Regulatory Intelligence — Portfolio-Aware Monitoring, HAQ Readiness & HA Correspondence
Intelligence Scored Against
Your Portfolio, Not the World's.
Live agency feeds from FDA, EMA, and MHRA — scored by AI against your actual applications, triaged into a surveillance workflow, and connected to predictive HAQ readiness, health-authority correspondence tracking, and an RFI response workbench. Built on analysis of 1,000+ real health-authority questions.
Sources
Triage
Compared
Who This Is Built For
Regulatory intelligence fails in two directions: too much noise, or too little foresight. These are the roles this module was designed around.
Your first major filing is approaching and the question that keeps you up at night is not "is the dossier complete?" — it's "what will the agency ask?" Every RFI round costs months, and your team's response process starts from a blank page each time. You need to know which questions are likely before you submit, and you need draft answers grounded in what has actually worked, not in guesswork.
- Predictive HAQ readiness: likely questions by discipline, before submission
- Every prediction explainable — grounded in analysis of 1,000+ real HA questions
- Recommended responses and prep actions attached to each predicted question
- Review rounds reduced by preparing answers to predictable questions up front
Your inbox is a firehose: Federal Register notices, EMA updates, MHRA bulletins, subscription digests. Ninety percent is irrelevant to your portfolio, but you have to read it all to find the ten percent that matters — and then chase SMEs by email to assess impact. You need the relevance filter to run before the content reaches you, and a triage workflow that survives an audit.
- Every item AI-scored against your actual applications, not generic topics
- Inform-Only vs Potential-Impact triage with SME routing and change-control escalation
- Guidance & consultation tracker with impact ratings and comment deadlines
- AI-generated digest newsletters replace the manual weekly roundup
Agency correspondence lives in inboxes. Commitments made in a response letter two years ago surface only when an inspector asks about them. Tracking interactions per application means archaeology across shared drives and departed employees' mailboxes. You need a system of record where commitments cannot silently disappear.
- Full health-authority interaction log per application and agency
- Commitments discharged only by a responding correspondence — no "mark done"
- Inbound email ingestion with auto-attribution, human-confirmed before it counts
- PDUFA and precedent tracker with computed median review times
How It Works
From raw agency feed to a defensible decision — and from a submitted dossier to a ready answer.
The platform ingests five live sources directly from official channels — the Federal Register, openFDA approvals and enforcement actions, EMA updates, and MHRA announcements — plus a generic RSS ingester for any public feed you want to monitor. No third-party aggregator sits between you and the source, and no per-seat content licensing meter is running.
Each item is scored by AI against your registered applications — therapeutic areas, dosage forms, regions, and lifecycle stage — rather than against generic topic tags. A nitrosamine guidance scores high if you have an affected product and near zero if you don't. This is the core difference from feed-style intelligence products: relevance is computed per portfolio, not per topic subscription.
Scored items land on a triage board where an analyst confirms or overrides the AI's assessment. Inform-Only items archive with a record of the decision; Potential-Impact items route to named SMEs for assessment and, where warranted, escalate up a change-control ladder. The result is an auditable trail from "the agency published something" to "here is what we decided and why."
The guidance and consultation tracker manages open comment periods, impact ratings, and internal positions in a structured 14-column workspace. The precedent tracker computes median review times from real approval data, and the self-updating regulatory calendar keeps PDUFA-style dates visible. An AI country-requirements comparison covers 16 countries for market-planning questions.
From your submission profile and the CTD modules present, the platform predicts likely health-authority questions — grounded in base rates from analysis of 1,000+ real HA questions, where the mapping from CTD section to questioning discipline proved 98–100% deterministic. Predictions arrive grouped by discipline with likelihood, recommended response strategy, and preparation actions. Every prediction is explainable; nothing is a black box.
When an information request does arrive, paste it into the workbench: AI decomposes it into atomic questions, routes each by discipline, and retrieves grounded draft responses from your own approved past answers. A rubric-based AI QC pass catches placeholders and unsupported claims before a human ever reviews. Approved answers join the knowledge base — so the next response cycle starts further ahead. Teams using this loop target 500–900 hours saved per RFI cycle.
Every agency interaction is logged per application. Inbound emails are ingested and auto-attributed to application, agency, and interaction type — but land as PROPOSED until a human confirms them. Commitments can only be discharged by a responding correspondence, never by a checkbox, which means the commitment register an inspector sees is the one your team actually worked from.
Six Capabilities, One Intelligence Loop
Monitoring, prediction, and response are usually three tools. Here they share one data model — your portfolio.
