Analytics agents need the context your team keeps in their heads.
Cassis is where it lives: a living context layer between your data and your business. Sharpened by every conversation, governed by your data team, fueling all your agents.
The context was always scattered and stale. Analytics agents turn that into production risk.
Every table, dashboard, and metric hides human decisions. Without that context, agents guess.
Business evolves, the data stack changes, new domains open. Without maintenance, agents drift.
Hey @data-team the board deck says 4,215 active customers but the revenue dashboard says 3,892. Which one do I use? Exec meeting in 2h
Board deck pulls from Looker, anyone with a login in the last 90 days. Revenue dash uses Metabase, filters on paid_plan = true Both are "active customers."
Based on the customers table, you currently have 5,104 active customers.
Great, now we have four numbers. Which one goes on the slide.
Cassis is where agents find the context your team trusts.
Before SQL gets written, Cassis turns the question into a trusted data path: the concepts involved, the definitions your team approved, the joins that are valid, and the rules that shape the answer.
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Cassis builds and keeps your context alive.
Cassis bootstraps from your data stack and documentation. From there, every question, correction, schema change, and ambiguity enriches the ontology.
based on active status
Governance without becoming the bottleneck.
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The queue handles itself.
Related issues are grouped, traced to the definition or model behind them, and ranked by potential impact.
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Drift surfaces before it breaks.
Schema changes, rule changes, and rename collisions arrive as flagged updates, not Slack alerts from finance.
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Every ontology object has receipts.
Owner, source, edit history, last review. Roll back any change. Trace any answer to the context that produced it.
Numbers you can defend in the meeting.
Every answer shows its work.
Ambiguity surfaces. It does not hide.
Cross-domain questions get the joins right.
Augments your trusted data stack.
Notes on data, meaning, and context
A blank beats a guess: assembling context for analytics agents
Your stack already holds much of the context an analytics agent needs. Here's how to recover what's there without inventing what isn't.
What breaks when you point an analytics agent at your dbt project
GitLab, Mattermost, Cal-ITP: 13 public dbt projects and 5,284 models. Here is the context their metadata still cannot establish for an analytics agent.
Your data catalog is certified, owned, and quietly wrong
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.
Discuss your analytics agentproject with us
Book a call with our founding team, compare your setup with what the most advanced data teams are doing, and see how Cassis can help you build trusted agents grounded in governed, maintained context.