Use internal data in your AI tools
Connect tools like Claude & Cursor to your internal data, with context and governance built-in.
Rig in 70 seconds
You're starting to work insideCoworkCursorCopilotChatGPTClaude CodeWindsurf but how can you give it the right data?
Table not found: user_revenue_2024
Did you mean: rev_summary_v3, acct_data_final2?
LLMs burn tokens and still fail
Without schema context, models hallucinate table names, retry endlessly, and exhaust context windows before getting a useful answer.
-- no WHERE clause
Full table scan detected
2.3B rows · est. $48.20
Direct data access is risky
Raw database access means expensive full-table scans, unfiltered sensitive data, and results with no business context attached.
Provisioning is painful
Setting up data access for every AI agent and internal user individually is slow and painful.
Contextualised, safe data access with 3 clicks
Rig generates and maintains context for your data, and lets data teams provision access for humans and agents.
Use cases
Works for every team that touches data
10x faster dbt modelling
How Rig works
From warehouse to AI tool in minutes
01
Connects to your data sources
Rig reads and understands the data in your structured data sources. Data never leaves its source.
Try Rig02
Generates your data context layer
Business definitions, join paths, usage rules, and metric logic, all auto-generated and editable. Context auto-updates as your schema evolves.
Auto-generated context
17 cols
14 cols
10 cols
Semantic Metric
Usage Rule
03
Edit and add to the context layer
Data teams can add tribal knowledge and business context that tooling can't infer, like edge cases, exceptions, nuanced definitions, and rules only insiders know.
04
Build inside Rig or connect your AI tools
Automate data-heavy processes in Rig or expose a governed MCP endpoint to Claude, Cursor, Copilot, or any agent, in minutes.
MCP guideTrigger
cron
Agentic Researcher
24 rows
Reporter
1018 words
Why Rig
Context and governance that other approaches miss
Direct database access gives AI raw data with no understanding. Rig gives it context.
| Capability | Without Rig | With Rig |
|---|---|---|
| Connecting data | Manual permissioning | One-click oAuth |
| Data context | Manual & slow (or AI slop) | Accurate and maintained |
| Context freshness | Goes stale as schema changes | Detects drifts & updates |
| Tribal knowledge | Write & share .md files | Edit in context database via Claude/Cursor |
| Token use | Big scans & greps, burning tokens | Context via MCP, token-efficient |
| Governance and audit | Build & maintain manually | Ready out of the box |
| Integrations | Manually build & maintain | Built-in automations to CLIs, IDEs, and SaaS tools |