Business logic changed
How many accounts activated last month?
accounts.status = 'active' 2,431 accounts activated last month.
first_deployment.status = 'succeeded' 1,847 accounts activated last month.
Schemas change, definitions move, and nobody edits the context repo. Cassis catches the drift, drafts the fix, and proves it against evals. So your agents are always served current, approved context.
First answer in about two minutes. Read the quickstart
01/06 · Listing maintenance issues
02/06 · Drafting the metric proposal
description: > Accounts whose first successful production deployment occurred in the requested period. Internal accounts excluded. display_name: Activated accounts domain_path: product/activation expression: COUNT(DISTINCT "ACCOUNT_ID") filters: '"IS_INTERNAL" = FALSE' name: activated_accounts synonyms: - activated accounts - activated customers table_name: FIRST_DEPLOYMENT table_schema: PRODUCT unit: accounts
03/06 · Validating the context files
04/06 · Testing against the eval suite
05/06 · Pushing the review branch
06/06 · Opening the GitHub pull request
description: > Accounts whose first successful production deployment occurred in the requested period. Internal accounts excluded. display_name: Activated accounts domain_path: product/activation expression: COUNT(DISTINCT "ACCOUNT_ID") filters: '"IS_INTERNAL" = FALSE' name: activated_accounts synonyms: - activated accounts - activated customers table_name: FIRST_DEPLOYMENT table_schema: PRODUCT unit: accounts
How many accounts activated last month?
accounts.status = 'active' 2,431 accounts activated last month.
first_deployment.status = 'succeeded' 1,847 accounts activated last month.
MEDIAN(deployments.duration) 3 context references affected
acme-data wants to merge 1 commit into main from cassis/activated-accounts
Evidence: 6 observed conversations · Proposal only
The repository above is a specimen. Browse a real context repo on GitHub, CI gates included.
Cassis uses your schema, dbt project, documentation, and questions. Your warehouse or existing agent executes the generated SQL. Row data stays in your environment. Evals score by SQL equivalence review.
Cassis executes generated SQL through a dedicated read-only role. Access is limited to the datasets your agents use, with no warehouse writes. Evals execute the generated and expected SQL and compare result rows.
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Cassis manages Anthropic inference through Amazon Bedrock.
Use your Amazon Bedrock or Google Vertex AI account.
Connecting your own warehouse starts with a conversation. One domain's schema is enough, as a read-only connection string or a DDL dump. Talk to us.
Connect Cassis to your Git repository and install the Cassis CLI. Then give it your schema: a scoped read-only warehouse connection, or a DDL dump if you would rather not connect one yet.
Run the bootstrap from your coding agent. Cassis writes the first context tree to your repository from your schema, dbt project, BI exports, query history, and docs.
Connect Claude Code or any MCP-compatible agent. Cassis can answer questions, generate SQL, or return only the context the calling agent needs.
List detected issues from your development environment, apply and test proposed fixes, then merge the approved pull request to publish a new context version.
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Freshness controls can tell you a definition is old. Real use can reveal that it is wrong. We checked fifteen vendors to see who closes the loop.