Privacy-preserving RWE, powered by federated intelligence

Bring the query to the data, not the data to the query.

Celato.ai connects life-science teams to clinical & real-world data where it lives. Execute analytics across fragmented sources without moving sensitive patient-level data.

See how it works Why Celato

Faster insights

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Reduce time-to-cohort & iteration

Lower risk

No raw patient data exfiltration

Federated by design

Works across siloed sources

At a glance

Celato Bridge

A secure bridge between data custodians and research teams. Orchestrate queries, governance, and reproducible analysis with minimal operational overhead.

Privacy-first

Run analytics without moving raw data.

Explainable & auditable

Reproducible query plans and governance.

Composable

Plugs into existing data stacks & tools.

Governed collaboration

Custodians retain control & approvals.

Talk to us

The problem

Real-world evidence is fragmented across hospitals, payers, registries, and research networks — but moving and harmonizing patient-level data is slow, expensive, and risky.

Fragmentation

Silos everywhere

Data lives in many environments with different schemas, controls, and permissions.

Compliance

High governance burden

De-identification, contracting, and audits add months to research timelines.

Iteration

Slow trial-and-error

Cohort definitions change; data pipelines don’t keep up.

Celato’s answer

Celato.ai makes federated analysis practical: orchestrate secure queries and feature extraction across distributed datasets while keeping sensitive data in place.

How it works

A federated workflow that moves computation to the source, with clear governance checkpoints.

Flow 01 Discovery

Find & validate data

Identify relevant sources, confirm coverage, and align on governance requirements.

Flow 02 Cohorts

Define cohorts & features

Translate inclusion/exclusion logic into auditable, reproducible query plans.

Flow 03 Evidence

Run analysis safely

Execute federated analytics and return only governed outputs (e.g., aggregates, features).

Key building blocks

Policy & approvals — custodian-controlled governance gates.
Federated execution — compute runs where the data lives.
Audit trails — traceability from question → output.
Reproducibility — versioned logic, consistent results.

What teams get

Faster cohort iteration, lower compliance risk, and a scalable path to multi-site evidence.

Technical moat

A federated approach requires more than connectors — it needs governance, orchestration, and trust.

Federated query planning

Translate analysis intent into a safe, auditable plan that runs across heterogeneous environments.

Bring computation to data

Minimize transfers, reduce exposure.

Guardrails by default

Policy checks + consistent governance flow.

Trust & adoption

Data custodians keep control — researchers get answers faster, without negotiating new pipelines each time.

Custodian-controlled approvals

Clear checkpoints + audit-ready logs.

Explainable outputs

Traceability from question → result.

Composable architecture

Plugs into existing data stacks & methods.

Let’s talk

Want to see a federated workflow on your data environment? Tell us your use case — we’ll set up a demo.

For pharma & biotech

Accelerate evidence generation and cohort iteration.

For data custodians

Enable governed access without moving raw data.