The AI-native operating system for real-world data and evidence generation.

A private-deployed agentic platform that takes a study question from raw real-world data to an audit-ready evidence pack, with your scientists approving every step. The bottleneck in evidence generation is not the model. It is the manual workflow around it.

High governance Enterprise regulatory grade Audit-ready outputs

EvidenceReproducible · logged Focus 030% Move to the center to focus

Built for the scrutiny pharma work gets.

High governance

Agents plan and verify; approved steps run as versioned code. A human can check and approve every step before the next one runs.

Enterprise regulatory grade

Built for GxP and designed around 21 CFR Part 11, EU Annex 11, HIPAA and GDPR. Deployed in your cloud, with your keys.

Audit-ready outputs

Data, code, environment and seeds are pinned, so any result reruns identically. Every estimate links back to source records, and every action, check and sign-off is logged for QA, regulators and HTA.

See it answer.

Two study questions, taken from raw real-world data to an evidence pack, with sign-off at every step.

AgilityChange a cohort, comparator or outcome and rerun everything. No rebuild.

SpeedMonths of manual work become days of review.

PrecisionHumans approve every step. Every result is reproducible.

All context in one knowledge graph.

Targeted knowledge graphs, built for your setup and for each agent, and richer with every study. They never leave your cloud or feed a shared model.

ConnectedMolecules, targets, studies, cohorts and SOPs, linked.

CompoundingEvery study and decision enriches the graphs. The next agent gets faster.

GroundedAgents reason over the graphs, so every answer leads back to its source.

Central Dogma Logged · Part 11

Which real-world cohorts match the eligibility criteria in protocol draft v3?

Protocol v3 → eligibility criteria Concept sets → checked against OMOP Claims + EHR → licensed, de-identified Matched cohorts, with every step recorded

An agent for every step of the evidence lifecycle.

Every agent reuses the same graphs and the same GxP-ready governance layer, so each new one deploys faster.

Flagship · Evidence

RWE Agent

Automates the evidence workflow end to end: raw data to decision-ready evidence in one GxP-ready workflow. Your scientists approve every step.

ExplorationData mapping and quality checks. Cohort building and feasibility.

Target trial emulationEligibility, treatment strategies, time zero. Confounding adjustment and sensitivity analyses.

Reporting and visualizationStudy report drafted for expert review. Tables, survival curves and evidence pack.

Flagship · Compliance

GxP Agent

GCP, GLP, GMP, GDP, GVP, Part 11 and Annex 11. Every agent action, check and sign-off is logged, traceable and auditable, as the work runs.

Document Synthesis
Protocols, study reports, briefings and regulatory documents
Every statement linked to its source
Manuscript drafts

Conference Insights
Abstracts, posters and sessions
Scientific and competitive insight reports
Live session summaries

Trial Prediction
Forecasts safety, probability of success and outcomes
Flags enrollment, endpoint and site risks early
Test protocol changes before you commit

Platform
Orchestrator: decomposes the question, coordinates the agents
Privacy Agent: de-identification, consent, residency
Critic checks at every stage

Your cloud. Your graph. Your keys.

Deployed in your AWS, Azure or GCP tenancy. Keys, network and identity stay with you Nothing is shared between customers: no storage, embeddings or models Embedded in each team's workflow and tools, not bolted on Model-agnostic. Your enterprise model choice is a configuration, not a rebuild

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Built by people who have run the studies.

Decades building enterprise technology, designing and managing clinical studies, and producing the real-world evidence behind them.

i.Clinical study design

ii.Real-world evidence

iii.Regulated enterprise tech