Data infrastructure for business teams.
GTM, RevOps & CX teams use Rig to sync, understand, and build with internal data - inside their AI tools.
Bring your own warehouse - or leave the ingestion, warehouse & modelling to us.
Pick the version of Rig that fits.
Three on-ramps. Same product underneath.
Five steps, raw data to running.
From "we have a CRM and three SaaS tools" to "the rest of the team is shipping on it" inside a working week.
Building a 'data brain'? Do it on Rig.
Managing stale YAML files and writing skill files reliant on 15 MCPs creates tech debt fast. Bring your warehouse to Rig, and we'll take care of the rest.
Building a DIY context & access layer.
- Manually document your data in static files. Gets stale, misses context.
- Integrate multiple MCPs and APIs, handling re-auth and API changes constantly.
- Document grep that returns random chunks or is inattentive.
- A semantic layer only covering 10% of the data estate. Permissions are fiddly to set up in the warehouse, poor governance.
- Every data schema change requires manual updates.
Managed data context, access, and hosting.
- Self-maintaining managed context database.Tables, joins, metrics, PII, business terms, built from your data and kept current by Rig's Gardener as your schema changes.
- Bring your warehouse, or let us handle it.Snowflake, BigQuery, Databricks if you already have one. Otherwise we host the warehouse for you.
- One MCP, all your data fully permissioned.Claude, Cursor, Codex, custom agents. Per-user RBAC, PII masking and full audit enforced through the AI tool.
- Build, host, and share internal apps.Your team ships dashboards, automations and agent workflows on Rig without a frontend engineer.
Your team is already vibe-coding with your data.
Dashboards in Claude. Reports in ChatGPT. Analyses in Cursor. The apps get built, but they live as loose HTML files no one can host, share, or audit, on numbers the AI often gets wrong. Rig Connect puts one governed context and semantic layer underneath, then hosts what your team builds.
- Same question, three different numbers
- No control over who sees the customer data inside
- Nothing logged, nothing a security review would pass
- Accurate numbers, one context + semantic layer
- Role-based access, PII masked, you decide who sees what
- Every query and app logged. Security-review ready
Use cases.
Build anything that relies on data. Here are some ways customers use Rig today.
The bits we're better at.
Five things most teams comparing data stacks end up caring about. They're what the Rig product was actually built around.
Auto-generation.
Rig builds your context layer from your data. Not a blank dbt project to fill in.
Wider than a semantic layer.
Semantic layer plus ingestion, automations, hosting, and an AI interface. We embed, we don't compete.
PII sandbox.
Sensitive data is segregated by default. Rig knows what's PII and treats it accordingly.
RBAC.
Row-level access control out of the box. Compliance-ready from day one.
Headless mode.
Use Rig with your existing warehouse, or as the full stack. Your call.
Connect your data. Try Rig today.
Self-serve, no credit card. If you'd rather talk to a person first, we'll walk you through it.