Fragmentation
Different schemas, controls, permissions, and extraction patterns make each new request feel like a custom integration.
Bring the query to the data, not the data to the query.
Celato.ai connects life-science teams to clinical and real-world data where it lives through secure, governed AI workflows. Hospitals gain a standardized, research-ready layer across structured and unstructured sources without moving sensitive patient-level records.
Computation moves into approved environments. Patient-level records remain contained.
Raw patient-level data remains within the custodian environment.
Every workflow includes governance checkpoints.
Query logic and outputs remain reproducible and traceable.
A governed bridge between research intent and distributed clinical data.
Celato turns research questions into auditable query plans, routes them through custodian-controlled policy checks, and runs computation where the data already lives.
Real-world evidence is distributed. The work around it is still too manual.
Clinical data spans hospitals, payers, registries, and research networks. Moving and harmonizing patient-level records is slow, expensive, and risk-sensitive.
Governance burden
Requests travel through repeated contracts, approvals, exports, and manual reviews before analysis can begin.
Slow iteration
When a cohort definition changes, a brittle pipeline often has to be rebuilt instead of updated as reproducible logic.
Distributed clinical sources
Schema fragments
Governance checkpoint
Local execution boundary
Approved outputs
A research-ready workspace that keeps control where it belongs.
Celato is a solution for the custodian as much as it is for the researcher: keep sensitive data local, standardize structured and unstructured sources, and support more research without rebuilding pipelines.
Harmonize tables, codes, and definitions into a consistent analytics layer without copying raw records out of the custodian environment.
Before Celato
After Celato
A credible working surface for governed research questions.
This representation shows how a research question becomes cohort logic, custodian approvals, local execution, governed results, and an audit trail.
Research question
Identify an eligible cohort for a multi-site real-world evidence analysis.
Eligible population estimate
Coverage validating
Participating custodians
Site A Policy review
Site B Approved
Site C Pending scope
Query plan
- Validate coverage metadata
- Compile cohort criteria
- Run approved local execution
- Return governed aggregate output
Audit trail
Question drafted
Criteria versioned
Policy check recorded
Federated infrastructure with governance in the path, not around the edge.
Celato’s architecture connects research intent, query planning, policy enforcement, local execution, and standardized outputs into one traceable workflow.
Architecture detail
Research question and intent
Define the question, population, endpoint, and analysis intent before any execution begins.
One bridge, two trust-sensitive workflows.
Iterate faster across multi-site evidence.
Define cohorts, run governed analysis, and reproduce query logic across participating custodians.
Discuss a research workflowKeep control while supporting more research.
Standardize structured and unstructured sources in place, route approvals, and return governed outputs.
Discuss a custodian workflowBring a real use case. We’ll show how the workflow stays governed.
Tell us whether you are exploring Celato as a research team or as a data custodian. The form keeps the conversation focused on your operating model.