SaturnSQL ContextComing soon

The context layer for AI

AI tools guess what your columns mean, and the numbers come out wrong. Context teaches Claude, ChatGPT and Cursor what your data means, so they answer the way your team would.

See plans

€29/user/month · Pro plan, with Guard

Context · prod-postgres
Notes for AI
Amounts are in cents. Use orders_v2, not orders.
Definitions
active customerBought something in the last 90 days
last_order_at > now() - interval '90 days'
Tables and columns
orders_v2One row per paid order
totalOrder total in cents, before VATedited
statuspaid, refunded or voidfrom database
customer_idThe buying companyfrom database
Claude·get_context
intent: "revenue by active customer"

instructions: "Amounts are in cents. Use orders_v2, not orders."
definitions: [{ name: "active customer", sql: "last_order_at > now() - …" }]
tables: [{ name: "orders_v2",
  columns: [{ name: "total", description: "Order total in cents, before VAT", synonyms: ["revenue"] }] }]

What an AI needs from your database

The schema says what exists. It doesn’t say what amounts are in, which of two tables is current, or what your team means by an active customer.

The AI seesIt also needs to knowWhere it goes
orders.total bigintIn cents, before VATColumn description
orders, orders_v2Use orders_v2. orders is the old systemNotes for AI
customers.last_order_atAn active customer bought in the last 90 daysDefinition, with its SQL
acct_mgr_idPeople say account manager or ownerSynonyms
COMMENT ON TABLE customersAlready written in the databaseRead as it is

How it works

  1. 1

    Write down what only your team knows

    Which table is current, what the amounts are in, what counts as an active customer. A sentence each, in the app or straight from a conversation with the AI.

  2. 2

    Every AI tool your team uses reads it

    Claude, ChatGPT, Cursor and Claude Code get the notes that fit each question, so nobody has to explain the data again.

  3. 3

    Comments in the database count too

    Table and column comments in PostgreSQL, Redshift, MySQL, SQL Server, Oracle, BigQuery and ClickHouse come along. Where your team writes its own, that wins, and the database stays untouched.

  1. Schema
    orders_v2.total bigint
  2. Database comment
    Order total
  3. SaturnSQL
    Order total in cents, before VAT
  4. What Claude reads
    total bigint: "Order total in cents, before VAT"

Correct the AI once

When you tell the AI it got a column wrong, it can save the correction for everyone. The next person, and the next AI tool, gets it right without being told. Every change has a name on it.

Danatotal is in cents and excludes VAT. Remember that.
ClaudeCalling set_description on orders_v2.total. Saved for every AI tool on prod-postgres.
Recent changes
Danadescribed orders_v2.totalAI tool · 14 Nov, 10:42

Context layer vs semantic layer

Context is not a semantic layer, and doesn’t try to be. A semantic layer compiles governed metrics; Context tells an AI what your data means so it can write the SQL itself.

SaturnSQL ContextSemantic layer
What you build firstNothing. Write notes as questions come upModels, metrics and joins in YAML, LookML or views
How AI uses itReads the meaning, then writes SQLQueries governed metrics through an API
Who maintains itAnyone who knows the data, in plain languageUsually analytics engineers, in code
Same number every timeGuided by definitions, not guaranteedYes, metrics compile to the same SQL
Time to first answerMinutesOnce the first models are written and deployed

If finance needs revenue to match to the cent in every tool, model it in a semantic layer. For everything else, context gets AI answers right from day one. See the best semantic layer tools and Atlan alternatives for the heavier options.

No lock-in

Start from the Apache Ossie model you already have, and take everything your team writes with you as Ossie or dbt YAML on every plan. If you adopt a semantic layer later, start from here.

How Apache Ossie import and export work

prod_postgres.ossie.yamlor schema.yml for dbt
version: 0.3.0.dev0
name: prod_postgres
ai_context:
  instructions: Amounts are in cents. Use orders_v2, not orders.
datasets:
  - name: orders_v2
    description: One row per paid order
    fields:
      - name: total
        datatype: Integer
        description: Order total in cents, before VAT
        ai_context:
          synonyms: [revenue, amount]

AI that understands your data and only sees what it should

Context and Guard come together on the Pro plan, and Guard’s rules apply to context too.

