Case studies

Four deployments. The same engine underneath.

Each engagement is scoped as a work package: a named bottleneck, a measurable outcome, and modules that stay in your environment afterwards. Client names and full references are shared under NDA.

01 · Global pharma
Evidence on demand
02 · Government
Sovereign automation
03 · Public hospital
Inventory intelligence
04 · Top-5 pharma
AI pre-screening
01 · Global pharma · Market access, APAC

A scalable knowledge base — evidence on demand

One governed knowledge base unifies global and local evidence. Dossiers and value stories are generated from it market by market, rather than rebuilt each time.

Evidence in Governed base Evidence out

Global dossiers, local data, clinical and real-world evidence, and literature all lived in separate places. Every market request restarted the assembly work, and no two versions agreed — reused across 8 APAC markets from a single source of truth, creation moved from months to days.

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02 · Government · Sovereign cloud, Singapore

Sovereign workflow automation, work package by work package

Sovereign AI workflows for a government health agency, built from reusable modules inside the Government Commercial Cloud and extended one work package at a time.

WP1 live WP2 live WP3 in build WP4 in build

Rising submission and surveillance volume against fixed headcount, with a hard constraint that no document may leave the agency's environment — a private, in-country deployment with no external data sharing, extended one work package at a time.

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03 · Public hospital · Oncology pharmacy

Agentic inventory intelligence

A sovereign agentic workflow unifies stock, dispensing, and procurement data, forecasts demand, and recommends orders — assembled entirely from registered tools. Non-clinical operations only.

Ingest Harmonise Forecast Recommend Monitor

High-cost oncology stock managed across an ERP, several dispensing exports, and spreadsheets with no shared drug codes or units — now one explainable operating picture, with recommendations shown as reasoning, never auto-selected.

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04 · Top-5 pharma · Clinical trials

AI-driven pre-screening for intermediate AMD

A two-phase pipeline reads real-world ophthalmic records to find eligible patients for an intermediate-AMD study — de-identified, on secure infrastructure, source data resident with the site.

Phase 1 · records Phase 2 · OCT

Historic pre-screen failure of roughly 60%, with eligibility signals sitting in unstructured notes and imaging no manual chart review could cover at scale — targeting twice the historical screen rate, GDPR-compliant throughout.

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We will walk you through the one closest to your problem.

Client names, references, and full results available under NDA.

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ECLYPSE AI

Our proprietary AI orchestration platform for healthcare: one engine, a registry of reusable task modules and domain agents, and a governed knowledge base — deployed inside your walls and run by your team.

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