VerifiedEnd-to-EndHuman Control

Agentic data engineering.

DataSQRL gives your team the capacity to build, deploy and operate data products and pipelines with confidence.

Talk to an engineer See the lifecycle →
pipeline.sqrl
/** Payments with the account at payment time */
EnrichedPayments := SELECT p.*,
    a.customer_id, a.risk_tier
  FROM Transactions p
  JOIN Accounts FOR SYSTEM_TIME AS OF p.paid_at a
    ON p.account_id = a.account_id;
  1. Change #2196 of 6 stages
  2. BuildOne SQL specification, written by the agent
  3. VerifyCompiled, real workload simulated
  4. ApprovePull request approved by your engineer
  5. DeployBlue-green, beside the live version
  6. OperateMonitored, lineage recorded
  7. MaintainSource changes come back as tested fixes
  8. In production, with every step managed for compliance.

01 / In production

Medidata, a Dassault Systèmes company, runs 18,000 customer data pipelines on DataSQRL.

90%
TCO reduction
3
Months from Proof of Value to production
0
Additional hires while workload tripled

Built by the creator of JanusGraph and leaders from DataStax and Confluent.

Read the Medidata story →Who we are →

02 / The real work

AI can write data pipelines in minutes. The real bill comes after the code.

That bill: making sure your team understands the code, meeting compliance requirements, deploying changes safely, keeping results correct as sources change, finding and fixing what breaks. DataSQRL automates that whole lifecycle, checks every step and leaves every decision with your engineers.

03 / What you get

Data engineering capacity you can rely on.

  • Verified

    Nothing reaches production unchecked.

    • A compiler checks types, keys, schemas and connectors.
    • Validators enforce your policies; a simulator replays real event histories.
    • Deployment analysis shows what a change affects before it rolls out.

  • End-to-End

    From the first request to years in production.

    • Every deployment asset is generated and deployed from one specification.
    • Lineage, schemas and metrics are recorded for every pipeline.
    • Blue-green deployment: fixes are tested beside the live version.

  • Human Control

    Your engineers approve every change.

    • Every change is a pull request with its test evidence.
    • One readable SQL specification per pipeline.
    • Runs in your cloud account or on-premises, with the model you choose.

04 / The whole lifecycle

The AI model is the smallest part.

An AI model writes code. DataSQRL surrounds it with everything a production pipeline needs, from the first request to years of operation, and closes the feedback loop at every stage.

An AI model as a small chip inside seven DataSQRL layers: coding harness, compiler, simulator and tests, verifier, judges, deployment automation, lineage and observability; human approval on every change
  1. BuildAn agent writes the change as one SQL specification your team can read.
  2. VerifyThe compiler, validators and simulator check it and generate every deployment asset.
  3. ApproveIt arrives as a pull request with test results and lineage. Your engineer decides.
  4. DeployDeployment analysis first, then blue-green: the new version runs beside the current one.
  5. OperateMonitoring, lineage and metadata are recorded for every pipeline, in your environment.
  6. MaintainWhen a source changes or something breaks, a tested fix comes back as a pull request.
Explore the platform →

05 / Start where you are

Capacity for the use cases
already on your list.

Pick one use case your business is waiting for, and DataSQRL builds it, verifies it on your data and runs it. Nothing to rip out or replace.

  • Customer 360

    One current view of each customer for advisors, agents and service teams.

  • Transaction monitoring

    Real-time fraud and risk signals from payment and account events.

  • Regulatory and risk reporting

    Reports and dashboards fed from one verified definition, traceable to source.

  • Per-customer data delivery

    A governed pipeline for every customer or sponsor, operated at scale.

  • Clinical and operational data

    Data platforms and APIs for trial, care and supply operations.

  • Data for AI agents

    Governed, access-controlled data served to AI applications as APIs and MCP tools.

06 / First step

Start with one use case. Decide on evidence.

A scoped Proof of Value on a use case you already have, with success criteria agreed before we start.

See results in weeks How the Proof of Value works →