Describe your business. Ship the warehouse and the ontology.

OffDataAI is an AI data modeling and ontology platform. You can start from a plain-English interview, one of 174 templates, the drag-and-drop Model Builder, or the Database Modeler (import an existing database). It generates ERDs, platform-native DDL, complete dbt projects, realistic seed data and documentation for Snowflake, BigQuery, Databricks, Redshift, Postgres, Synapse, Microsoft Fabric and DuckDB, plus a business ontology your AI agents can use.

OffDataAI is a purpose-built pipeline: an interview agent gathers grain, cardinality, and SCD requirements; a synthesis agent compiles your answers into a validated Intermediate Representation (IR); validators check referential integrity and type coercions before any code is generated; and platform-specific generators emit DDL, dbt scaffolds, and seed CSVs. ChatGPT can sketch a schema, but OffDataAI ships warehouse-ready artifacts that are tested and consistent across paradigms (Kimball, Data Vault 2.0, 3NF).

The IR is a validated JSON document that captures your entire data model — entities, attributes, relationships, grain, and platform hints. It is the single source of truth that every generator consumes. You can inspect, edit, and version it like any other artifact.

OffDataAI uses OpenAI's GPT-4o, served through Microsoft Azure OpenAI, for the interview, model synthesis, the Refine assistant and ontology enrichment, and OpenAI's text-embedding-3-small (also on Azure) to search the documents in your knowledge bases.

Yes. Ask the Refine assistant in plain English (for example "add a promotions dimension"), or edit the model directly in the app. Changes flow through the validators and every downstream asset regenerates automatically.

OffDataAI ships native DDL and dbt projects for Snowflake, Google BigQuery, Databricks (Delta), Amazon Redshift, PostgreSQL, Microsoft Synapse, Microsoft Fabric and DuckDB. Each generator respects platform-specific syntax, clustering, partitioning, and data type rules.

Yes. OffDataAI supports three modeling paradigms out of the box: Kimball dimensional modeling (star and snowflake schemas), Data Vault 2.0 (hubs, links, and satellites), and Third Normal Form (3NF). You choose the paradigm — the synthesis agent shapes the IR accordingly.

A complete dbt project with source YAML definitions, staging models, mart models, schema tests, and seed CSVs (filled with realistic rows on Pro and above). It is ready for `dbt build` from day one.

Your descriptions, interview answers and the schemas and documents you attach are sent to Microsoft Azure OpenAI for processing; Azure OpenAI does not use API data to train models. From connected databases we read metadata only (tables, columns, keys), never rows. Your projects and uploaded files are stored in OffDataAI's PostgreSQL database and Cloudflare R2 object storage.

Yes. The Database Modeler connects to PostgreSQL, MySQL, Snowflake, BigQuery, Amazon Redshift, Databricks (Unity Catalog). It reads only the catalog (tables, columns, keys, indexes and comments, never your rows), suggests undeclared relationships for you to accept, classifies tables as facts, dimensions and more, and builds a project from it: mirrored 1:1, or remodeled as a star schema or Data Vault.

Yes. Connect GitHub, GitLab or Bitbucket and push the generated DDL, dbt project, seed SQL, documentation and the canonical IR to a branch in one click, optionally opening a pull request, so the output is reviewed and version-controlled in your real workflow.

Yes. Every column carries a classification and an optional masking strategy. OffDataAI inventories PII, reports masking coverage and compliance tags (e.g. HIPAA, GDPR), and generates platform-appropriate masking-policy DDL — for example Snowflake masking policies — so regulated teams can enforce governance from day one.

Yes. Every edit creates a new IR version. OffDataAI diffs any two versions and generates the exact ALTER TABLE / CREATE TABLE migration SQL for your target platform, so you can evolve a live warehouse safely instead of hand-writing DDL.

Yes. Invite teammates as viewers or editors, share a read-only link with stakeholders who don't have an account, and leave inline comments on the model for review and sign-off.

Yes. Create an API key and use the public REST API to create projects, fetch the generated IR and pull platform-native DDL, which is ideal for CI pipelines and internal tooling. The same keys connect AI agents to the built-in MCP server.

Yes, 174 of them, free on every plan. The template gallery offers complete, validated domain models across 18+ industries (banking, healthcare, insurance, SaaS, retail, SAP, Salesforce, telecom and more) in Kimball, Data Vault 2.0 and 3NF. Opening one creates a project with the ERD, DDL and dbt project already generated, with no interview needed.

Synthesis runs in the background. OffDataAI can notify you by email and/or Slack the moment your model is ready — or if a run needs attention — so you don't have to watch a progress bar.

Yes. The Free plan costs nothing and needs no card: 3 projects, all 174 templates, the Model Builder canvas, the ERD, SQL for every platform, dbt and docs, and creating an ontology from your model. AI features use credits that come with the paid plans: Starter ($29/month, 60 credits) adds the AI interview, the Refine assistant, uploading your schemas, dictionaries and sample files, and one knowledge base for your documents (10 documents); Pro ($99/month, 250 credits) adds seed data, migrations, AI ontology enrichment and 5 knowledge bases (100 documents); Pro also adds the public API, the MCP server, comments and share links, and publishing dbt to GitHub or GitLab; Team ($399/month, 1,200 credits) adds the Database Modeler, live warehouse connections, governance and lineage, and unlimited knowledge bases (1,000 documents). Enterprise is custom.

An ontology is the business meaning layer on top of your data model: concepts (Customer, Order), relations between them, and metrics (Revenue = sum of order amount). OffDataAI drafts it from your model, puts every AI suggestion in a review queue, and lets you publish it with resolvable IRIs and export it as OWL Turtle, JSON-LD, RDF/XML or JSON.

OffDataAI includes an MCP (Model Context Protocol) server. Connect Claude or any MCP client with an API key and it can search concepts, get the query context for a question, find join paths, run read-only queries against your connections and propose new concepts for review, so agents write SQL against the right tables and definitions.

The Model Builder is a drag-and-drop canvas for designing entities and relationships by hand, with a full-screen mode and one-click auto-arrange. It offers Kimball, Data Vault 2.0 and 3NF building blocks (facts, dimensions, hubs, links, satellites, bridges and more), saves automatically, exports to JSON or PNG, and can be the source of an ontology. It needs no AI credits.