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.
How DnXT models multi-market submissions: a global core template, market variants that inherit and diverge deliberately, and a variance ledger that shows every difference at a glance.
DnXT’s Predictive HAQ Readiness forecasts the health authority questions your submission is most likely to receive — before you submit — grounded in analysis of 1,000+ real agency questions.
A practical look at eCTD validation: 23 compliance rules, multi-region support, and how AI helps with preparation while deterministic rules handle the actual checking.
A look inside DnXT’s V5 workflow engine — 50+ endpoints, state machine architecture, and the preCheck pattern that lets AI agents look before they leap.
Combining keyword and semantic search with Reciprocal Rank Fusion delivers more reliable regulatory document retrieval than either approach alone.
Connecting planning, document management, and publishing into a single operational pipeline eliminates manual handoffs and compresses submission timelines.
Extending document management beyond regulatory submissions to quality, clinical, corporate, commercial, and operational domains under a unified governance model.
