VERUM · Pharma & Life Sciences

Plausible isn't correct: enforcing pharmaceutical fact at runtime

In pharma, the dangerous AI output is not the obviously broken one, it's the one that reads perfectly and is wrong. A dose that fits the sentence but not the patient. An adverse-event term that's plausible but mis-coded. A claim no one flags because nothing about it looks off.

VERUM is built for exactly that gap between plausible and correct. VERUM reads what your AI produces and reconciles it against two things at once: the verified fact, and the exact regulation behind it.

Take a real shape of error, an AI output recommending metformin at a standard dose for a patient whose kidney function (an eGFR of 28) puts them below the threshold where metformin is contraindicated, for risk of lactic acidosis, with no renal dose adjustment attached. Nothing in the sentence looks wrong. VERUM holds it, attaches the fact and the governing rule, and routes it to a clinician before it reaches the record. Every finding is bound to a verified fact and a precise clause, not a confidence score, not a vibe.

Behind that sits the regulated machinery of drug safety and quality. In pharmacovigilance, signal and case integrity are governed by Good Pharmacovigilance Practices, where a mis-coded adverse event can dilute a drug–event combination and delay a real safety signal. In the GxP record, data integrity is governed by standards like 21 CFR Part 11 and the ALCOA+ principles, where every entry must be attributable, contemporaneous, original and preserved unaltered. An AI output that breaks either is a regulatory event, and VERUM's job is to catch it the moment it's produced, not in the audit that finds it later.

VERUM is also held to the standard it enforces. It runs on its own enforcement layer, so every answer it returns is itself checked, signed and logged, it resists manipulation and adversarial prompting, keeps sensitive data where it belongs, watches for drift, and holds its own output to clinical and pharmaceutical integrity before that output is trusted. The point is simple: the analyst that judges your AI has to be able to pass its own inspection.

The regulators are moving the same direction. In January 2026 the FDA and EMA jointly issued the Guiding Principles of Good AI Practice in Drug Development, principles spanning the lifecycle from research to post-market safety, emphasizing human oversight, data governance and lifecycle control. They are principles, not yet binding rules, but they describe precisely the discipline VERUM enforces today.