One design rule makes AI defensible in regulated environments: AI output is always a draft until a human approves it into the record. Here is how that boundary works in practice.
A submissions grid that took 12 seconds to load was issuing one database query per row. The fix took the page to 0.7 seconds — and taught us why performance is an adoption and compliance issue.
Can regulated software fix itself? Yes — within a strict boundary. The rule we use to decide what heals automatically, what escalates to humans, and why every heal writes an audit record.
How do you regression-test software whose ultimate validator is a health authority? Harvest real published sequences, replay them, and diff the results. Inside our characterization harness.
We automated 50+ operational qualification scripts and learned that the hard part isn’t automation — it’s honesty. How to build OQ evidence a QA auditor can actually trust.
In regulated software, a false success is worse than a crash. Three real silent-failure stories from our own platform, the engineering discipline that eliminates them, and what buyers should ask vendors.
Audit preparation should not be a three-week fire drill. How continuous readiness scoring, one-click audit packs, and mock inspection mode turn inspection prep into a standing capability.
Most ‘AI search’ in regulatory software is keyword matching with a chat interface. Here is what true vector retrieval looks like, why citations are non-negotiable in GxP, and how we proved ours works.
DnXT’s auto-assign engine places documents into eCTD sections using patterns mined from 3,081 real sequences — deterministic first, AI last, and every placement explainable.
We studied 14 real NDA-program tracking spreadsheets before building DnXT’s document tracking grid. Here is what Excel gets right, where it breaks, and what replaces it.
