DecisionX for Pharma

Decision AI for Pharma

One connected enterprise decision brain for pharma, reasoning across R&D, Manufacturing, and Commercial as a single system, not three disconnected tools.

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Built with provenance, auditable, and fully explainable, for industries where decisions carry real-world consequence.

The three pillars

Deep in each function, connected across all three.

Each pillar runs its own full decision lifecycle in depth on its own page, four core use cases, and where DecisionX plugs in.

Pharma R&D icon

Pharma R&D

Trial, pipeline, and regulatory decisions grounded in a traceable evidence chain.

Trial Signal Adjudication
Enrollment & Site Risk Forecasting
Regulatory Submission Readiness
Adverse-Event Triage
Explore Pharma R&D
Manufacturing Excellence icon

Manufacturing Excellence

Batch disposition, deviation, and yield decisions built on production and quality data.

Batch & Lot Disposition
Deviation Root-Cause Diagnosis
Asset Failure Pre-emption
Yield & Waste Recovery
Explore Manufacturing Excellence
Commercial Excellence icon

Commercial Excellence

Launch, HCP engagement, and access decisions grounded in commercial signals.

Demand & Margin Reconciliation
HCP Call-List Ranking
Sales-Decline Root-Cause Routing
Media & Trade Spend Reallocation
Explore Commercial Excellence

The Pharma AI Stack

One connected enterprise brain, purpose-built for pharma.

A state-of-art decision infrastructure, causal, contextual, self-learning, with one reasoning engine sitting underneath every domain.

Where Decisions
SurfaceEvery dashboard and board each function already works in.
PHARMA R&D
Trial & pipeline dashboards Regulatory submission trail Adverse-event trackers
MANUFACTURING
Batch disposition trackers Deviation & CAPA boards Yield recovery dashboards
COMMERCIAL
Launch & field boards HCP engagement dashboards Demand & allocation boards
Reasoning
AgentsSpecialized agents that reason over the ontology layer below.
Forecasting Agent RCA Agent Optimization Agent Attribution Agent
DecisionX
Reasoning EngineOne brain, reasoning across R&D, Manufacturing, and Commercial at once.
DecisionX Contextual Aware Intelligence Layer

Causal, contextual, and self-learning, every ontology below is reasoned over by the same brain, so a decision in one domain is visible context for the other two.

DecisionX
Ontology LayerThe structured business logic each domain reasons against.
R&D ONTOLOGY
Target & Biology Safety & Tox Trial & Site Regulatory & Submission Real-World & Market Competitive & IP
MANUFACTURING ONTOLOGY
Quality & Batch Deviation & CAPA Asset & Reliability Yield & Waste Change Control & Compliance Network & Tech Transfer
COMMERCIAL ONTOLOGY
Demand & Margin Pricing & Trade Field & HCP Media & Channel Gross-to-Net & Contracting Launch & Access
Data & Business
SystemsStructured & unstructured, handwritten scans, messy files, every format in between.
R&D SOURCES
EHR / claims data CRO reports Trial site & enrollment history Biomarker & lab data Competitor trial registries Protocol & amendment histories
MANUFACTURING SOURCES
LIMS / MES batch records Stability data Equipment & sensor logs Deviation & CAPA records Supplier CoAs Change control records
COMMERCIAL SOURCES
HCP / CRM engagement data Sales & claims data Demand & inventory signals Media & channel spend Payer & contract data Field call & territory data

How DecisionX reasons

Five layers, one decision.

Every signal above, trial, stability, or launch, moves through the same reasoning arc before it becomes a decision.

Signals icon
LAYER 01
Signals
What is happening?
Trial signals, stability flags, and launch-readiness gaps are watched continuously, not surfaced once a quarter.
Reason icon
LAYER 02
Reason
What does it mean?
The endpoint interaction, the batch's stability risk, and the launch-messaging exposure are reasoned together, not adjudicated three times.
Decide icon
LAYER 03
Decide
What's the best action?
Release, hold, or expand testing, ranked by regulatory risk, batch value, and launch timeline in one recommendation.
Track icon
LAYER 04
Track
Did it hold?
The stability read and post-launch signal are watched after the decision, not assumed to have resolved.
Learn icon
LAYER 05
Learn
What refines next time?
Outcomes feed back into how future deviations and trial signals get triaged.

