Permission-aware retrieval
Search only approved sources, with user and role context carried into each result.
The AI Pharma platform
Built around evidence, orchestration, and control.
A modular foundation for pharmaceutical AI workflows—connecting approved information to expert review without hiding how the answer was produced.
Reference architecture
One foundation, clear responsibility at every layer.
The platform separates the user experience, workflow logic, intelligence services, and governance controls. Each layer can evolve without obscuring the decisions made above it.
Experience
Guided copilotsSearch & synthesisReview queuesPatient support
Orchestration
Task routingTool useHuman approvalEscalation
Intelligence
Model servicesStructured extractionKnowledge graphsEvaluation
Control plane
IdentityPermissionsData boundariesVersion & audit records
Designed for change. Models, tools, data sources, and policies can be versioned independently while the workflow remains testable and reviewable.
Evidence engine
Retrieve, structure, and show the evidence path.
Reliable pharmaceutical AI starts with a controlled information boundary. The evidence engine brings retrieval, transformation, and citation handling into one inspectable path.
Structured synthesis
Extract entities, relationships, claims, and evidence gaps into a reviewable format.
Source-linked output
Attach citations, document locations, confidence signals, and unresolved limitations.
Workflow orchestration
Turn models into controlled operating steps.
The orchestration layer coordinates retrieval, analysis, validation, and human review. It can ask for missing information, stop at a defined gate, or route an exception to the right owner.
- 01Model routing
Choose an approved model and configuration for the task, sensitivity, and latency. - 02Tool controls
Constrain which data, actions, and external services a workflow can access. - 03Review states
Represent draft, verified, approved, rejected, and escalation states explicitly. - 04Recovery paths
Capture failures and fallbacks without silently repeating a consequential action.
Source-leveltraceability for generated claims
Config-levelrecords for model and workflow versions
Review-levelvisibility for approvals and exceptions
Evaluation layer
Define what “good” means before deployment.
A general benchmark cannot establish fitness for a pharmaceutical task. We create representative cases with the people who will use and review the system.
Task-specific test sets
Expected answers, acceptable variation, missing evidence, and failure conditions defined with domain experts.
Groundedness & completeness
Checks that claims are supported, relevant, appropriately qualified, and not missing important limitations.
Adversarial testing
Prompt injection, sensitive data, conflicting evidence, out-of-scope questions, and unsafe requests.
Human review study
Compare assisted and unassisted work for time, agreement, reviewer confidence, and error discovery.
Deployment
Match the environment to the data and operating requirement.
There is no single universal deployment answer. The right design depends on sensitivity, latency, integration, regulatory context, and the organisation’s ability to operate the service.
Managed service
A faster shared-service option when approved data boundaries and operational requirements permit it.
Private cloud
Isolated network, storage, identity, and monitoring controls for a dedicated environment.
Customer environment
Deployment inside an approved VPC, on-premises stack, or customer-managed service boundary.
Operations & governance
Make ownership visible after go-live.
Production operation includes monitoring, change control, access review, feedback handling, incident response, and periodic re-evaluation. We agree these responsibilities explicitly rather than treating launch as the end of the project.
Operational evidence
Version records, source references, review outcomes, and workflow events for investigation and audit preparation.
Continuous improvement
Feedback signals feed a controlled evaluation set; changes are tested before they alter production behaviour.
Human accountability remains explicit. The platform assists qualified users. It does not make autonomous prescribing, release, or other regulated decisions.
Start with the decision
Build a platform around a real pharmaceutical workflow.
Tell us which decision is slow, inconsistent, or difficult to audit. We will help frame the smallest responsible implementation.