Raghu Devayajanam

I help enterprises close the gap between AI ambition and production reality.

That gap is usually an architecture problem: systems that were never designed for governance, evaluation, observability, or operational control.

My focus is the engineering side of governed AI — designing the structural controls that make responsible AI enforceable in production. That includes trust boundaries, evaluation pipelines, operational guardrails, model observability, and audit-ready architecture.

My work spans AI-ready data architecture, traditional ML platforms, generative AI systems, and agentic / multi-agent architectures, with emphasis on production design in regulated and high-accountability environments. My recent work includes governed MLOps pipelines, trust-layer RAG architectures, NIST AI RMF-aligned governance patterns, and architecture work focused on accountable agentic systems.

My background includes enterprise-scale architecture across federal and regulated environments, where reliability, traceability, and operational discipline are non-negotiable.