AI in life sciences, diagnostics, and the food chain, validated like an assay and documented for the inspection.
A model that performs well on average can still fail an inspection, because inspections ask about the failures: which modes are characterized, at what rate against which denominator, and what record reconstructs each decision the system made. This practice designs validation the way a laboratory designs an assay, with defined denominators, failure modes bounded and characterized, edge conditions provoked deliberately, and documentation written for the inspector rather than the steering deck, so the system's evidence is ready before anyone asks for it.
The practice lead built and ran clinical laboratories to CAP/CLIA standard before he built AI systems. As a laboratory manager he stood up a GLP tissue-culture and immunology laboratory from an empty room in roughly 12 weeks, and a veterinary diagnostics laboratory validated to the human clinical standard in 56 days; at Daisy Brand he built the molecular pathology, clinical pathology, nutrition, and microbiology functions. The validation discipline this practice sells is the one he was trained in and ran.
He is a PhD pathologist with 22 years of professional AI, beginning at Virginia Tech's CMMID in 2004, with 13 peer-reviewed publications. His ECC method reduced production LLM hallucination from 22.1% to 1.1% (N=2,943).