# Multi-Modal Anomaly Detection The defensible AI play in F&G safety. Combines multiple data streams to eliminate false alarms and predict failures. ## Architecture 1. **Computer vision** on existing CCTV infrastructure 2. **IoT sensors**: heat, gas, vibration, pressure, flow 3. **Time-series models** trained on facility-specific baselines 4. **BMS/SCADA integration** for automated response ## Why It's Defensible Facility-specific learning creates [[Switching Costs]]: - Each plant has unique "normal" signatures - Models improve with operational history - Retraining from scratch = months of baseline collection This is [[Embedding]] into operations. Hard to rip out once calibrated. ## What It Solves - Steam vs smoke discrimination - Flaring vs fire detection - Equipment heat vs thermal anomaly - Sensor drift compensation - Predictive failure detection (2-4 weeks ahead) ## Revenue Model SaaS per facility + % of downtime prevented. Outcome-based pricing aligns incentives: customer pays when you save them from the $84M/year [[False Alarm Problem in F&G]]. ## Links - [[F&G Safety Opportunity MOC]] - [[Defensibility Principles MOC]] - [[Predictive Maintenance in O&G]] - [[False Alarm Problem in F&G]] --- #ai #technology #defensibility