AI Industry Consulting

AI Industry Consulting

Use-cases that move the needle

  • Predictive maintenance & asset health (compressors, pumps, gearboxes)
  • Energy & utilities optimization (boilers, chillers, VFD tuning; IEEE-519 compliant harmonic plans)
  • Process optimization & APC assist (soft sensors, MPC targeting)
  • Quality analytics & traceability (SPC, genealogy)
  • Computer vision (safety PPE checks, barcode/label verification, defect detection)

Architecture & governance

  • Historians/SCADA/DCS integration (OPC UA, Modbus, PROFINET), edge inference
  • MLOps, data catalog, and model governance; cybersecurity aligned to IEC 62443 / ISO 27001
  • Change management: operator adoption, alerts KPIs, and SOPs

Step-by-step

  1. Value discovery (3–5 high-ROI use-cases with business cases)
  2. Data audit (tags, quality, gaps, historian latency)
  3. PoC (8–12 weeks, clear success criteria)
  4. Pilot at scale (one line/area with operator workflows)
  5. Rollout & MLOps (monitoring, drift, retraining, governance)

KPIs we commit to track
Unplanned downtime ↓, energy kWh/ton ↓, first-pass yield ↑, maintenance cost/MTBF ↑, alarms per hour within ISA-18.2 targets.

RFQ checklist
Pain points & target KPIs, data sources (historians/CMMS/ERP), security policies, integration endpoints, budget window, success criteria & timeline.