Quality
Inspect every part, not a sample of them
Manual inspection covers what a person has time to look at. When the escape rate matters more than the sampling plan, inspection has to run on every part, at line rate, on the floor.
Symptoms
You'll recognize at least three of these
- Customers are finding defects your final inspection did not
- Inspection is sampled, so the real escape rate is unknown
- Skilled inspectors are spending their shift on parts that are fine
- Defect records live on paper and never reach process engineering
- Rework and scrap costs are rising without an obvious cause
Levers
What we change, in order of payback
Decide at the camera
Detection runs on an industrial edge unit at the cell, so the call is made before the part leaves the station and the reject gate can act on it.
Train on your defects
Models are built from footage of your parts under your lighting, against the defect classes your quality team already uses on paperwork.
Drive equipment you already have
Results go to the existing PLC and reject mechanism rather than requiring new hardware downstream.
Keep the evidence
Every decision is recorded with its image, giving process engineering the defect data that paper records never produced.
100% inspection coverage, a defect rate you can actually measure, and a dataset that tells you where in the process the defects are being introduced.
Engagements
How we deliver it
- 01Physical AI
Edge AI & Computer Vision
Detection, inspection, tracking and safety on-device. Milliseconds, and no network dependency.
- 02Start here
Vision Feasibility Check
Send 30–50 sample images. Get a written memo: accuracy band, hardware class, risks, go or no-go.
- 03Physical AI
Vision Systems Integration
Cameras, optics, lighting, enclosures, and the PLC, SCADA and MES interfaces that make results act.
FAQ
Questions we get
Line rate is a design input, not an outcome — the survey establishes what your process requires and the model is optimised to hold it on the hardware being installed. If the target is not achievable we say so before you commit.
Where defect classes are open-ended, we frame the problem as anomaly detection against known-good rather than classification, which flags the unfamiliar instead of ignoring it.
Start with a call, then a costed plan
Thirty minutes on the problem, the site and the constraints. If it looks like a fit, the next step is a four-week AI Pilot at a fixed price, with acceptance criteria signed before any code is written.