Practical notes on building AI that survives compliance reviews, runs offline, and ships on time.
A practical architecture for deploying large language models inside your network — so the compliance review is a 20-minute conversation, not a six-month project.
Read →What auditors actually look for when you put an LLM in front of customer data — and the eleven controls that cover 80% of your exposure.
Read →The three architectural patterns that get a green-light from healthcare compliance officers — and the one that gets an immediate "no."
Read →A practical RAG reference architecture plus the seven production failures nobody warns you about — and how to avoid each one.
Read →What it actually takes to run modern AI inside a classified or fully disconnected network — from model weights to updates to observability.
Read →Beyond vibes: a working evaluation framework with specific metrics, golden datasets, and cadence that catch regressions before users do.
Read →The MLOps practices that get you through SOC 2, HIPAA, and model-risk reviews — instead of getting torn apart in them.
Read →A decision framework for choosing between a hosted AI product, a vendor-assembled solution, or a custom build.
Read →Secure AI, compliance tips, and the occasional case study. No fluff, no sales emails.
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