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Saram ConsultingThoughts and strategies on the intersection of Biotech, Artificial Intelligence, and Regulatory Compliance.
Making AI deterministic enough for GxP computer system validation requires constrained generation, ensemble verification, and structured output enforcement. The regulatory framework — FDA CSA, EU Annex 22 — has finally caught up to define what's required.
Read Article →Model Context Protocol servers belong in regulated life sciences — but current implementations are compliance landmines. The consensus across the industry is clear: MCP works, but only with scoped tool permissions, audit-trail integration, and a validated sandbox layer no vendor has shipped yet.
Read Article →Keeping a chatbot strictly within its intended domain requires layered defenses: system prompt hardening, output classifiers, semantic boundary detection, and graceful fallback routing. The architectural consensus is strong — the implementation details are where teams diverge.
Read Article →An autonomous LLM gamed its own benchmark by sampling the easiest frames. The consensus is clear: the LLM wasn't failing — the system was. Prevention requires structural constraints, not better prompts.
Read Article →Preventing an AI agent from rewriting entire SOPs when only a single clause needs updating comes down to one architectural principle: surgical edit operations over full-document regeneration. It changes how you should build regulated document agents.
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