Outcomes feed back into Signals, closing the loop for the next decision.

Why this needs one system

Point solutions vs. one connected brain.

Three point tools, each smart on their own, still leave three separate re-diagnoses. One connected brain doesn't.

The old wayPoint Solutions
The DecisionX wayOne Connected Brain
Re-diagnosis at every handoffEach tool investigates the same signal from scratch, inside its own function.
No re-diagnosisOne evidence chain, reused at every handoff. The root cause found in R&D isn't re-investigated in Manufacturing.
Blind handoffs between functionsCommercial finds out about a quality hold after launch messaging is already live.
No blind handoffsEvery function reads the same fact. Commercial sees the quality hold before launch, not after.
Three logs that don't agreeA regulator or auditor has to reconcile three separate systems of record after the fact.
One audit trailA regulator or auditor traces one decision straight across all three functions.
Insight arrives after the window closesBy the time each tool's answer surfaces, the decision it was meant to inform is already made.
Reasoning inside the decision windowOne brain, watching all three domains continuously, surfaces the answer while it still matters.

Built for scale, not a pilot

Most AI pilots die at the second use case. This one is built to scale from the first.

Enterprise AI's real failure point isn't the model, it's what happens after the first pilot succeeds and the next team has to start from zero.

95%
Of enterprise GenAI pilots fail to deliver measurable ROI
MIT, 2026
14%
Of enterprise AI agent pilots ever reach production scale
Enterprise technology leader survey, March 2026

Marginal cost drops with scale

The next function reuses the same ontology and reasoning engine. It doesn't start from zero.

Value compounds over time

A self-learning ontology means every decision made anywhere sharpens the next one, enterprise-wide.

Built on real data, not a curated demo

Structured and unstructured, handwritten scans and messy files, the same data pilots are usually shielded from.

Sponsorship is structural, not incidental

Spanning three functions by design means it requires, and sustains, sponsorship from day one, not one team's budget.

3 functions live today. Each new one reuses the same brain, it ships in weeks, not quarters.

Proof, not promises

What pharma teams actually saw.

R&D · Pharmacovigilance
90%
Faster adverse-event signal triage, with zero missed 15-day reporting deadlines and 3x more signals caught early.
Global pharma conglomerate, pharmacovigilance function
Commercial · OTC Business
11%
Marketing efficiency gain, with 2.2x sales rep productivity and campaign decisions made 3 weeks faster.
Global pharma OTC business, three quarters post-rollout
Manufacturing · Quality
Same-day
Batch disposition and deviation root-cause answers, evidenced and ready before the review meeting, not after.
DecisionX manufacturing engagements
Causality
Every recommendation ranked against a matched comparison, not a hunch or a static rule.
Self-learning
Every decision's outcome sharpens how the next signal is triaged.
Unified context
One evidence chain, read the same way by R&D, Manufacturing, and Commercial.

Questions, answered

What teams ask before they start.

The essentials on scope, validation, data, and time to first value.

Point solutions each re-diagnose a signal from scratch inside their own function. DecisionX reasons over one shared evidence chain, so a root cause found in R&D or Manufacturing is reused, not re-investigated, at every downstream handoff.
DecisionX is built to produce an auditable, traceable reasoning chain for every decision, the evidentiary standard regulators expect. Validation scope and documentation are confirmed per deployment during onboarding.
Pharma R&D (discovery through post-approval pharmacovigilance), Manufacturing Excellence (batch disposition, deviation, yield, and quality), and Commercial Excellence (demand planning, HCP engagement, and launch decisions), all on one connected evidence chain.
Most pharma deployments see first-value decisions within 15 days of connecting the relevant data sources.
Trial and safety data (EHR, claims, CRO reports), manufacturing and quality systems (LIMS, MES, batch and stability records), and commercial systems (HCP/CRM, sales, and launch data), reasoned across all three inside one ontology.
CEO Guide for AI in Pharma, book mockup
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.
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Decision AI for Pharma

Stop re-diagnosing the same signal three times.

See DecisionX connect your trial, plant, and launch data. First value in 15 days.

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