Expert-in-the-Loop AI for Integrity Operating Windows (IOW)

The purpose of this offering is to help Oil & Gas customers get started with or extend their use of Microsoft Azure by integrating real-time operational data from PI Systems, DCS, APM, and CMMS platforms into Azure services such as Azure Data Factory, Azure Synapse, and Azure Machine Learning—enabling intelligent decision support and AI-powered refinery optimization.

In today’s Oil & Gas industry, the retirement of experienced operators and a shrinking talent pipeline are creating critical knowledge gaps that threaten refinery performance, safety, and continuity. Traditional digital systems lack the contextual intelligence to guide frontline teams through complex operational decisions. This offering addresses that challenge head-on by embedding expert knowledge into intelligent systems that empower every shift, every operator, and every decision.

Core Capability: AI-driven decision support for Integrity Operating Window (IOW) management

Key Benefits: • Reduces unplanned downtime by up to 25% • Cuts equipment failures by 40% • Speeds up operator training by 30% • Achieves 3x–10x ROI within 18–24 months

System Integrations: OSIsoft PI, GE APM, DCS (Emerson, Honeywell, ABB), SAP PM, IBM Maximo

Customer-Centric Value Proposition: Transform refinery operations by embedding decades of expert insight into every shift. People Tech Group’s Expert-in-the-Loop (EITL) AI for Integrity Operating Windows (IOW) empowers Oil & Gas operators with intelligent decision support that enhances asset reliability, reduces unplanned downtime, and accelerates workforce development. Seamlessly integrating with existing PI Systems, APM, DCS, and CMMS platforms, our EITL solution learns from your best operators—preserving institutional knowledge, guiding real-time decisions, and ensuring operational continuity amid workforce transitions. Reduce failures by up to 40% and achieve 3x–10x ROI in under 24 months.

Implementation Strategy:

Phase 0: Discovery & Planning (Weeks 1–2) -Conduct site assessment -Align with key stakeholders -Inventory all relevant data sources (PI Historian, APM, CMMS, etc.)

Phase 1: Data Integration Setup (Weeks 3–6) -Establish data pipelines from historian, DCS, APM, and CMMS systems -Validate data quality and perform tagging

Phase 2: Pilot Implementation (Weeks 7–12) -Deploy Expert-in-the-Loop (EITL) AI on one critical unit -Start capturing operator decisions and interactions -Generate and analyze initial operational insights

Phase 3: Model Training & Feedback Loop (Weeks 13–20) -Train AI models based on real operator actions -Activate real-time AI recommendations -Conduct feedback sessions with operators and engineers

Phase 4: Expansion (Months 6–9) -Scale EITL AI across multiple operational units -Customize AI recommendations for specific scenarios and shift patterns

Phase 5: Facility-Wide Deployment (Months 9–12) -Fully integrate the solution across the entire refinery -Train junior operators using AI-guided mentorship and decision support

Phase 6: Optimization & ROI Measurement (Months 12–18) -Continuously monitor key performance indicators (KPIs) such as downtime, failure rates, and training time -Refine and improve AI logic using updated operational data