Enrichment recovered invisible patients. Models re-ranked the call plans. Reps acted — and impact was measured continuously against matched controls, independently, not self-reported.
The intelligence layer for pharma GTM. Five accelerators, two platforms — in production across 10+ brands and 350+ field and HQ users.
Five AI accelerators. Two platforms. Every one in production across 10+ brands.
Patient-data enrichment for claims & EHR
SageField & HQConversational intelligence for field & HQ
HorizonForecastingPatient-based forecasting for brand & S&OP
QuillContentEvidence-linked content for brand teams
AtlasLaunchKOL and competitive intelligence for launch teams
The ML factory for pharma data — builds, deploys and refreshes commercial models 70% faster.
02Governed AI analysts for data teams — validated context, 17 skills, and drift, bias and traceability checks on every run.
Deploy a single accelerator, a set of components, or the full platform — start where the value is, scale on the same foundation.
Enrichment recovered invisible patients. Models re-ranked the call plans. Reps acted — and impact was measured continuously against matched controls, independently, not self-reported.
First drafts 70% complete, in hours. Every claim traceable to its source page — hallucination detection and numerical fact-checks built in. Live across AMNOG and GRD; filings land months sooner.
Commercial AI metrics from live production deployments with Top-10 global pharma — field impact independently measured against matched controls by Definitive Healthcare. Detailed case studies available under NDA.
Pharma experts and engineers embedded with your teams — from first assessment to scaled deployment, with transparent pricing.
Your AI capabilities scored use case by use case — basic to advanced — so you know exactly where you stand.
A phased plan with value gates — sequenced to what your organisation can absorb.
A forward-deployed team inside your workflow — pharma operators and engineers, not a ticket queue.
First use case live in production in under 12 weeks — on your data, in your stack.
Impact measured against matched controls — then expand what works to the next use case.