Manufacturing
90-day quick wins for quality, maintenance, and safety. No stack replacement required.
Manufacturing AI from Allerin is in-line defect detection, predictive maintenance and worker safety analytics that run on the cameras and sensors already on your line, judged by scrap rate, first-pass yield and breakdowns rather than by a demo.
Who is this for?
Plant operations managers who own throughput and downtime, quality managers who answer for scrap and first-pass yield, and the engineering leads who have to make any new system live beside the MES. It is built for plants that run industrial cameras (GigE, USB3) or can add them on a line. It is not for teams that want a dashboard without changing what happens on the floor.
What problem does it solve?
Most defect escapes are not mysteries. The line produced them in plain sight; nobody was looking at that frame at that second. Manual inspection catches what a tired eye catches, and the trend by SKU or by shift only shows up in the monthly scrap number, long after the cause has moved on.
Maintenance has the same shape. A bearing that is going to fail gives weeks of warning in vibration and heat, but the warning lives in a sensor nobody trends, so the failure arrives as an emergency stop at the worst possible hour. And safety incidents get reviewed from memory, because the footage was never flagged, never clipped, and is gone by the time anyone asks.
How does it work?
QualiZer watches the line through industrial cameras and classifies each unit as it passes, holding or alerting the MES when a defect crosses the threshold you set. Every decision is explainable: the reviewer sees the frame, the region and the reason, and can overrule it. Trends are kept by SKU and by shift, which is where the real causes usually hide.
VibraQore fuses vibration, current and thermal signals with a visual baseline to catch bearing wear and imbalance early, and turns the alert into a planned intervention through your CMMS instead of an unplanned stop. SiteSentra runs on the same edge hardware for PPE and zone compliance, flags near misses, and keeps the clip as evidence for training.
All of it is gated. We agree the KPI before the pilot (scrap rate, first-pass yield, cycle time, breakdowns), track it on live dashboards, and roll back if it does not move. A model that cannot show its number on your line does not get to stay.
Quick wins
- In-line defect detection (QualiZer)
- Predictive maintenance on motors/pumps (VibraQore)
- Worker safety analytics (SiteSentra)
Capabilities
- CV
- Analytics
Solutions for Manufacturing
What does it connect to?
- Industrial cameras (GigE, USB3) and thermal sensors, usually the ones already on the line
- MES and ERP through REST APIs, for holds, alerts and trend data
- CMMS hooks for maintenance work orders
- Vibration sensors for VibraQore baselines
How is it deployed, and how long does it take?
Inference runs on Jetson or x86 edge devices rated for the plant floor, so a line decision never waits on a network. Cloud is optional and used for model updates and cross-line analytics; your images and sensor data stay under your control.
Starting data is modest: sample images of good and bad parts for defect detection, breakdown logs and sensor baselines for maintenance. We scope exactly what is needed during the proof of concept rather than asking for a data lake.
Proof of concept to pilot in 8 to 12 weeks with KPI gates and a reversible rollout. Most plants see a measurable return within 3 to 6 months of the pilot.
Related products and services
- SENTRA
Edge analytics for real-time inference at line speed
- NeuroSight
Safety monitoring with PPE detection and zone compliance
- VISTA
Redaction for training clips and incident review