Can we bring our existing object model?

Your existing model is the reference. The ontology is rebuilt from source against it and reviewed by your team as pull requests, so nothing depends on exporting from another platform.

What happens if we stop working with TextQL?

You keep everything. The ontology, metrics, apps and agent definitions are files in your own repository, readable without us.

Where does our data live?

Where it lives today. TextQL deploys in your cloud, on-prem or air-gapped, reads data in place, and enforces your permissions on every call.

Transformations / own your ontology

Own the Ontology That Runs Your Business

Your objects, metrics, apps and agents — as code in your own repository, on the stack you already run. Whoever you work with next, it stays yours.

Book a demo See how it works

[ the difference ]

Who Holds the Keys

today

The ontology lives inside the vendor’s platform
Data replicated into their environment
Logic in a language only their platform runs
Leaving means starting from zero

with textql

The ontology lives in your own GitHub
Data read in place, inside your cloud
Definitions in open, portable files
Leave any time — you keep all of it

[ 01 model ]

An Ontology Built From What You Already Run

Object types, links and metrics are inferred from your warehouses, applications and documents — then reviewed by your team as pull requests, not hand-built for months inside someone else’s platform.

Inferred from the systems you run, in place
Every object approved as a pull request
Kept current as your systems change

[ 02 own ]

One Definition. Any Model. Anywhere.

Every definition is a file with a history. Swap the model that reads it or the place it runs, and the answer doesn’t move — because the logic lives in your repository, not in a platform.

Models repointed without rewriting logic
Runs in your cloud, on-prem or air-gapped
Every change reviewed and attributable in git

[ customer story ]

client: Scale
profile: software services · san francisco, ca
owner: the data team · one semantic layer
status: live · ops, finance, growth & hr

6 tools 1 agent across every system of record

4 teams 1 layer ops · finance · growth · hr, governed together

Scale

Four business units. One layer the data team owns.

I suggest you try TextQL on your messiest datasets, hook it up to your worst codebase and documents, and ask the most complicated question that actually drives your business.

Heqing Huang, Director of Analytics, Scale AI

~1,900

data requests answered every week

business units on one semantic layer

layer, owned by the data team

Read the full story

See Your Ontology in Your Own Repository

Pick one workflow the current platform runs. We connect to the same sources inside your cloud, rebuild it as code in your repository, and run it side by side.

Book a demo Talk to an expert

01A 30-minute call to choose the workflow and its sources
02We connect in place — nothing is replicated out of your cloud
03Your team reviews the ontology and the app as pull requests

[ use cases ]

What Teams Move First

01 Ontology, rebuilt as open files
Object types, links and properties rebuilt from source with your existing model as the reference — then versioned in your own repository.
02 Operational apps as code
Internal apps for review, triage and approval rebuilt as Data Apps: code you can read, test and change.
03 Pipelines without a proprietary builder
Transformations expressed as reviewed code against the warehouse you already run, instead of a platform-specific pipeline tool.
04 Agents on your permissions
Agents that act through the same row- and column-level access your people have, with every step logged.
05 Health plans and payors
Claims, members, providers and contracts modeled as your IP, with payment integrity and cost of care running on top.
06 Government and regulated estates
Single-tenant or air-gapped deployment, with the ontology kept inside your own walls and your own repository.

[ faq ]

What Teams Ask Before They Start

Talk to the team

No. TextQL connects to the same sources in place and runs beside it. One workflow moves first; the rest is decided by results and your renewal date.

[ try textql ]