Semantic SQL · Any database · Your SQL client

Ask your data
what it means.

MEANS, CLASSIFY, TOPICS, SUMMARIZE — 100+ AI operators that work like native SQL. Connect any database. Query from DBeaver, DataGrip, or Tableau. No Python. No notebooks. No new tools.

Start free Try the playground

PostgreSQLMySQLSnowflakeBigQueryClickHouseS3the webany API

query.sql

SELECT name, region,
       SENTIMENT(feedback) AS mood
FROM   customer_reviews
WHERE  feedback IMPLIES
         'might cancel soon'
ORDER  BY mood;

name	region	mood
Sarah K.	Northeast	-0.82
James R.	West	-0.71

2 rows·41ms·cached

Down the rabbit hole

Start with a single semantic filter. By end of week, you're running verified AI aggregations across your entire warehouse.

Day 1—Connect & query

start.sql

-- Point at your database
CREATE CONNECTION prod
  TYPE postgres HOST 'db.company.com'
  DATABASE 'analytics';

-- Query it with meaning
SELECT *
FROM   support_tickets
WHERE  body MEANS
  'billing dispute';

→ 23 rows matching semantic intent

Day 3—AI creates the dimensions

topics.sql

SELECT
  TOPICS(tweet, 4) AS topic,
  CLASSIFY(tweet,
    'political, not-political')
    AS political,
  COUNT(*) AS tweets,
  AVG(likes) AS avg_likes
FROM   twitter_archive
GROUP BY topic, political;

→ You didn't define the topics. The data organized itself.

Week 1—Your own operators, verified

operator.sql

CREATE SEMANTIC OPERATOR
  compliance_risk(clause VARCHAR)
  RETURNS VARCHAR
  PROMPT 'Evaluate this contract clause
    for regulatory risk. Return: low,
    medium, or high with one sentence
    explaining why.
    Clause: {{ input.clause }}';

SELECT compliance_risk(clause):takes(3,
         'Pick the most conservative')
       AS risk_assessment
FROM   contracts;

→ Custom operator + ensemble verification. Built in SQL, runs in SQL.

wonderland

100+ built-in operators

Filtering, classification, summarization, parsing, validation, embeddings — all callable from SQL.

Intent & meaning

MEANSIMPLIESCONTRADICTSSIMILAR_TO

Classification

CLASSIFYSENTIMENTINTENTTOXICITY

Dimensional

TOPICSTHEMESCLUSTERCONSENSUSSUMMARIZE

Data quality

VALIDATEDEDUPECORRECTFILL

Parsing

PARSESMART_JSONPARSE_ADDRESSPARSE_NAME

Embeddings

EMBEDVECTOR_SEARCHEMBED_COLUMN

Build your own operators

Define custom AI functions in SQL DDL — no Python, no deploy pipeline. CREATE it, then SELECT with it.

operator.sql

CREATE SEMANTIC OPERATOR
  compliance_check(text VARCHAR)
  RETURNS VARCHAR
  PROMPT 'Check if this text violates
    our compliance policy.
    Return: pass, warn, or fail.
    Text: {{ input.text }}';

Then use it immediately:

SELECT clause, compliance_check(clause)
FROM   contracts
WHERE  compliance_check(clause)
       MEANS 'fail';

The web is a database. Query it.

Scrape web pages, search the internet, parse RSS feeds, or connect any API — all from SQL. Results compose with semantic operators and JOIN with your own data. No ETL. No scripts. No new tools.

Built-in web operators

WEBSCRAPERSSWEB_SEARCHWEB_MAPWEB_EXTRACT

API federation via MCP

Connect any MCP server. Its tools become SQL functions.

GitHubSlackLinearNotionGoogle Driveany MCP server

federated.sql

-- Scrape competitor pricing, analyze semantically
WITH competitor AS (
  SELECT * FROM WEB(
    'https://competitor.com/pricing',
    tier VARCHAR, price DECIMAL,
    features TEXT
  )
)
SELECT
  tier, price,
  features MEANS 'AI-powered' AS has_ai
FROM competitor
WHERE price < 100;

→ Scraped, parsed, and semantically filtered — in one query.

mcp.sql

-- Connect a service
CREATE MCP SERVER github
  COMMAND 'npx'
  ARGS '-y @anthropic/github-mcp';

-- Discover its tools with semantic search
SELECT tool_name, description
FROM   mcp_server_tools('github')
WHERE  description MEANS
         'find bugs';

-- Query it — with semantic SQL
SELECT title, assignee,
  CLASSIFY(body,
    'bug, feature, support') AS type,
  SENTIMENT(body) AS frustration
FROM   github.issues(
         'myorg/api', 'open')
WHERE  body MEANS
         'performance regression'
ORDER BY frustration ASC;

→ Connect → discover → query. Semantic operators compose across any data source.

Your data never moves

DataRabbit speaks the PostgreSQL wire protocol. Connect from psql, DBeaver, DataGrip, Tableau, or anything that talks to Postgres. Queries run where your data lives.

Bring your databases

PostgreSQL, MySQL, Snowflake, BigQuery, ClickHouse, S3, Parquet. Data never leaves your infrastructure.

Write semantic SQL

Use your existing SQL client. Add operators like MEANS, CLASSIFY, or SUMMARIZE alongside standard SQL.

LLM once, SQL forever

The system fingerprints data shapes — not individual values. A million phone numbers might have 10 formats. 10 LLM calls generate SQL expressions. The expressions run on every future row. No LLM needed.

Built for production, not demos

LLM outputs are non-deterministic. DataRabbit has first-class primitives to make them reliable, auditable, and cost-efficient.

Takes

Run N model variations in parallel. An evaluator picks the best. No serial retry loops.

Shape compiler

Fingerprints data structures, generates SQL expressions, caches the code not the values. A million rows, ~10 LLM calls. Cost drops toward zero over time.

Query memory

Every query is embedded and summarized. Search past analyses by meaning. 'Did anyone look at churn by region?' surfaces the answer — and the SQL.

Few-shot training

Flag any operator result as 'correct.' Future calls automatically include your validated examples. No ML pipeline. No fine-tuning. Just click 'this was right.'

Your team already knows SQL.
Now it knows AI.

Free tier includes 5,000 credits. No credit card. Connect any database in 60 seconds. If you already have a SQL client open, you're ready.

Credits scale with AI usage. Cache hits are free. The more you use it, the cheaper it gets.

Start free

Works with PostgreSQL, MySQL, Snowflake, BigQuery, ClickHouse, S3, Parquet. Connects via pgwire — use psql, DBeaver, DataGrip, Tableau, or any Postgres-compatible client.

Ask your datawhat it means.