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.
Three Products, One Standard: Zero Tolerance for Error
Through Self-Improving Code, I build AI for industries with zero tolerance for error:
• Acquit.ai: Litigation intelligence that models opposing parties as cognitive agents to predict behavior. 91% accuracy across 201 tracked predictions. ARPN risk framework adapted from FMEA. Court-ready evidence pipeline.
• FrawdBot: Insider threat detection for Google Workspace. Behavioral pattern analysis, communication graph modeling, and campaign-level threat correlation.
• Regulated Industry Consulting: AI systems for nuclear (NRC), financial services (FINRA), and food safety (FDA) where every decision must be auditable, traceable, and correct.
Production-Grade AI That Regulators Trust
We don't build demos. We build systems that pass audits. This is MLOps—the discipline of deploying, monitoring, and governing machine learning models in production. Using LangGraph for orchestration, OpenTelemetry for complete observability, and implementing skills-based agent architecture on MCP, Claude Agent SDK, and DeepAgents for portable, 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
• Continuous model evaluation — automated quality assessment in production
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 1,700+ 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.
