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The question travels.
The data doesn't.

Celato is building the inference layer for real-world evidence. Pharma asks the question, computation runs inside every connected hospital, and one exact, auditable answer comes back.

No patient-record movement Agentic study design in MDQL Exact aggregate answers
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Federated RWE inference layer

Real-world evidence without moving the data.

Celato transforms fragmented medical data into reproducible, auditable, research-grade evidence. Scientists stay in the loop, definitions are versioned, and every result can be traced from question to output.

10x faster insights. 100% hospital control. Zero risk to patient data.

How a Celato inference run works A research question is designed by Celato, compiled into a versioned study definition, executed locally inside hospital boundaries, and returned as exact aggregate evidence only. YOUR QUESTION How many eligible patients, and where? Versioned MDQL design Agentic · reproducible · audited HOSPITAL BOUNDARY Site A · approved Executed locally Site B · approved Notes + structured records Site C · in review Custodian approval pending Raw patient records never leave this boundary RETURNED TO YOU Exact aggregate answer only
Agentic study design Federated hospital execution MDQL versioned definitions Auditable exact answers

Opportunity & problem

The RWE gap is slowing medical innovation.

Healthcare needs data-driven decisions, but patient data is fragmented, siloed, and impossible to move. The result is real-world evidence that is slower, more expensive, and less complete than modern medicine needs.

Fragmented and siloed data

Incomplete study populations lead to missed trial opportunities and flawed evidence before the analysis even begins.

Local data only

Without a global signal, pharma and regulators are left with blind spots that delay market access and weaken decisions.

Slow and costly access

Traditional data access stretches R&D cycles, drains budgets, and delays therapies that depend on better evidence.

Delayed, low-quality insights

When access is hard, teams ask simpler questions, produce weaker evidence, and lose opportunities permanently.

Solution

The new infrastructure for real-world evidence.

Celato enables fast, scalable generation of high-quality RWE by turning distributed hospital data into reproducible, auditable, research-grade answers.

01 · Faster insights

Ask richer questions across sites in days, not quarters.

Pharma asks a research question in natural language. Celato decomposes it into a study design, then compiles that design into MDQL - a precise, versioned definition that can run across the network.

  • Natural-language questions become structured study logic
  • MDQL makes every definition reproducible
  • Criteria can be tested, revised, and rerun quickly
See a worked study example
QUERY RESULTS BY NODE Site A184 Site B137 Site C91 Site D58 Site E34 Aggregate counts only · illustrative
02 · Hospital control

Run across the network while every hospital keeps control.

Hospital nodes harmonize local EHR schemas, codes, and free text to a canonical clinical model at the source. The query runs locally, under strict governance, and patient records stay put.

  • Structured data and clinical notes are standardized in place
  • Each custodian approves what runs and what leaves
  • Zero patient-record movement by design
Discuss the hospital workflow
LOCAL STANDARDIZATION Hospital node EHR schemas, codes, notes Approved release Aggregate statistics only Patient data stays inside
03 · Exact evidence

Aggregate distributed results into one defensible answer.

Celato combines node results into a mathematically exact answer with a full audit trail, so sponsors can move from slow one-off studies toward continuous evidence.

  • One answer across every approved node
  • Every definition, run, and output is auditable
  • Post-market first, then expansion into core R&D
Discuss an RWE program
EXACT AGGREGATE ANSWER Baseline12 mo24 mo Treated Comparator · illustrative

How it works

From question to exact answer.

The scientist stays in the loop at every step. Every definition is versioned, every hospital keeps control, and every result is auditable.

01

Ask

Pharma asks the research question in natural language.

02

Design

Celato's agent decomposes it into a precise study design and MDQL definition.

03

Standardize

Hospital nodes harmonize schemas, codes, and free text at the source.

04

Execute

The query runs locally at every approved node under strict governance.

05

Aggregate

Node results combine into one mathematically exact answer with a full audit trail.

The agentic layer turns research intent into governed distributed computation.

Live view
How a research question becomes exact federated evidence A research question moves from the researcher into Celato's agentic design layer, compiles into MDQL, executes inside hospital data environments, and returns exact aggregate output. Patient-level records never leave the hospital boundary. 01 · RESEARCH INTENT Life-science research team Question and hypothesis Cohort logic Endpoint definition 02 · AGENTIC LAYER Celato study design Question decomposition MDQL compilation Versioning and audit CUSTODIAN BOUNDARY 03 · CUSTODIAN DATA Hospital environments Structured tables Notes and reports Local execution design execute Patient-level records never cross this line

The agentic layer is the first two steps: ask and design. Everyone else is building agents over data they had to centralize first; Celato builds them over data that never moves.

Agentic design MDQL Standardization Local execution Exact aggregation
Everyone is building agents over data they had to centralize or individually consent first. Celato builds them over data that never moves.

For hospitals and data custodians

Join a research network without giving up control.

Celato lets hospitals participate in global RWE studies while data stays inside the local boundary. You approve the study definition, the local execution, and the aggregate release.

Control every run

Each study is scoped, reviewed, and approved against your own policies before computation begins.

Turn local data into research value

Your institution can support sponsor-funded studies as a partner while keeping patient records in place.

Connect once, answer many questions

The same governed node can support feasibility, post-market surveillance, safety, and continuous evidence workflows.

Governance & security

Built so the answer is defensible.

Celato is designed for the places where centralization and broad patient consent do not scale. The computation moves to the data, and the evidence that returns is versioned, governed, and auditable.

  • No patient-record movementRecords never leave the custodian environment. Only approved aggregate outputs are returned.
  • Approval before executionEach institution reviews and approves the study definition against its own policy before anything runs.
  • Reproducible by designMDQL logic is versioned. Any result can be regenerated and traced back to the question that produced it.
  • Complete audit trailQuestion, design versions, policy checks, local execution, aggregation, and release are all recorded.
  • Exact aggregate answersDistributed node results are combined into one mathematically exact answer without exposing patient-level records.

Get started

Bring one question.

Tell us the real-world evidence question you would ask if access, privacy, and fragmented data were no longer the obstacle. We'll show how it becomes MDQL, where it runs, and what exact aggregate answer comes back.

Please don't include patient-level information in this form. We'll only use your details to respond to this enquiry.