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Saram ConsultingThoughts and strategies on the intersection of Biotech, Artificial Intelligence, and Regulatory Compliance.
Standard RAG pipelines fail on quality documents. Fixed-size chunking destroys procedure context, vector search misses document IDs, and obsolete SOPs leak into answers. Here's the architecture that actually works for regulated industries.
Read Article →The consensus on continual learning for AI in GxP environments is clear — but several widely-cited regulatory claims don't hold up. Here's the verified framework for deploying adaptive AI in biopharma.
Read Article →Every credible analysis of MCP in regulated environments converges on the same architecture — a centralized gateway proxy. But several widely-cited claims about this pattern are wrong. Here's what the evidence actually supports, what the regulators actually require, and what the vendor landscape actually offers.
Read Article →The Model Context Protocol turns LLMs into active agents that can query LIMS, QMS, and clinical databases. Raw MCP is a compliance liability in GxP environments. The protocol isn't the problem — the implementation is. Here's the architecture that makes MCP regulator-ready, backed by OWASP, GAMP 5, and the FDA's own CSA framework.
Read Article →Self-learning agents don't retrain their neural weights in production. They use externalized memory loops, self-reflection, and skill crystallization. Here's the verified architecture — and how to deploy it in a GxP environment without triggering an FDA warning letter.
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