SDCStudio · public beta

Build self-describing
data models.

Turn CSV data, Markdown templates and plain ideas into semantic data models that keep working across systems, domains and languages.

Start building See how it works

sdcstudio — processing pipeline

The processing pipeline: input, AI-assisted modeling, generation, deployment. From CSV or Markdown to a deployed semantic application.

sdcvalidator on PyPI SDCRM · MIT BFO · ISO/IEC 21838-2 W3C XSD 1.1 · RDF · OWL · SHACL

What it does

Six things, and none of them lock you in

AI-assisted

The model is proposed, you decide

Vertex AI discovers candidate semantic relationships, suggests ontology mappings and drafts documentation. A domain expert still signs off, which is the point: the expert authors the model, the machine does the typing.

Inputs

CSV, JSON, PDF, Markdown, DOCX, or nothing at all

Import what you have or build from scratch in the visual editor. Thousands of rows process in seconds.

Standards

SDC4, and a dozen more you already answer to

XML Schema 1.1, RDF, OWL, SHACL and BFO (ISO/IEC 21838-2), plus the rest of the stack. There is no private format anywhere in it.

Outputs

Export to everything, deploy anywhere

XSD schemas, XML instances, JSON and JSON-LD, HTML documentation, RDF triples, SHACL constraints and GQL statements.

Catalog

Reuse is free, and it compounds

Browse and reuse published components at no cost. The more components exist, the cheaper it is for everyone to build the next model. That is the whole economic argument.

Validation

Check the output without trusting us

Every model validates against the open-source sdcvalidator, Apache-2.0, on PyPI. Run it locally, offline, with no account.

How it works

Data to semantic model, in three steps

Bring your data

Drop in a CSV, paste JSON, upload a PDF of requirements, or start from nothing in the visual editor.

Draft the model

The AI analyses your data, proposes relationships and ontology mappings, and drafts an SDC4-compliant model for your expert to correct.

Export and deploy

XSD, XML, JSON, RDF, SHACL, GQL or HTML. Deploy anywhere. The model is yours in open standards.

Pricing

No subscriptions, no seat limits, no model caps

Buy credits and spend what you use. Reuse costs nothing, which is deliberate: the catalog only gets cheaper for everyone as it grows.

Browse the catalog

Free

Explore published reference models and reuse existing components at no cost. Download schemas and validate locally with the open-source validator.

Build models

Pay as you go

Mint new components and assemble data models. Reusing catalog components stays free. You pay when AI creates something new, or when you publish a finished model.

Settle in production

$1.00 per Receipt

Issue a Settlement Receipt through the Verifiable Settlement Layer: the instance validated against its published model, the governance decision recorded, the Receipt signed and bound to both parties. 1,000 credits per Receipt. Verifying one is free, with no account.

Build self-describingdata models.