Scattered evidence
Literature, internal studies, protocols, and operational records are difficult to search consistently.
Human-supervised pharmaceutical AI
Move medicine forward with intelligence you can inspect.
We connect discovery, clinical, manufacturing, and support data to focused AI workflows—so qualified teams can find signal sooner, review the evidence, and stay accountable for every decision.
Built for accountable use. Source-linked outputs, evaluation gates, and human approval are part of the workflow—not an afterthought.
Research question
Which evidence supports this target across preclinical and translational datasets?
Traceable synthesis
Review ready
Evidence summary with source links, model trace, and reviewer state.
The operating challenge
Important knowledge is spread across systems, teams, and stages.
Pharmaceutical teams rarely lack data. They lack a safe, efficient way to connect it to the next decision. Generic AI can retrieve text, but regulated work needs stronger boundaries, traceability, and review.
High-cost review
Experts spend too much time locating context instead of assessing it, synthesising it, and making the call.
Accountability gap
Teams need a visible path from source to recommendation, with evaluation and approval before action.
The platform
A modular AI layer designed around pharmaceutical work.
Start with one bounded workflow, prove value and controls, then expand on a shared foundation for identity, retrieval, evaluation, and audit evidence.
Evidence engine
Connect the right sources before generating an answer.
Permission-aware retrieval brings together approved literature, internal documents, structured datasets, and knowledge graphs—with citations attached.
Explore the evidence engineMeasure usefulness before production.
Task-specific gold sets, groundedness checks, red-team cases, and reviewer feedback make quality measurable.
See our research postureDeliver inside the tools teams already use.
Design copilots, guided assistants, and review queues around real operating procedures.
View solutions
Governance by design
Keep evidence, permissions, and human decisions visible.
Role-based access, data boundaries, versioned prompts, source traces, and approval states support controlled operation and audit preparation.
Review trust principlesBuilt around your team
Focused applications. Shared responsible-AI foundations.
Every engagement begins with the decision your team needs to make—not with a generic list of AI features.
Research & discovery
Prioritise targets, map evidence, compare programmes, and accelerate literature synthesis with traceable context.
- Target and indication landscape
- Literature and patent synthesis
- Experiment planning support
Clinical & medical
Navigate protocols, evidence, safety information, and real-world data without losing source context.
- Protocol and CSR intelligence
- Medical review dossiers
- Signal and evidence summaries
Manufacturing & quality
Support faster information retrieval, investigation preparation, and controlled document workflows.
- Deviation and CAPA support
- Batch knowledge retrieval
- Change-control assistance
Patient access
Create consistent, accessible support experiences that know when to hand a conversation to a person.
- Approved education and guidance
- Case routing and triage
- Conversation quality review
How we deliver
Prove the workflow before scaling the technology.
A focused implementation creates a practical foundation: one decision, clear success measures, visible controls, and an operating owner.
Start a discovery conversation-
01
Frame the decision
Define the user, task, source boundary, risk, and measurable outcome.
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02
Build the evidence path
Connect approved data and make citations, confidence, and gaps visible.
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03
Evaluate with experts
Test realistic cases with domain reviewers before any production decision.
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04
Operate with control
Add human approval, monitoring, feedback, and a clear rollback plan.
Responsible by design
Useful AI should show its work.
Our delivery model keeps the chain from question to recommendation inspectable. Teams can see which sources were used, where the system was uncertain, how it was evaluated, and who approved the outcome.
- ✓Grounded answers with source links
- ✓Task-specific evaluation and red-team testing
- ✓Role-based access and data minimisation
- ✓Human review for consequential decisions
Trust centre
Controls that support regulated, changing environments.
Security, privacy, quality, and AI governance need to work together. We document assumptions and design the operating model around your requirements.
Data boundaries
Approved sources, access controls, retention choices, and deployment patterns.
Model governance
Versioned configurations, evaluation records, change control, and monitoring.
Human oversight
Approval states, escalation paths, and clear ownership for every workflow.
Operational evidence
Traceable artefacts designed to support audits, investigations, and review.
Build the first useful workflow
Bring us the decision your team cannot make quickly enough.
We will help clarify the use case, evidence requirements, risks, and the smallest responsible path to a useful pilot.