Reactive maintenance is expensive. Predictive maintenance is hard to operationalize. We bridge the gap with models that integrate with your existing reliability programmes.
Incident reports, audits, and compliance documentation eat into operational hours. We automate the drafting, classification, and routing. Humans stay on the call where it matters.
Operations and finance still wait on spreadsheets and PDFs. We replace the reporting layer with AI-generated, auditable, on-demand reports drawn from authoritative source systems.
Capital projects get paused but operational efficiency demands grow. AI delivers efficiency without large capex commitments. Pilots scale only when the business case is proved.
Time-series forecasting and anomaly detection on equipment sensor data. Integrated with your CMMS for actionable maintenance triggers.
Grounded copilots that summarize alarms, suggest playbook actions, and surface historical context for operators. No autonomous decisions.
Voice and text capture from field teams, automatically structured into reports and routed to the right systems. Hours back per shift.
AI agents that read from and write to SAP, with proper integration and audit trails. No screen-scraping, no shadow data.
Forecasting and scenario modeling for trading and hedging desks. Calibrated to your existing risk frameworks.
Yes. Native integration via documented APIs for SAP, OSIsoft PI, Aveva, GE iFix, and similar industrial platforms. We use the integration layer your platform team is already maintaining. No parallel infrastructure.
No. Every AI we deploy in operations is advisory by default. We surface information and suggested actions; humans operate. Closed-loop control needs explicit safety approval and existing process automation.
Pilot results in three to six months on a single asset class. Operational ROI from reduced downtime typically takes six to twelve months, depending on existing maintenance data quality. We scope honestly upfront on data readiness.