Manufacturing
AI that operates inside the realities of production environments.
Manufacturing is the highest-stakes deployment surface for AI we work on. Latency, reliability, and integration with PLC/SCADA systems are non-negotiable. Models can't just be accurate — they have to fit the operational constraints of a working facility.
Where AI fits
High-leverage use cases in this industry.
01
Computer vision quality control
Defect detection on production lines, at line-rate throughput, with on-prem deployment. Calibrated against your QA team's labeling, not internet datasets.
02
Predictive maintenance
Sensor data into forecasts of equipment health, calibrated to your maintenance schedule and spare-parts logistics. Reduces both downtime and unnecessary preventive replacements.
03
Demand & production planning
Forecasts that respect line constraints, supplier lead times, and inventory holding costs. Output usable by your planners — not abstract.
04
Knowledge systems for technicians
RAG over equipment manuals, SOPs, and historical incident reports — surfaced at the workstation, in the language your operators use.
How we approach this industry
A pattern that respects manufacturing realities.
- Assess01
Industry-aware discovery
Two-week sprint scoped to your operational reality — regulatory, OT/IT, integration constraints baked in from day one, not discovered mid-build.
- Design02
Reference architecture
Topology that fits your facility, your cloud, your compliance. On-prem, hybrid, or cloud — the call is documented before we start building.
- Build03
Pilot on one site / line / product
Working system on one part of your operation. Measurement window with explicit success criteria. Go/no-go decision before scaling.
- Operate04
Roll out & operate
Multi-site rollout if pilot succeeds. Central observability, drift detection, runbook. Optional retainer for ongoing operations.
Frequently asked
Industry-specific questions.
Will this work in our plant without giving the AI access to the open internet?
Yes — fully air-gapped deployment is supported. Model updates ship via signed bundles through your existing change-control process. Observability exports to your local infrastructure. No outbound connectivity required.
How does it integrate with our existing SCADA / PLC / MES stack?
Via standard industrial protocols (OPC UA, Modbus, MQTT). For legacy or proprietary interfaces, we build adapters during discovery, with sign-off from your OT team before write actions are enabled.
Who labels the defects? Our QA team is already overloaded.
Initial labeling is a structured 1–2 week effort, usually shared between your QA team (provides examples and edge cases) and us (does the bulk labeling work using your provided guidance). Active learning then keeps the labeling burden low as the model improves.
How long until we see ROI?
Realistic timeline: 4–5 months from kickoff to a working pilot on one line, with measurement window. ROI typically clear by month 6–8 if the use case fits. We're explicit during discovery when ROI math doesn't justify the build.