offdata ai — agentic AI for data modelers and data engineers

PostgreSQL

AI PostgreSQL schema generator

Describe your domain in plain English. Ship a Postgres warehouse — DDL with proper constraints, indexes, JSONB, and declarative partitioning, plus a complete dbt project.

ddl/postgres.sqlpostgresql
CREATE TABLE analytics.dim_customer (
  customer_sk   TEXT          NOT NULL PRIMARY KEY,
  customer_id   TEXT          NOT NULL,
  name          TEXT,
  plan_id       TEXT,
  valid_from    DATE          NOT NULL,
  valid_to      DATE,
  metadata      JSONB         DEFAULT '{}'::jsonb,
  CHECK (valid_to IS NULL OR valid_to >= valid_from)
);

CREATE INDEX idx_dim_customer_customer_id
  ON analytics.dim_customer (customer_id, valid_from);

CREATE TABLE analytics.fct_usage (
  usage_id      TEXT          NOT NULL,
  customer_sk   TEXT          NOT NULL,
  plan_id       TEXT          NOT NULL,
  event_ts      TIMESTAMPTZ   NOT NULL,
  api_calls     BIGINT        NOT NULL,
  PRIMARY KEY (usage_id, event_ts)
) PARTITION BY RANGE (event_ts);

CREATE TABLE analytics.fct_usage_2026_05
  PARTITION OF analytics.fct_usage
  FOR VALUES FROM ('2026-05-01') TO ('2026-06-01');

OffDataAI is purpose-built for PostgreSQL. Our generator emits Postgres-native DDL with the right features: declarative range/list partitioning on facts, JSONB for semi-structured columns, generated columns, CHECK constraints for domain validation, and indexes tuned to the queries you described. From the same conversation, you also get a complete dbt project configured for dbt-postgres.

What OffDataAI generates

  • Postgres-native DDL

    JSONB, TIMESTAMPTZ, NUMERIC(p,s), declarative table partitioning, GENERATED ALWAYS AS columns, and CHECK constraints — never generic ANSI SQL.

  • Kimball, Data Vault 2.0, or 3NF

    Three paradigms supported. 3NF is especially well-suited to Postgres operational workloads.

  • Full dbt project

    dbt_project.yml configured for dbt-postgres, sources, staging, marts, schema tests, and seeds. dbt build works on the first run.

  • Indexes and constraints

    Primary keys, foreign keys, unique constraints, and supporting indexes for the queries you described — all emitted automatically.

Frequently asked questions

Can OffDataAI generate PostgreSQL DDL from plain English?
Yes. Describe your business domain in plain English. OffDataAI's interview agent gathers grain, cardinality, and indexing hints, synthesizes a typed Intermediate Representation, and emits PostgreSQL-native DDL with proper constraints, indexes, partitioning, and JSONB columns where appropriate.
Does OffDataAI use Postgres-specific features like JSONB and table partitioning?
Yes. The Postgres generator uses JSONB for semi-structured columns, declarative table partitioning (PARTITION BY RANGE / LIST) for large facts, GENERATED ALWAYS AS columns, and CHECK constraints for domain validation.
Does it support Kimball, Data Vault, and 3NF on Postgres?
Yes. All three paradigms are first-class. 3NF is especially common on Postgres for operational workloads; Kimball is supported for analytical schemas.
Does it generate a dbt project for Postgres?
Yes. OffDataAI emits a complete dbt project for Postgres — dbt_project.yml configured for dbt-postgres, sources, staging, marts, schema tests, and seed CSVs.
How is this different from asking ChatGPT for Postgres DDL?
ChatGPT can sketch DDL, but rarely uses correct Postgres idioms (JSONB vs JSON, declarative partitioning, NUMERIC precision) consistently. OffDataAI runs a dedicated Postgres generator after a validated IR — the output is reproducible and Postgres-correct.

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.