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Saram ConsultingThoughts and strategies on the intersection of Biotech, Artificial Intelligence, and Regulatory Compliance.
Reliable AI agents are built through a closed loop: trace real behavior, detect useful signals, classify failures, preserve them as test cases, run controlled experiments, and combine deterministic checks, calibrated model judges, and human review before release.
Read Article →Deploying AI agents into a GxP-regulated Quality department is not a chatbot project. It requires a validated, auditable operating system where specialized agents share governed infrastructure, and every output traces back to approved source documents. Here is the architecture, the regulatory framework, and the implementation roadmap that actually works.
Read Article →Dumping PDFs into a folder and calling it a training set will not work for GxP documents. SOPs, deviations, and CAPAs need four distinct dataset layers — each with a different format, training type, and behavioral purpose. Here is the architecture.
Read Article →In May 2025, a federal judge ordered OpenAI to preserve every ChatGPT conversation indefinitely. ZDR customers were exempt. Here's what ZDR actually means, which providers offer it, and why it increases ROI instead of shrinking it.
Read Article →When a single LLM call fails on a complex task, the answer is almost never a bigger prompt. It is decomposition — systematically breaking the problem into smaller, verifiable, independently solvable pieces. Here are the 5 patterns every AI architect must know, when to use each, and the anti-patterns that will sink you on the exam and in production.
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