More on SaturnSQL Guard and database governance for MCP and AI.

Questions about Context

What is a context layer for AI?
A context layer is what an AI tool reads about your data besides the schema: what tables and columns mean, which units they use, which table is current, and how the business defines its terms. With it, an AI writing SQL picks the right table and the right filter instead of guessing from column names.
What is SaturnSQL Context?
SaturnSQL Context is a context layer for AI on the databases you already have. Your team writes down what the data means, as notes for AI, definitions, descriptions and synonyms, and Claude, ChatGPT, Cursor and Claude Code read it through SaturnSQL’s MCP server, together with the table and column comments already in your database. It comes with the Pro plan, €29 per user per month, together with Guard.
What is the difference between a context layer and a semantic layer?
A semantic layer, such as dbt’s, Cube or LookML, defines metrics, joins and models in code, and tools query those metrics so a number comes out the same every time. It needs modelling first. A context layer describes the data in plain language so an AI can write the SQL itself. It works from day one but does not guarantee identical numbers. Many teams start with context and add a semantic layer for the few metrics that must never drift.
Do I need a semantic layer for AI?
Not to get useful answers. Most wrong AI queries come from missing meaning, such as amounts in cents or an old table that looks current, and plain-language context fixes those. If finance needs governed metrics that match to the cent across every tool, a semantic layer is the better fit, and you can export SaturnSQL Context to Ossie or dbt YAML as a starting point.
Which databases does Context work with?
Context reads table and column comments on PostgreSQL, Redshift, MySQL, SQL Server, Oracle, BigQuery and ClickHouse. Notes, definitions, descriptions and synonyms work on every connection SaturnSQL supports. Snowflake and Databricks are not covered.
Can an AI tool update the context?
Yes. Through the MCP tools set_description, set_descriptions (up to 200 tables and columns at once), set_instructions, upsert_definition and delete_definition, an AI tool can save what you explain in a conversation, and import_ossie brings in a whole Apache Ossie model after showing a preview. It needs the person to own the connection or be an admin, a plan with Context, and an API key or sign-in with the write scope. Each change is recorded with the person’s name. The same changes can be made over the REST API at /api/context, and export_ossie gives an AI tool the context as Ossie YAML.
Can I export my context?
Yes, on every plan. Export a connection as Apache Ossie YAML, the open semantic model format formerly called Open Semantic Interchange, or as a dbt schema.yml with models or sources. Notes for AI and definitions go along, so nothing you write is locked in. On the Pro plan you can also import an Apache Ossie model, and SaturnSQL shows what will change before it saves.
What does Context not do?
It is not a metrics engine: definitions guide the AI, which still writes the SQL. It does not crawl lineage or catalogue other tools, it does not describe relationships between tables yet, and it does not cover Snowflake or Databricks. If you need those, a semantic layer or a data catalog is the better choice.
How much does Context cost?
Database comments reach AI tools on every plan with the MCP server, from Starter at €19 per user per month. Writing your own notes, definitions, descriptions and synonyms comes with the Pro plan at €29 per user per month, together with Guard. Pro is coming soon. Join the waitlist to hear when it opens.

Connecting an AI tool? Start with the SaturnSQL MCP server.

SaturnSQL ContextComing soon

Give your AI tools the meaning, not just the schema

Explain your data once, and every AI tool your team uses works from it.

See plans

€29/user/month

Pro plan: everything in Starter, plus

  • Context: notes for AI, definitions, and table and column descriptions every AI tool reads with the schema
  • AI tool access per connection: off, shared queries only, read-only or same as the user
  • Approvals: risky AI tool requests wait for a person, alerted in Slack or by email
  • Limits on AI tool queries for each database or group: rows per query, queries a minute per person, timeout, BigQuery bytes
  • Access rules per group: hide tables, hide or mask columns, filter rows
  • Audit log of every query, from people and AI
  • Query version history

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