I build AI systems for industries where failure isn't an option.
After 25 years architecting, automating, and operating hyperscale infrastructure for Silicon Valley giants, I saw how AI could transform critical industries — if we could make it trustworthy. Today, I'm proving it's possible.
Two Missions, One Standard: Zero Tolerance for Error
At Always Cool AI, we tackle humanity's most critical challenges:
• Nuclear Energy: Developing AI solutions for the nuclear industry where every decision must be auditable, traceable, and correct. When you're working with nuclear power, "probably right" isn't good enough.
• Food Supply Chain: Automating ingredient safety analysis and FDA compliance validation. Our AI turns months of manual review into automated workflows, helping brands launch safer products in 12 weeks instead of 12 months.
Production-Grade AI That Regulators Trust
We don't build demos. We build systems that pass audits. Using LangGraph for orchestration, OpenTelemetry for complete observability, and implementing both MCP and A2A Protocol for secure agent communication, our AI systems provide:
• 100% decision traceability — every AI action logged and auditable
• Real-time compliance monitoring — for FDA, NRC, FedRAMP, and SOC-2 requirements
• Zero-trust agent architecture — because critical infrastructure demands it
Real Results in Production
My experience with enterprise compliance taught me that manual processes don't scale — and with AI, they're impossible. So we built AI systems that generate their own audit trails, validate their own decisions, and prove their own compliance. Energy companies now scale safely. Food manufacturers launch products with confidence. Government contractors achieve FedRAMP compliance faster. That's what happens when you combine deep technical expertise with practical AI implementation.
Building the Future With 1,300+ Practitioners
I founded Austin LangChain AIMUG because the hardest problems require collective intelligence. Our community doesn't just theorize — we share production patterns, debug real implementations, and push the boundaries of what's possible with practical AI.
The future belongs to AI that works when everything is on the line.
