Manufacturing AI Solutions

Start where downtime or scrap cost is visible and a single line/asset class has workable data — often predictive maintenance or inspection. Avoid enterprise 'smart factory' programmes that skip a proving pilot.

Yes. We integrate with common MES/SCADA/historian stacks rather than replacing them. AI value is in decisions and closed loops on top of OT systems you already run.

Most plant pilots run 12–20 weeks including data access, model build, operator workflow, and KPI measurement. Multi-plant scale-out follows a replication playbook after pilot proof.

Single-line or single-asset-class pilots commonly range from $90,000 to $250,000. Vision hardware, multi-plant rollout, and deep OT integration expand scope and are priced explicitly.

Yes. Latency-critical and OT-isolated deployments run on plant servers or industrial PCs, with optional cloud for training and fleet analytics.

We design collection and inference with network zoning, least privilege, and minimal bidirectional traffic — reviewed with your OT security stakeholders before production data paths open.

No. Many PdM and yield use cases are sensor and event driven. Vision is chosen when the defect or event is visual. We separate feasibility so you do not buy cameras for a vibration problem.

Thresholds, alert text, and acknowledge flows are designed with supervisors and technicians. If operators do not trust or cannot act on alerts, we treat that as a delivery failure — not a training footnote.

Useful when prioritising across plants or when data access/security posture is unclear. If a specific line problem is already sponsored, we can scope that pilot directly.

Manufacturing AI delivery assumes shift work, OT constraints, and maintenance/quality systems of action. Notebook insights without work-order closure are not considered done.

Most of our work is brownfield. We plan for uneven sensor coverage, gateway constraints, and phased instrumentation rather than assuming greenfield IoT everywhere.