Workato moves your data.
Rig understands it.
Workato is a drag-and-drop workflow builder for enterprise, it shuttles data between systems beautifully. But it doesn't understand what your data means or how it connects, and hence can't be used to answer the analytical questions your team relies on to make decisions. That's Rig.
Why every Workato workflow needs a data context layer
Workato is great at moving data between systems, but the second a workflow needs to understand the underlying data and how it relates, it needs a ChatGPT node with a long prompt that explains the schema to it. You end up hand-writing files that describe your tables, joins, metric definitions, and business logic, just so a Workato agent has something to reason over. Across a large warehouse, that takes months, and it starts going stale the moment your schema changes.
Without Rig, here's what "data-driven Workato" actually costs you:
- →Drop a ChatGPT node into every recipe with a long prompt describing your schema, tables, columns, and joins
- →Define metric logic and business rules in YAML or prompts that someone has to author and own
- →Repeat that work for every new data source you connect
- →Catch and patch every schema change, dropped columns, renamed tables, new enum values
- →Re-validate recipes after each warehouse update so they don't silently return wrong numbers
- →Months of work upfront, ongoing maintenance forever, and the context goes stale the moment you stop tending it
With Rig, the data context is generated for you
A context layer that auto-builds from your warehouse, no hand-written data files
Self-updating as schemas drift, so it never goes stale
Plugs into Workato as a node: any recipe step can ask Rig a question and get a governed, audited answer
Sandboxed SQL with RBAC and a full audit trail, so AI access is governed by default
300+ native integrations and MCP support, so Rig can also drive end-to-end actions when Workato isn't already in the loop
Days, not months, to first data-driven workflow
Already on Workato?
Drop Rig in as a node. Your existing recipes keep running, and any step that needs to reason over warehouse data calls Rig instead of brittle hand-written context files. See how the context layer works
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Common points of confusion
Both platforms talk about "modeling data," but they don't mean the same thing.
Workato models data in motion: events flying between SaaS tools, getting normalized and acted on.
Rig models data at rest: the warehouse where everything those events generated eventually lands, and where the hard questions get asked.
| Aspect | Rig | Workato |
|---|---|---|
| What gets modeled | Data at rest: the warehouse, tables, columns, joins, business terms, certified metrics | Data in motion: API payloads, webhooks, business events flying between SaaS tools |
| Layer of the stack | Semantic layer, meaning of the business ("active customer," "qualified pipeline," "MRR") | Transport layer, shape and routing of the message between systems |
| What "context" means here | A semantic understanding of your warehouse so an LLM can generate governed SQL | A clean, typed event payload an automation can act on |
| Where it shines | Plain-English analytical questions answered with audited SQL across BigQuery, Redshift, Snowflake, Postgres | Routing, syncing, and triggering across hundreds of SaaS connectors |
| What you'd trust it for | Answer questions like "find data on school renewals and prepare an evidence pack" | Reliably moving a Stripe charge into Salesforce in seconds |
Snippets that read "Workato generates data context" are technically true at the transport layer. They're easy to misread as "Workato understands your warehouse." It doesn't, and isn't trying to.