Industrial AI Platforms
AI systems for industrial settings — operating in your facilities, on your data, under your constraints.
AI built for the realities of manufacturing, logistics, and operational environments. Edge constraints, deterministic SLAs, regulatory considerations — handled from day one.
What we ship
Concrete deliverables, not features.
01
Computer vision for production lines
Quality control, defect detection, asset tracking. On-prem or hybrid deployment, integrated with your PLC/SCADA stack, calibrated against your QA team's labeling — not internet datasets.
02
Forecasting & planning systems
Demand forecasting, predictive maintenance, capacity planning. Models that respect operational constraints (lead times, line capacity, regulatory windows), not academic benchmarks.
03
On-prem & hybrid deployment topology
AI systems that run in your facility, your cloud, or a hybrid — your call, fully documented before we start. Edge inference where latency matters, cloud training where compute matters.
04
Long-term operations handover
Industrial AI lives for years. We design for monitoring, drift detection, retraining cadence, and clear handover to your engineering team — including documentation auditors actually read.
How it works
Discovery first. Weekly cycles. Pause anytime.
- Discover01
Discovery sprint
Two weeks, fixed scope. Working prototype on your data, architecture decision record, scoped follow-on proposal.
- Design02
Build & deploy on-prem
Weekly cycles with demos and decision points. Scope can be adjusted, paused, or ended after any cycle. No surprises at the end.
- Build03
Ship to production
Code in your repositories. Deployment in your cloud by default. Runbook written for your team to operate, monitor, and extend it.
- Operate04
Optional retainer
Monthly retainer to maintain, monitor, and extend the system. Quarterly review against business outcomes. No auto-renew.
Where it compounds
Scenarios where this work pays back.
01 · Use case
Visual QC at line speed
A 3-line plant inspecting at full throughput catches defects that manual sampling misses. Plus auto-classification of defect modes feeding back into upstream process control.
Typical: 2–4× defect detection rate vs manual sampling, with first-pass yield up 1–3 percentage points.
02 · Use case
Predictive maintenance on critical assets
Sensor data into forecasts of equipment health, calibrated to your maintenance schedule and spare-parts logistics. Reduces unplanned downtime AND unnecessary preventive replacements.
Typical: unplanned downtime down 20–35%, preventive maintenance cycles extended 15–25% where safe.
03 · Use case
Multi-site rollout with central operations
AI deployed across 5–50+ facilities, centrally monitored, locally tuned. Drift detection per site, model versioning per line, audit trail per inspection.
Typical: single ops team can supervise 20+ sites with proper observability and alerting.
Engagement
Fixed scope after discovery.
- Duration
- 4–9 months
- What you get
- Production platform, on-prem or hybrid deployment, integration with operational systems
- Start with
- Discovery sprint, 2 weeks
Frequently asked
Questions before the first call.
Can you deploy on-prem with no internet connection?
Yes. We've shipped systems to facilities with strict OT/IT separation and no outbound connectivity. Model updates ship via signed bundles through your established change-control process. Observability is exported to your local infrastructure.
How does this integrate with our existing PLC/SCADA/MES stack?
Via standard industrial protocols (OPC UA, Modbus, MQTT) and custom interfaces where needed. Integration is usually 30–50% of project effort and we scope it explicitly during discovery — including a sign-off from your OT team.
What happens when the model drifts or a line changes?
Drift detection is built into the system, with alerts on defined thresholds. Retraining cadence is part of the runbook — sometimes scheduled, sometimes event-triggered. Your team can retrain themselves with the documented pipeline, or we handle it via retainer.
What's a realistic timeline from kickoff to a line running on the system?
Discovery sprint (2 weeks) → pilot on one line (8–12 weeks) → measurement window (4–6 weeks) → rollout decision. So 4–5 months to a working pilot, full multi-line rollout takes another 3–6 months depending on facility access.