Critical Infrastructure Secured with Predictive Intelligence
Industry Focus: Energy Distribution, Utilities & National Infrastructure
Aging pipeline infrastructure creating catastrophic failure risks. Legacy monitoring systems offered delayed, disconnected insights without proactive intervention capabilities.
Next-generation agentic AI system combining real-time acoustic monitoring, autonomous agents, and domain-trained LLM chatbot on Azure Databricks infrastructure.
95.62%+ accuracy in failure detection, $5.25B+ in protected critical assets, 80% faster incident response, and prevented cascading pipeline failures.
Quantified business impact across critical infrastructure protection
Multi-agent system with autonomous decision-making and LLM interaction
Autonomous agent framework with embedded domain-trained LLM chatbot. Ensemble time-series models for anomaly detection with self-prioritizing agents for threat triage and workflow automation.
Databricks Lakehouse architecture for unified batch and streaming analytics. MLflow for model versioning and continuous refresh. Distributed edge processing with cloud orchestration.
Seamless integration with SCADA systems, maintenance records, and regulatory codes. Multi-agent collaboration with audit trails for multi-jurisdictional oversight.
From traditional monitoring to autonomous AI-powered infrastructure protection
Integrated thousands of acoustic and pressure sensor streams via Delta Lake. Established zero-trust edge security and data quality protocols across distributed infrastructure.
Built ensemble time-series models for anomaly detection with 95.62%+ accuracy. Developed agentic AI logic for autonomous threat prioritization and escalation workflows.
Deployed domain-trained LLM chatbot with voice interface for field teams. Integrated with SCADA, maintenance records, and regulatory codes for explainable diagnostics.
Full deployment across critical infrastructure with 24/7 autonomous monitoring. Achieved 80% faster response times and prevented cascading pipeline failures.
Quantified results across safety, financial, and strategic dimensions
Prevented catastrophic pipeline failures and environmental incidents through predictive AI monitoring across critical energy infrastructure.
Fully autonomous threat detection, prioritization, and escalation with multi-agent collaboration and real-time decision making.
Revolutionary reduction in incident response time through autonomous agent workflows and LLM-powered field team assistance.
Industry-leading anomaly detection accuracy with ensemble models, preventing false alarms while ensuring zero critical failures.
Key insights from deploying agentic AI in critical infrastructure
This transformation showcases how agentic AI can redefine critical infrastructure protection. By combining autonomous decision-making, domain expertise, and human-AI collaboration, we created a system that doesn't just monitor—it actively protects, learns, and assists teams in real-time across billions in critical assets.