Portfolio-Aware Impact Scoring
Live items from FDA, EMA, and MHRA are scored against your actual applications rather than topic subscriptions. The score reflects overlap with your therapeutic areas, regions, and lifecycle stage, and the reasoning is visible — so an analyst can confirm or override it in seconds. The practical effect is inverting the intelligence workload: instead of reading everything to find what matters, your team reviews a pre-ranked queue where the top items almost always matter.
Predictive HAQ Readiness
Built on analysis of 1,000+ real health-authority questions, the readiness engine predicts likely questions for your specific submission before it goes out the door — grouped by discipline, with likelihood, recommended response approach, and preparation actions. Because CTD-section-to-discipline mapping proved 98–100% deterministic in the underlying corpus, predictions are explainable and auditable rather than probabilistic hand-waving. Teams use it to prepare answers up front and cut review rounds from three to two.
RFI Workbench & RTQ Knowledge Base
Paste an agency information request and the workbench decomposes it into atomic questions, routes them by discipline, and drafts grounded responses retrieved from your own approved past answers. Rubric-based AI QC catches placeholders and unsupported statements before human review, and a human approval gate stands between every draft and the record. Each approved answer strengthens the knowledge base — a genuinely self-improving loop, proven end to end.
HA Correspondence & Commitments
A full interaction log per application and agency, with a hard rule at its core: commitments are discharged only by a responding correspondence, never by a manual "mark complete." Inbound email ingestion auto-attributes application number, agency, and interaction type, but everything lands as PROPOSED until a human confirms it. The register your inspectors see is the register your team actually operates.
Guidance & Consultation Tracker
Open guidances and consultations are managed in a structured tracker — impact ratings, comment deadlines, assigned owners, and internal positions — instead of a bookmarked-links document. Paired with the triage board, it closes the loop from "published" to "assessed" to "commented" with an audit trail at every step.
Precedent & Calendar Intelligence
The precedent tracker computes median review times from real approval data so your timeline assumptions are grounded in evidence, and the self-updating regulatory calendar keeps agency dates current without manual upkeep. An AI-driven country-requirements comparison spans 16 countries for expansion planning. AI-generated digest newsletters summarize the week for stakeholders who will never open the tool.
DnXT vs The Alternative
The incumbent approaches are enterprise intelligence feeds and the email-plus-spreadsheet system nobody admits to. Here is the honest picture.
| Capability | DnXT Regulatory Intelligence | Enterprise Feeds (Cortellis-style) | Email + Spreadsheets |
|---|---|---|---|
| Relevance model | ✓ AI-scored against your portfolio | ● Topic subscriptions, generic impact | ✗ A human reads everything |
| Predictive HAQ readiness | ✓ Grounded in 1,000+ real questions | ✗ Not offered | ✗ Institutional memory only |
| RFI response workbench | ✓ Grounded drafts + AI QC + human gate | ✗ Out of scope | ✗ Blank page every time |
| Commitment tracking discipline | ✓ Discharged only by responding correspondence | ✗ Out of scope | ● Checkbox columns, silently stale |
| Triage audit trail | ✓ Every decision recorded with rationale | ● Read receipts at best | ✗ Forwarded emails |
| Cost profile | ✓ Included in the platform — see pricing | ✗ Six-figure annual content licenses | ● "Free," paid for in analyst hours |
Design Decisions That Matter in a GxP Context
- Federal Register — rules, notices, and guidance publications as issued
- openFDA — approvals plus recall and enforcement actions
- EMA & MHRA — official European and UK channels
- Bring your own RSS — monitor any public feed on demand
- Base-rate grounding — predictions derive from real question frequencies, not vibes
- Deterministic backbone — CTD section → discipline mapping is 98–100% reliable
- Visible reasoning — every score and prediction carries its rationale
- Analyst override — humans confirm, adjust, or reject; the system records which
- PROPOSED state — auto-ingested correspondence never enters the record unconfirmed
- Human approval gates — on RFI drafts, triage decisions, and commitments
- AI QC before human review — rubric checks catch placeholders early
- Consistent with the platform's Part 11 audit architecture
- Semantic retrieval — vector search across approved answers, guidances, and lessons learned
- Citations included — retrieved drafts show their sources
- Keyword fallback — retrieval still works when embeddings are unavailable
- Compounding value — the knowledge base gets better with every cycle your team completes
Know What the Agency Will Ask. Before You Submit.
See portfolio-aware intelligence, predictive HAQ readiness, and the RFI workbench working on a real submission profile.
