offdata ai — agentic AI for data modelers and data engineers

Data Modeling

The AI data modeling tool that ships real warehouses

Describe your business domain in plain English. OffDataAI designs the data model for you — entities, relationships, keys, and history — then generates ERDs, DDL, a complete dbt project, and seed data. No drag-and-drop diagramming, no hand-written SQL.

Get startedBook a demo
model/ir.jsonjson · intermediate representation
{
  "model": "saas_analytics",
  "paradigm": "kimball",
  "entities": [
    {
      "name": "dim_customer",
      "type": "dimension",
      "scd": 2,
      "grain": "one row per customer version",
      "attributes": [
        { "name": "customer_sk", "type": "surrogate_key" },
        { "name": "customer_id", "type": "natural_key" },
        { "name": "plan_id", "type": "string" },
        { "name": "valid_from", "type": "date" },
        { "name": "valid_to", "type": "date" }
      ]
    },
    {
      "name": "fct_usage",
      "type": "fact",
      "grain": "one row per API call event",
      "measures": ["api_calls"],
      "dimensions": ["dim_customer", "dim_date"]
    }
  ]
}

OffDataAI is a purpose-built AI data modeling tool. Where most modeling tools make you draw entities by hand, OffDataAI interviews you about your domain — grain, cardinality, slowly-changing dimensions — and compiles your answers into a validated, typed data model called the Intermediate Representation (IR). The IR is the single source of truth: it enforces referential integrity, keeps data types consistent, and feeds every downstream generator. From one conversation you get an editable model, an ERD, platform-native DDL, a runnable dbt project, and realistic seed data — for Snowflake, BigQuery, Databricks, Redshift, Postgres, Synapse, and Fabric.

What OffDataAI generates

  • Model from natural language

    Describe the business, not the schema. The interview agent asks the right follow-ups and OffDataAI infers entities, relationships, keys, and grain.

  • Kimball, Data Vault 2.0, or 3NF

    Pick the paradigm your team uses. The same domain description compiles to star schemas, hub/link/satellite models, or normalized 3NF.

  • Validated, versioned models

    Every model is a typed IR that passes referential-integrity checks. Each edit creates a new version you can diff, roll back, and turn into migration SQL.

  • Generate everything downstream

    ERDs, native DDL, a full dbt project with tests, and seed data — all generated from the same model and kept in sync as it changes.

Frequently asked questions

What is an AI data modeling tool?
An AI data modeling tool turns a description of your business — entities, relationships, and how data is used — into a structured data model and the code to build it. OffDataAI runs a guided pipeline: an interview agent gathers grain, cardinality, and history requirements; a synthesis agent compiles a validated Intermediate Representation (IR); validators check referential integrity; and generators emit ERDs, DDL, and a full dbt project. The result is a warehouse-ready model, not a sketch.
Do I need to know SQL to model data with OffDataAI?
No. You describe your domain in plain English and answer a few targeted follow-up questions. OffDataAI handles keys, foreign keys, surrogate keys, data types, and slowly-changing-dimension logic for you, and emits the SQL automatically. You can still inspect and edit every artifact.
Which modeling approaches does it support?
Three first-class paradigms: Kimball dimensional modeling (star and snowflake schemas), Data Vault 2.0 (hubs, links, satellites), and Third Normal Form (3NF). You choose the paradigm and the model is shaped accordingly — from the same domain description.
Can I edit the data model after it's generated?
Yes. Every model is captured as a versioned Intermediate Representation. Edit it in the UI or via the API, and the validators re-run and downstream artifacts (DDL, dbt, ERD, seed data) regenerate automatically. Every change creates a new version you can diff and roll back.
How is this better than modeling in a generic chatbot?
A general chatbot can sketch a schema, but it won't enforce referential integrity, won't keep types consistent across platforms, and won't hand you a runnable dbt project. OffDataAI is a purpose-built pipeline with validators and platform-specific generators, so the output is reproducible and ready to deploy.
Which databases and warehouses can it target?
Snowflake, Google BigQuery, Databricks (Delta), Amazon Redshift, PostgreSQL, Microsoft Synapse, and Microsoft Fabric. The same model compiles to native DDL for each — respecting platform-specific types, clustering, and partitioning rules.

Explore data modeling with OffDataAI

Your data warehouse is one conversation away.

Describe your domain, or open one of 150+ production-grade templates. ERDs, DDL, and a complete dbt project — generated in under a minute.

Get startedTalk to usBrowse templates