AI that keeps the line running
Predictive maintenance, OEE intelligence and visual defect detection on one stack — catching equipment failures before they stop the line, with 45% fewer unplanned stops in production.
A Hominis app module · your manufacturing data, app-ified
Keep the line running, shift by shift
Predictive maintenance, live OEE and in-line vision on one stack.
Stop reacting to downtime
Predict equipment failures from sensor telemetry before they halt the line — shifting maintenance from reactive firefighting to a planned schedule.
45% fewer unplanned stops
Make OEE a live signal, not a monthly report
Availability, performance and quality fused into one real-time OEE picture per asset and cell, so losses surface the shift they happen — not in next month's review.
Live OEE per asset
Catch defects the human eye misses
Vision models inspect every part on the line at full speed, flagging surface and dimensional defects in milliseconds and routing the reject before it reaches the next station.
100% in-line inspection
Read what your equipment is telling you
Decades of historical sensor data, SCADA logs and maintenance records folded into one model that explains the degradation pattern behind each alert — not just that something is wrong.
Degradation, explained
Plan the supply chain you actually have
Demand and constraint signals fed into production and replenishment planning, so schedules reflect real capacity and lead times instead of optimistic spreadsheets.
Constraint-aware planning
Bolts onto the plant you already run
The integration and trust choices that moved teams from reactive to proactive.
Brownfield-first: bolts onto SCADA and PLCs you already run
Models ingest streaming telemetry from existing SCADA, historians and PLCs with no rip-and-replace of plant infrastructure — the integration pattern that lets a 30-year-old line get predictive maintenance without a shutdown. Data gravity stays on the floor; the AI comes to it.
Alerts maintenance teams trust enough to act on
Tuned for high detection sensitivity at very low false-positive rates, because an alert nobody believes is worse than no alert — and every flag shows the degradation signature behind it. That trust, not raw accuracy, is what moved teams from reactive to proactive.
One method, tuned for manufacturing
Assess, Transform, Sustain — the cycle every organization runs, dropped one level deeper for your sector's pains and sticky aspects.
Assess
OEE-loss mapping plus failure-mode and data-readiness audit across lines
We map your six big OEE losses against the assets that drive them, then audit telemetry coverage — which machines stream, which are dark, how clean the SCADA and historian data is — and rank predictive-maintenance and quality use cases by downtime cost and feasibility. The output is a ranked roadmap tied to specific lines and failure modes, typically in 4–6 weeks.
Transform
Asset-specific failure models and in-line vision, integrated to the control stack
We build models tuned per equipment type and operating condition — baselines differ across a pump, a press and a flowmeter — and wire them into your SCADA and maintenance workflow so alerts land where technicians already work. Vision inspection is deployed at line speed, with false positives driven down until the floor trusts the signal.
Sustain (AIOps)
Drift retraining against changing tooling and recipes, plus closed-loop OEE
Production conditions move — new tooling, recipe changes, seasonal throughput — so models are monitored for drift and retrained against current operating data, not the day-one baseline. OEE and stop-cause analytics feed back continuously so the system keeps surfacing the next loss to attack as the line evolves.