Solution
AI Vision Quality Inspection
Cameras and trained models that judge every part at line speed, so defects are caught at the station that produced them.
Catch the defect where it is made, not at final inspection or at the customer.
What this addresses
- Defects found at final inspection after value has already been added
- Sampling misses intermittent faults that only appear at speed
- Inspection consistency drops across shifts and operators
- No usable record of what failed, when, or on which station
What we build
Surface and dimensional checks
Scratches, porosity, burrs, weld quality, print and label verification, presence and absence, dimensional gauging against tolerance.
Line-speed decisions
Inference runs on an industrial edge device beside the line. The verdict reaches the PLC in time to divert the part on the same cycle.
Trained on your parts
Models are built from images of your own production, including the defects your team actually sees, rather than a generic library.
Evidence for every call
Each decision stores the image and the reason, so quality can review borderline cases and argue from the record.
Operator-facing, not a black box
HMI screens show why a part failed. Operators can flag a wrong call, and those corrections feed the next training round.
Technology
- Industrial cameras and controlled lighting
- Edge inference hardware
- PyTorch / ONNX
- Siemens S7-1500 integration
- OPC UA to MES or SCADA
How it integrates
- The vision station is added alongside the existing line, not in place of it
- Reject handling is wired through the PLC so existing safety interlocks are untouched
- Runs locally; a network outage does not stop inspection
Estimate the saving
Enter your own figures. Every assumption behind the result is listed, and nothing is hidden inside the calculation.
Estimated annual saving
₹19.44 L
- Units per year
- 7,20,000
- Defects per year
- 14,400
- Defects caught
- 12,960
Assumptions used
- 1,200 units per shift × 2 shift(s) × 300 days
- 2% of production is currently defective
- The system catches 90% of those defects
- Each defect caught avoids ₹150 of scrap, rework and handling
- Savings are gross: system cost, installation and running cost are not deducted
This is an estimate from the figures you entered, not a quotation. It shows gross savings only — system cost, installation and running cost are not deducted. Real detection rates depend on whether the defect is reliably visible under your production conditions, which is what a proof of concept establishes.
Discuss a proof of conceptCommon questions
What is required for an AI vision proof of concept?
Sample parts covering good production and the defect types you care about, access to the station for mounting and lighting trials, and agreement on what counts as a defect. A proof of concept establishes whether the defect is reliably visible under production conditions before any line changes are committed.
Does production data leave the facility?
Not unless you choose it. Inference runs on an edge device inside the plant. Images and results stay on your network by default. If you want remote dashboards, we agree exactly what is sent and how it is secured before anything is configured.
Start with an assessment
We walk the line with your team and tell you where the largest opportunity is — whether or not that is this solution.