Manufacturing
Turn shop-floor and ERP data into agentic systems that predict failures, tighten quality, and keep production flowing — from the line to the supply chain.
Manufacturers sit on decades of sensor, MES, and ERP data that rarely translates into faster, better decisions on the floor. We build AI systems that predict equipment failures before they stop the line, catch defects in real time, and give planners and technicians grounded answers from tribal knowledge and manuals. The result is higher uptime, tighter quality, and decisions that used to wait on a specialist.
Problems we solve
The operational bottlenecks that hold enterprises back — and where AI delivers measurable impact.
Unplanned downtime destroys throughput
A single unexpected line stoppage cascades across shifts and orders. Reactive maintenance means failures are found after they cost production, not before.
Quality inspection cannot keep pace
Manual and sampled inspection misses defects, and escapes reach customers. Root-cause analysis after a recall is slow and expensive.
Expertise is retiring off the floor
The technicians who know how to diagnose a machine by sound are retiring, and their knowledge is not captured anywhere a new hire can reach.
Planning and data live in silos
MES, ERP, and quality systems do not talk, so planners reconcile spreadsheets and react to disruptions instead of anticipating them.
What we build
Production-grade capabilities, engineered for enterprise scale, security, and reliability.
Predictive maintenance
Models learn the signatures of impending failure from sensor and historian data, alerting maintenance to intervene during planned windows instead of after a breakdown.
AI visual quality inspection
Vision models inspect every unit on the line in real time, catching defects earlier and feeding classified defect data straight into root-cause analysis.
Maintenance knowledge agents
A RAG-powered assistant answers technician questions from manuals, historical work orders, and captured expert know-how, cutting diagnosis and repair time.
Production planning intelligence
Agents integrate MES and ERP signals to recommend schedule adjustments when demand, supply, or machine availability shifts, with explainable trade-offs.
Supply chain and inventory optimization
AI forecasts demand and lead-time risk, flags shortages before they hit the line, and recommends reorder and buffer actions grounded in your data.
OT-safe deployment
Systems run on-premise or at the edge alongside OT networks, respecting air-gaps and safety requirements, with no dependency on shipping shop-floor data to the cloud.
Why it matters
- Fewer unplanned line stoppages
- Defects caught before they leave the plant
- Expert knowledge available to every technician
- Higher overall equipment effectiveness (OEE)
- Faster root-cause on quality escapes
- Planners ahead of disruptions, not behind them
Implementation roadmap
Discovery & data assessment
We audit sensor, MES, and ERP data availability, pick a high-loss line or asset class, and define OEE and downtime baselines with plant and OT leadership.
Pilot on one line
We connect to your historian and MES, deploy predictive or vision models on a single line at the edge, and validate alerts against real failures and defects.
Production deployment
We integrate alerts into your maintenance and quality workflows, harden edge deployment within OT constraints, and run under agreed reliability targets.
Scale across plants
We roll out across lines and facilities, standardize the data pipeline, and hand over monitoring so your engineering teams own and extend the models.
Common questions
No. We deploy at the edge or on-premise alongside your OT network, so sensor and production data stays inside the plant. Cloud is optional and used only for aggregated, non-sensitive analytics if you choose.
Yes. We integrate with systems like SAP, Ignition, OSIsoft/AVEVA PI, and major MES platforms through their APIs and standard OT protocols, layering intelligence on top rather than replacing your systems.
It varies by asset, but a meaningful history of sensor readings with some labeled failure events accelerates results. Where data is thin, we start with anomaly detection and improve models as more events are captured.
We deploy in a read-oriented, non-intrusive way that respects air-gaps and functional-safety boundaries. The AI advises maintenance and quality teams; it does not directly actuate control systems unless you explicitly scope and gate that.
A single-line pilot typically demonstrates measurable downtime or defect reduction within a couple of months, giving you a provable ROI before scaling across plants.