Automated visual inspection that catches the defects manual QC misses.
97%+ defect detection accuracy, sub-50ms edge inference, deploys in under 4 weeks - purpose-built for production-line speed.
- Free 90-day pilot
- No annual commitment
- Data stays on-prem
Book your walkthrough
20-min live walkthrough on your defect type. No slides. Praveena and Vaishnavi run the call.
Built for production-line visual inspection across automotive, electronics, pharma, and food & packaging.
The intelligent eye, engineered for production-line speed.
PrismIQ packages the camera, edge-inference compute, model library, and PLC integration kit your line needs — pre-tuned to your defect taxonomy and ready to drop in.
Watch a PCB defect get caught in real time
The 3D walkthrough illustrates how Opsio inspects PCB solder defects. The same model architecture catches defects across every discrete-manufacturing line — you bring the defect type, we train and deploy.
- ✓PCBs & semiconductors — solder, polarity, missing parts
- ✓Automotive & aerospace — welds, surface, dimensional
- ✓Pharma & medical — tablets, blisters, labels
- ✓Food & packaging — seals, fill, foreign objects
Manual QC is leaking defects, time, and margin
Three line-killers that don't show up cleanly on any dashboard — but together they're costing 8–15% of revenue at the average discrete-manufacturing plant.
Manual inspection tops out at 80–85% accuracy. Subtle scratches, weak welds, and contamination slip through to your customer.
Headcount math gets worse every quarter — and good inspectors are hard to recruit, harder to retain, and fatigue-prone by design.
Most plants don't even know their true rework rate — by the time it shows up in the P&L, three months of margin is gone.
Trained on your defect types - not generic datasets
Surface, dimensional, and weld inspection at 100+ parts per minute
Detect scratches, dents, porosity, dimensional drift, and welding defects on engine components, body panels, fasteners, and aerospace assemblies. IATF 16949-ready audit trails. Trusted by Tier-1 suppliers serving global OEMs.
Three steps: Capture · Inspect · Act
From rig design to edge-deployed pass/fail signals — engineered for your line, not a generic template.
We design the camera and lighting stack for your line - area-scan, line-scan, hyperspectral, or 3D and integrate the rig into your existing conveyor or fixture. No line redesign required.
Deep learning models trained on YOUR defect classes, CNNs and vision transformers fine-tuned on images we collect from your actual production, not generic datasets. No internal labeling team required.
Pass/fail signals to your PLC, real-time dashboards for quality leads, and full traceability logs for audits. Edge-deployed on NVIDIA Jetson or Intel OpenVINO — no cloud dependency.
What customers measure after they go live
Every number below is measured on real production lines, post-deployment. Ranges are conservative — your line may exceed them.
Measured on customer production lines.
Tuned per defect class to minimise unnecessary scrap and operator overrides.
2,000–5,000 parts/hr per inspection station.
Reduced scrap + fewer customer returns + inspector redeployment.
92% fewer defect escapes - in the first 90 days
Three shifts of manual visual inspection on a high-volume body-panel line, replaced with an Opsio edge inspection system trained on the customer's exact defect taxonomy in under four weeks. PPM escapes to the OEM dropped 92% in the first quarter and the inspection bottleneck that had been capping line throughput disappeared.
Everything a QE or plant manager will ask about
Six things every QE and plant manager asks before signing off a pilot scope. Here's what's in the box.
Pass/fail signals slot in next to your existing automation — no rip-and-replace.
Sub-50ms inference at the line with no internet dependency — or run cloud-side for cross-line analytics.
New defect class? Add 50–200 labelled examples and ship. Active learning surfaces the images that move accuracy.
Tamper-evident logs, electronic signatures, and image archives meeting the common regulated-industry standards.
Quality engineers label defects in a browser. No Python, no labelling vendor. Your domain experts stay in the loop.
Production-line downtime SLA, dedicated engineering POC, model performance monitoring with proactive alerts.
The questions QEs and plant managers actually ask
Will it integrate with our existing PLCs / MES / SCADA?+
How long does deployment actually take?+
What happens with edge-case defects we haven't seen before?+
Do we need to label thousands of images?+
How is this different from Cognex / Keyence?+
What's the pricing model?+
Is data processed on-prem or in the cloud?+
What happens if the system goes down?+
Stop shipping defects. Start measuring them.
20-min live walkthrough on your defect type. No slides, no scripted pitch — just the system working on a reference line.
20 min · Bring your defect type · No slides