The data context for your AI tools

    Auto-generated from your warehouse, governed by your team, queried accurately by any user.

    Try RigBook a demoSee comparisons
    AI toolsAgentsAppsData WarehouseCRMERP
    Rig Context Layer

    You can't let AI agents loose on the database

    The same question, asked of the same warehouse, gives two very different answers depending on what the AI actually knows.

    Sales Ops · 9:42 AM

    What's our net revenue retention for enterprise accounts last quarter?

    Without Rig

    Used warehouse

    Snowexecute_query14 tables, 2.1 GB

    NRR last quarter was roughly 147% based on fct_revenue.

    What's missing

    No flag that this used gross revenue, wrong grain, ignored row-level access, or scanned tables the user shouldn't see.

    With Rig

    Used Rig integration

    Rigsearch_metricsnet_revenue_retention
    Rigget_metric_detailscustomer × quarter
    Rigexecute_sql1 row returned

    NRR for enterprise in 2025 Q4 was 112%.

    How it resolved

    Resolves to your certified net_revenue_retention metric, filtered to segment = 'enterprise' for period = '2025Q4'. Rows scoped per RBAC.

    The warehouse didn't change. The context did.

    But semantic metrics only cover
    10% of your data

    The rest is tribal knowledge: how your team actually defines "active users" or "revenue".
    Rig captures it in two ways.

    Save a usage rule: enterprise means ARR > $100k and contract length ≥ 12 months.

    Used Rig MCP ›
    Rigcreate_business_termenterprise
    Rigcreate_usage_rulesegment.enterprise

    Saved.

    Reply…

    Opus 4.6

    Every definition — whether added in chat or the UI — becomes available to every AI tool and teammate.

    Your data changes. Your context keeps up

    Columns get added, tables get renamed, definitions evolve. Rig watches for drift and re-ingests automatically. No stale prompt files, no manual refresh.

    1. Warehouse change detected

      A new plan_tier column is added to users in production.

      + plan_tiertextNew

    2. Drift flagged

      Rig notices the schema diff within minutes — no manual review, no stale prompt files.

      02:14 UTC · auto

    3. Context re-ingested

      Metadata, embeddings, sample values and join candidates refreshed for the affected tables.

    4. Context updated

      3 metrics re-validated · 1 join relearned · all agents see the new column on their next call.

    You write business logic once. Rig keeps the plumbing current.

    Give your team governed
    and accurate data access

    RigRig Agents
    Claude / Cursor
    Fin / Decagon
    Powers every agent

    Rig Context Engine

    Auto Data ContextDrift DetectionAgent SandboxAccess GovernanceBusiness Logic
    Auto-discovery
    Snowflake
    BigQuery
    Redshift
    & many more

    • Automatically detects drifts in your schema
    • No stale prompt files or manual semantic layer

    Rig's context layer is the foundation for work that relies on data

    Rig Connect serves it through MCP. Rig Automate lets you work with it.

    Try Rig on your dataBook a demo