Decision AI for
Pharma R&D Excellence

AI decisions that compress the decision lifecycle and keep you audit-ready before the regulator asks.

Pharma R&D Decision Lifecycle

Six stages. Every one on a clock.

From first target to patent cliff, four phases, each one owned by a different team, each one running against a different kind of deadline.

New · The Decision Desk

CEO Guide for AI in Pharma

Ten decisions on capital, workforce, IP and governance, backed by Bain, McKinsey, Deloitte, BCG and EY research.

Stage by Stage

Each decision below shows the result your team gets, the data it runs on, and the ontology layer it reasons over.

The Ontology

Each layer is a noun, not a decision, an object with its own state and history, shared across whichever stage needs it.

Domain Ontology

The System

Why enrollment stalled, a case is causal, or a protocol needs to change.

Clinical trials + post-approval: built on diagnosis, not correlation

Every site ranking and ICSR verdict sharpens the next cycle.

+14–15 pts on deviation & timeline reduction, once it compounds

CRO, EDC, and eight spontaneous-report sources reconciled first.

One trusted case file before enrollment or ICSR verdicts run

Decision AI for Pharma R&D

See DecisionX on your trials, your safety cases, your pipeline. First value in 15 days.