Saram ConsultingWhy Your LLM Keeps 'Fixing' the Same Bug
LLMs don't debug — they simulate what a fix looks like. Here is the architecture behind the pretense, the empirical evidence quantifying it, and the seven escape hatches that actually work.
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Thoughts and strategies on the intersection of Biotech, Artificial Intelligence, and Regulatory Compliance.
The Regulated Knowledge Fabric: Adapting Enterprise RAG Architecture for GxP Life Sciences
Every regulated life sciences company has the same problem — knowledge scattered across validated systems that cannot be consolidated. The 'meet data where it lives' retrieval architecture solves this, but only if you wrap every layer in compliance controls, lifecycle awareness, and immutable audit trails. Here is the full architectural translation.
Read Article →The AI Engineering Loop: Turning Production Failures Into Release Evidence
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 →Building an AI Quality Operating System for Life Sciences: Agents, Architecture, and the Regulatory Path Forward
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 →Four Dataset Layers That Turn an LLM Into a Life Sciences Quality Professional
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.
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