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Saram ConsultingThoughts and strategies on the intersection of Biotech, Artificial Intelligence, and Regulatory Compliance.
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.
Read Article →Whether an AI agent drafting SOPs in biopharma needs its own QMS account depends on your architecture. The service account vs. no-account debate maps directly to how deeply the AI integrates with your validated systems — and EU Annex 22, FDA CSA, and the incoming GxP-AI framework all have something to say about it.
Read Article →When files land in S3 or MinIO, they should automatically flow through parsing, chunking, embedding, and indexing. Six architectural approaches for automating this pipeline locally — from simple webhook scripts to orchestrated workflow engines — compared side by side.
Read Article →AI agents designed for pharmaceutical computer system validation converge on the same architecture — and consistently miss the single most important regulatory precedent. Here's what the consensus gets right, where it diverges, and the three blind spots that should shape every CSV AI strategy.
Read Article →Building local RAG on Claude Enterprise converges on a single architecture: hybrid retrieval with contextual compression, chunk-level reranking, and strict metadata filtering. Anthropic's own benchmarks confirm it works.
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