offdata ai — agentic AI for data modelers and data engineers

Database Modeler

Reverse-engineer any database into a clean data model

Connect Postgres, MySQL, Snowflake, BigQuery, Redshift or Databricks. OffDataAI reads the catalog, finds the relationships nobody declared, classifies every table, and builds a project you can modernize.

Import your databaseBook a demo
database-modeler · analysissnowflake
source      snowflake · ANALYTICS.PUBLIC
tables      142 read  (catalog only, no rows)

inferred relationships
  orders.cust_id → customers.id      0.94  ✓
  order_items.order_id → orders.id   0.97  ✓
  payments.ord_ref → orders.id       0.71  review

classification
  orders           fact        grain: one row per order
  customers        dimension   SCD 2 · PII: email, phone
  order_items      fact        grain: one row per line
  stg_orders_raw   staging     excluded

build       remodel → Kimball · target: snowflake

Most real databases were never designed on a whiteboard. Foreign keys are missing, staging tables sit next to facts, and the only documentation is in someone's head. Rebuilding a model from that by hand takes weeks.

The Database Modeler does the archaeology for you. It reads only the catalog, never your rows, then infers undeclared relationships with a confidence score and the evidence behind each one. Every table is classified as a fact, dimension, bridge, lookup, staging table and more, with grain, PII and history detected. Review the result, then build a project: mirror the schema 1:1, or let AI remodel it as a star schema or Data Vault for the warehouse you are moving to.

What the Database Modeler does

  • Catalog-only introspection

    Tables, views, columns, keys, indexes, constraints and comments, read from the catalog. No rows are selected, and credentials are encrypted at rest.

  • Relationship inference you can review

    Undeclared foreign keys are suggested with a confidence score and evidence. Accept, reject or bulk-accept; only accepted ones are used.

  • Automatic classification

    Facts, dimensions, bridges, lookups, staging and audit tables, plus column roles, PII, SCD type, grain and subject area.

  • Mirror or remodel

    Keep the schema 1:1 for documentation, or remodel it as a Kimball star schema or Data Vault 2.0 for a new platform.

  • Schema drift

    Re-analyze a source and compare snapshots to see exactly what changed before you rebuild.

  • From database to ontology

    Use an imported schema as an ontology source and merge it with your warehouse model into one business vocabulary.

Frequently asked questions

Which databases can the Database Modeler read?
PostgreSQL, MySQL, Snowflake, Google BigQuery, Amazon Redshift and Databricks (Unity Catalog).
Does OffDataAI read my data?
No. The Database Modeler reads only the database catalog: tables, views, columns and types, declared primary and foreign keys, indexes, constraints, comments and estimated row counts. It never selects rows from your tables, so no customer data is read or sent to an AI model. Connection credentials are encrypted at rest.
What if my database has no foreign keys declared?
That is common, so OffDataAI infers them. It looks at column naming, type compatibility, whether the target is a key, indexes, joins inside views and NOT NULL constraints, and gives each suggested relationship a confidence score and its evidence. You accept or reject them, or bulk-accept the high-confidence ones.
How are tables classified?
A rule-based pass always runs and labels tables as fact, dimension, bridge, lookup, operational, staging, audit or junk, and columns by role such as key, measure or flag. It also detects PII, SCD type, grain and subject area. An optional AI pass refines the tables you select. You can edit any classification.
What can I build from an imported schema?
A full OffDataAI project. Mirror keeps your tables 1:1 and is free of AI credits; Remodel redesigns the schema as a Kimball star schema or Data Vault 2.0 using AI. Either way you choose a target platform (PostgreSQL, Snowflake, BigQuery, Databricks, Redshift, Synapse or Fabric) and get the ERD, DDL and dbt project. The imported schema can also feed an ontology.
Can it detect schema changes?
Yes. Re-analyze a connection and OffDataAI shows the drift between snapshots, so you can see what changed in the source before rebuilding.
Which plan includes the Database Modeler?
The Database Modeler is part of the Team plan, along with Git and warehouse integrations, the Knowledge Base and API access.

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