Solution

AI Vision Quality Control

Inspection at line speed, with records that survive an audit.

Vision inspection is one of the oldest applied machine learning use cases and still one of the most frequently botched, usually because the defect image set is too small or too clean. The hard part is not the model. It is assembling a representative set of the defects that actually occur, including the rare ones that matter most.

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Why this is usually broken

Defect image sets are small, imbalanced and missing the rare failures that cost the most
Lighting and fixturing variation degrades models that tested well in a lab
Inspection decisions are quality records and need traceability most vision systems do not produce
A model that drifts silently passes defective product, which is the worst possible failure mode

How we build it

Defect set built deliberately

We work through your scrap and return history to assemble a representative defect set before any modelling, including deliberately produced samples of rare classes.

Robust to line conditions

Trained and validated across the lighting and fixturing variation the line actually shows, not a controlled sample.

Inspection records as output

Every decision recorded with the image, model version and result, so the inspection is traceable under your quality system.

Drift monitoring

Continuous monitoring against sampled human inspection, because silent degradation is the failure that matters here.

What changes

Line speed Inspection without slowing the line

Every unit Rather than a sample

Traceable Image, version and result recorded

Monitored Drift caught against human sampling

Figures are drawn from Senteras engagements and are illustrative of typical results. Outcomes vary by data quality, infrastructure and scope.

Common questions

What if we have very few defect examples?

That is the normal starting position. Anomaly detection against known-good product is often the right first approach, moving to classification once enough defect examples have accumulated. Synthetic augmentation helps but does not substitute for real rare-class examples.

Where we deploy this

Manufacturing & Supply Chain

Reduce downtime, improve quality, and build a more resilient supply chain with AI.

Energy & Oil and Gas

Subsurface, reliability and land data intelligence, without sending proprietary data offsite.

Find out what this looks like for your organisation

A 30-minute call. We will tell you plainly whether AI is the right tool for the problem you have, including when it is not.