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Saram ConsultingThoughts and strategies on the intersection of Biotech, Artificial Intelligence, and Regulatory Compliance.
Generic chunking destroys life science documents. Here's the document-type-specific chunking architecture for SOPs, Deviations, CAPAs, Work Instructions, and Batch Records — with the metadata schema, evaluation framework, and compliance controls that make it defensible.
Read Article →MTP speculative decoding delivers 1.4–2.2x faster generation with zero accuracy loss. Here is how it works, which hardware benefits most, and the exact flags to set for Qwen3.6 on llama.cpp, vLLM, and Unsloth Studio.
Read Article →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.
Read Article →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 →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.
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