Insights
Architecture patterns, compliance strategies, and engineering perspectives from building AI systems in environments where the stakes are real.
Most AI initiatives fail not because the models don't work, but because the architecture around them wasn't designed for the real constraints: compliance timelines, data classification, multi-tenancy, and auditability. Here's how to think about AI platform design from first principles.
Manual compliance evidence gathering doesn't scale. This article walks through a reference architecture for automated evidence collection that maps cloud infrastructure state to NIST control families and generates OSCAL-native documentation.
Building RAG for government or healthcare means solving problems most tutorials ignore: document-level access control, citation tracking, audit logging, and classification-aware chunking. A reference architecture for production RAG in regulated settings.
Governance and velocity aren't mutually exclusive. This article outlines a practical governance operating model that integrates bias monitoring, model risk assessment, and explainability into existing CI/CD workflows.
AI agents in production need patterns most teams haven't built before: reliable task decomposition, human-in-the-loop approval gates, tool-use sandboxing, and conversation state management. Key patterns from real deployments.
Traditional APM doesn't capture what matters for AI systems. This article covers the observability stack you need: token usage, latency distributions, retrieval quality, hallucination rates, and cost attribution per interaction.
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