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Saram ConsultingThoughts and strategies on the intersection of Biotech, Artificial Intelligence, and Regulatory Compliance.
Life science quality teams want AI. Regulators demand determinism. The AI Harness resolves the tension — a governed orchestration layer that makes every LLM output traceable, auditable, and defensible under GxP. Here's the full architecture.
Read Article →DSPy turns prompt engineering from guesswork into compiled, version-controlled code. For life sciences quality teams, that's the difference between a fragile demo and a validated system. Here's the architecture, the compliance pattern, and the 4-week pilot plan.
Read Article →Bi-encoder retrieval gets you recall. Reranking gets you grounding. In life sciences quality — where 'deviation handling' appears in 200 SOPs — a cross-encoder reranker is the difference between a traceable citation and a hallucinated one. Here's the architecture, the models, and the validation framework.
Read Article →Supervised Fine-Tuning makes an LLM good at your SOPs. Reinforcement Learning makes it robust to edge cases regulators care about. Here is the architecture, use cases, and implementation roadmap for deploying both in GxP-regulated quality systems.
Read Article →Agent performance is no longer about the model. The same LLM produces 6x different results depending on its harness. Here's the anatomy, the ecosystem, and why this shift matters for anyone building agents in regulated environments.
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