AI-Powered Portfolio Analysis & Failure Prediction System
Deploys 9 AI agents combining IoT sensor data, digital twin simulations, ML predictions, pattern recognition, financial modeling, and compliance validation to identify at-risk systems and generate optimized schedules..
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Agent Command Center Dashboard
Executive Insights and Risk Assessment
Action Plan with ROI Analysis
AI Analysis Summary with Digital Twin Monitoring
AI Agents
Specialized autonomous agents working in coordination
Lead Analysis Coordinator
Portfolio analysis requires coordinating multiple AI capabilities across large datasets.
Core Logic
Receives mission configurations, decomposes into parallel workstreams, assigns tasks to 9 agents, manages communication, tracks progress, synthesizes findings into executive reports with recommendations.
Real-Time Sensor Intelligence
Periodic inspections miss problems developing between visits; real-time data enables intervention.
Core Logic
Streams telemetry from hundreds of sensors at 1Hz. Applies edge analytics for anomaly detection, correlates with baselines, generates autonomous alerts, packages enriched data for downstream agents.
Virtual Asset Modeling Specialist
Understanding future equipment behavior requires simulation accounting for degradation and usage.
Core Logic
Maintains virtual replicas synchronized with IoT data. Runs physics-based degradation simulations, predicts remaining useful life, simulates failure scenarios, identifies optimal intervention timing with 83%+ confidence.
Statistical Analysis Specialist
Raw data contains noise, missing values, and hidden patterns requiring statistical processing.
Core Logic
Ingests from multiple sources, validates integrity (99.7%+), applies transformations, detects anomalies, generates ARIMA forecasts, identifies outliers, produces enriched datasets for downstream agents.
Weibull Reliability & Risk Specialist
Not all at-risk systems have equal failure probability or consequences; quantification enables prioritization.
Core Logic
Applies Weibull analysis for time-to-failure curves, calculates reliability at 30/60/90 days, runs 10,000+ Monte Carlo iterations for cost uncertainty, quantifies preventive vs reactive value with confidence intervals.
Pattern Recognition & Correlation Specialist
Individual failures may be symptoms of larger patterns like defective batches or installation issues.
Core Logic
Applies K-means clustering with silhouette scoring, identifies common characteristics, searches historical databases, discovers root causes like defective components. Single discovery prevents dozens of failures.
Cost-Benefit Analysis Specialist
Maintenance decisions require financial justification comparing preventive vs reactive costs.
Core Logic
Builds cost models for preventive vs reactive scenarios. Calculates ROI, NPV, payback periods, IRR. Factors bulk discounts, avoided downtime, insurance impacts for executive business cases.
Regulatory Validation Specialist
Maintenance programs must align with regulations; non-compliance carries penalties exceeding costs.
Core Logic
Validates against NFPA 25, EN 12845, NF S 61-937, APSAD standards. Identifies non-compliant systems, flags deadlines, validates proposed programs, produces audit-ready documentation.
Sustainability & Governance Specialist
Enterprises must track ESG metrics; maintenance has environmental impacts requiring optimization.
Core Logic
Calculates carbon footprint (1,000+ tCO2e/year), tracks water consumption, monitors safety metrics. Projects 15%+ carbon reduction, generates GRI/SASB/TCFD reports, produces ESG scores (A+ to F).
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
1 technologies
Architecture Diagram
System flow visualization