Priced per camera. Paid back in scrap.
From $200 per camera per month, or per line with root cause and process integration. No integrator days, no rules engineer, no capex approval cycle.
Per camera
Up to $800 depending on resolution and frame rate.
- Few-shot teaching, unlimited part numbers
- Zero-shot anomaly detection & localisation
- Inspection log, exports and alerts
- Edge appliance included on pilot
- PLC verdict output over OPC-UA
Per line
Most pilotsAll cameras on a line, root cause and process integration.
- Everything in Per camera
- Root-cause correlation across line, shift, tool, lot
- Closed-loop signals to MES, SCADA, historian
- Fleet model management & staged rollouts
- Named engineer during pilot
Plant & enterprise
Air-gapped deployment, validation support and SLAs.
- Everything in Per line
- Air-gapped and validated environments
- Audit-grade verdict and frame-hash logging
- Cross-site defect intelligence
- Uptime and response SLAs
Pilot pricing and volume discounts available. Camera pricing scales with resolution and throughput; we'll quote after a 20-minute line review.
How a pilot pays for itself.
We don't ask you to model a five-year ROI. We ask for one camera, one line and a few weeks.
- Week 0 — Line reviewA 20-minute call to pick the line, the camera position and the part with the highest scrap or claim cost.
- Week 1 — Teach and deployEdge appliance arrives, camera mounts, a dozen good samples are captured. The model is inspecting the same day.
- Weeks 2–4 — Baseline and liftOnqyra runs alongside your current inspection. You see what it catches that humans or rules missed, and where the defects cluster.
- Week 4+ — Close the loopRoot-cause signals go to MES and the PLC. Scrap and rework move; you decide whether to extend to the next camera or the next line.
The second shift never gets tired. Neither should your inspection.
Human inspection fatigues. Rules-based machine vision fossilises. Onqyra learns.
| Human inspection | Rules-based machine vision | Onqyra | |
|---|---|---|---|
| Setup for a new part number | Train an inspector, hope they retain it | A rules engineer, per SKU, per change | A dozen good samples. Minutes. |
| Catches defect types nobody defined | Sometimes — if they're looking | Only what was explicitly programmed | Zero-shot anomaly detection |
| Consistency across a shift | ~70–80% catch rate, fades within hours | Consistent, until the product changes | Every part, every shift, same eyes |
| Survives a product change | Retraining and a learning curve | Breaks. Re-engineer the rules. | Re-teach from new good samples |
| Explains the root cause | Anecdotal, on a good day | Pass/fail only | Correlated to line, tool, shift, lot |
| Cost model for a mid-sized plant | Headcount per line per shift | Capex plus integrator days | From $200 per camera per month |
Questions from the plant floor.
The ones quality managers and process engineers ask first.
A handful of good parts — typically a dozen — captured on your own line with your own lighting. Onqyra learns a self-supervised representation of “normal” for that part and flags deviations. You never need a labelled defect library, and you can add named defect classes later as they appear.
Put an inspector on every line that never blinks.
Pilots start with one camera and a dozen good parts. Most plants see scrap move within the first weeks.