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. Hospitals don’t just share data — they gain a standardized, research-ready layer across structured and unstructured sources, without moving sensitive patient-level records.

See how it works What hospitals get

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.

Standardize & organize

Make structured + unstructured data research-ready in place.

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. And for hospitals, we add a durable foundation: a standardized, organized layer that makes future research faster and easier—without rebuilding pipelines every time.

For hospitals: organize your data, in place

We’re not coming to “drink” your data. Celato helps you standardize, structure, and govern your clinical data where it already lives—so you can support research requests confidently and repeatably.

Capability 01 Structured

Standardize structured data

Harmonize tables, codes, and definitions into a consistent analytics layer—without copying raw records out of your environment. Support mappings that align with common healthcare standards when needed.

Capability 02 Unstructured

Unlock unstructured data

Turn notes, reports, and documents into research-ready signals via governed extraction—so unstructured data becomes usable alongside structured fields.

Capability 03 Research

Automatic research questions

Build reusable “question templates” (cohorts, endpoints, feasibility checks) that translate into auditable query plans—so new requests become repeatable, fast, and consistent.

What this changes for data teams

Standardize once — stop rebuilding one-off pipelines for each study.
Govern by design — approvals, policies, and audit logs live in the workflow.
Support more research — answer more questions with the same team.

A simple promise

Celato is a solution for the custodian as much as it is for the researcher: you keep control, you gain structure, and you get a repeatable way to serve governed research—without shipping raw data.

Outcome

A research-ready layer spanning structured + unstructured data, with automated, auditable question-to-query workflows.

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.
Standardization layer — unify structured + unstructured into a research-ready foundation.
Question templates — reusable research questions that compile into auditable query plans.

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.

Standardization that compounds

Each dataset becomes more usable over time—structured + unstructured.

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

Standardize & organize data (structured + unstructured) for governed research access.