Context Management

A context sandbox,
per team.

Each team gets its own scoped sandbox of company context. One install, governed centrally, every query auditable.

How it works.

  1. 01Connect once. Butter ingests Notion, Slack, dbt, Sheets, Confluence into a single knowledge graph.
  2. 02Scope per team. Define which entities each role can see - metrics, experiments, SOPs, decisions.
  3. 03Every query is filtered. The model receives only the context that team is authorized to see, every time.

Context Management vs. system prompts.

System promptsButter
Where context livesPasted into every prompt or stored per appCentralized at the data layer, one source
Per-team scopingRebuilt per workflow, drifts over timeScoped at ingestion time, governed centrally
AuditabilityNo audit trail of what the model sawEvery query and every context delivery logged
Permission changesManual prompt edits across surfacesOne graph update, propagated everywhere

Questions.

What is context management for AI?

Context management for AI is the practice of deciding what business context flows into LLM prompts and who can see it. With Butter, it becomes governance at the data layer rather than copy-paste at the prompt layer. Each team's queries are enriched only with the context they are authorized to see.

How is this different from row-level security in a data warehouse?

Row-level security gates raw data. Butter gates context: definitions, SOPs, decisions, experiments. A salesperson and an analyst querying the same metric should see different framings, caveats, and depth. Butter handles that, governed centrally, without rewriting prompts per role.

Can different teams have different definitions of the same metric?

Yes. Butter's knowledge graph is versioned and namespaced by team. The Retail team's definition of active customer can differ from Wholesale's, and Butter routes the right one to the right team automatically.

Does this work with my existing AI tools?

Yes. Butter runs as an MCP server. It plugs into Cursor, Claude Code, Claude Desktop, Notion AI, ChatGPT, and any LLM-powered workflow that supports MCP. No model lock-in.

Is everything auditable?

Every query and every context delivery is logged. You can review what context any team has been seeing, version-by-version, and trace any answer back to the exact context that produced it.

How is this different from Glean or other enterprise search?

Enterprise search retrieves documents to the user. Butter delivers governed context to the model. The user never sees the search step. They see a better answer, scoped to what their role is authorized to know.

Govern AI access at the context layer.

We are rolling out access by team. Tell us how you'd scope context across analysts, sales, and CS. We'll reach out within a few business days.

Request access