stops. Inference is local; results queue and sync when connectivity returns.

Invoices, claims, purchase orders and contracts read, validated and pushed into your ERP with a review queue for edge cases.

A grounded assistant for voice and chat that drafts replies, resolves routine tickets and escalates cleanly with full context.

Forecasting and anomaly detection over sensor, sales or logistics data so teams act before an exception becomes a cost.