AI quality control that catches what the tired inspector misses.
Manual visual inspection at the end of a production line has a known failure rate — and the failure rate is higher in the third shift than the first. AI vision systems hold the line consistently, log every check, and surface defect patterns that humans miss across thousands of units.
Manufacturers tired of QC variance between shifts and inspectors.
Manual visual QC is good. Manual visual QC at 11pm in the third shift, after the lead inspector has called in sick, with two trainees on the line — that is where defects ship. AI vision systems hold a consistent standard 24/7, log every check for audit, and flag patterns no human could see across thousands of units.
- Defect rates spiking on certain shifts and nobody knowing why
- Customer complaints about issues that QC supposedly caught — but the paper records don't show it clearly
- Inspectors burning out from end-of-line visual checks that all look alike after hour 6
- Production tags and labels printed wrong, caught only when the customer rejects the shipment
- PPE compliance on the line tracked by supervisor walk-around — inconsistent at best
- Machine downtime that could have been prevented with earlier signal — but the signal was never captured
AI vision applications shipped on real factory floors.
We build manufacturing AI that runs on-prem, integrates with your existing line, and produces results inspectors trust. The patterns below are deployed, not lab demos.
Surface defect detection
Scratches, dents, discolouration, contamination, missing components. Trained on your product's specific defect taxonomy. Configurable accept/reject thresholds.
OCR on production tags
Read printed batch numbers, expiry dates, lot codes, serial numbers on labels and packaging. Validate against the production order. Catch print errors before they leave the line.
Pre-shipment label verification
Final-station verification of carton labels, shipping marks, regulatory marks. Catch wrong-label-on-right-product errors that recall investigations later.
PPE compliance via existing CCTV
Helmet, vest, mask detection in production zones. Non-compliance events logged with timestamp and clip. Audit trail for safety regulators and your insurance renewals.
Predictive maintenance signal
Machine sensor data + ML to predict failures before they cause line stoppage. We have cut unplanned downtime by 30–50% in pilot deployments.
Headcount & productivity zones
Track shift attendance, zone-based productivity, dwell time in workstations — useful for capacity planning and operational improvement.
PoC fast. Production deliberately.
2–4 week proof of concept
We deploy a PoC on a sample of your defects within 2–4 weeks of project start. You see the actual model performance on your actual products before committing to production deployment. If accuracy is not good enough, you have not committed to the production phase yet.
Production-grade integration
Production deployment includes line integration (camera mounts, lighting, PLC tie-in), operator UI on shop-floor tablets, escalation workflow to QC supervisors, audit logging, and ongoing model retraining as new defect types emerge. We do not ship PoCs as production systems.
Manufacturing sub-sectors we have shipped for.
Direct answers.
How long until we see results?
Working PoC on your defect data in 2–4 weeks. Production deployment with line integration, operator UI, and audit logging adds 6–12 weeks depending on integration complexity. We do not believe in 6-month research projects with nothing to show for them.
Can the AI run on-prem without cloud?
Yes — and we usually deploy that way for manufacturing. Models run on a small appliance at your site. No video or proprietary product data leaves the factory floor.
What if our defect types change over time?
Models can be retrained as new defect types emerge. We typically include quarterly retraining cycles in the support retainer. Operators can flag false positives / false negatives in the UI, which feed back into the retraining set.
How accurate is AI defect detection compared to human inspectors?
On well-controlled lines with consistent product appearance, AI typically achieves 95-99% recall (catching real defects) and reduces false positives over time. The bigger win is consistency — AI does not get tired, distracted, or off-shift.
Will it integrate with our existing MES or ERP?
Yes — defect events feed into your MES (Wonderware, Ignition, custom) and ERP (SAP, Oracle, custom). Bi-directional integration so production data feeds the model and the model feeds back to traceability records.
Quality issues you cannot quite get on top of?
30-minute call. We will tell you honestly whether AI vision is the right answer or whether process changes would solve more cheaply.