> ## Documentation Index
> Fetch the complete documentation index at: https://relevanceai-docs-tsp-1332.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# RevOps
> Put AI Agents to work across revenue operations — pipeline hygiene, routing, forecasting — so the GTM machine actually runs on clean data.
RevOps owns the systems that everyone else depends on. Data hygiene, routing rules, pipeline reviews, forecast accuracy — all of it gets done manually because the rules are too edge-casey to hard-code. AI Agents handle the judgment-y middle ground: clear enough to automate, fuzzy enough that hard rules break.
## Start with a Marketplace template
The Marketplace ships pre-built Agents you can clone in one click. Adapt them to your CRM, your segmentation, your pipeline definitions. Or [build a custom Agent from scratch](/build/introduction).
Fills in firmographics, contact details, and job-function classification on every record — the foundation for everything else.
Takes a LinkedIn profile or CRM record, researches the company, and returns a qualification read you can wire into routing.
The full catalog of Agents — enrichment, routing, forecasting, data hygiene.
Click **Clone Agent** on any listing, connect your CRM and data sources, and run on real records.
## Pick your path
How hands-on do you want to be? Pick a tab — the rest of the page is written for you.
You don't need to touch the builder. Open the cloned Agent above, ask in plain English, and let it work across the systems it's connected to.
## Sharpen the answers with knowledge
Upload your ICP definitions, segmentation rules, routing logic, and pipeline definitions. Every conversation references them — sharper output, no new setup.
Upload context once. Every conversation references it.
## Make it run on its own
When you're answering the same data-quality or routing question every week, set a trigger — every new account auto-enriches, every stale record auto-flags. One toggle, no code, no builder.
Schedule runs, fire on CRM events, listen to webhooks.
## Ready to go further?
When prompting and templates stop being enough — when you want the Agent to enforce your routing rules across the whole pipeline and orchestrate cross-system workflows — pick the **Build visually** tab above.
You're comfortable in product UIs. You don't want to wire up an Agent from scratch, but you do want it to enforce your routing logic record by record. Open the cloned Agent in the builder and shape every piece.
## Connect the tools it needs
RevOps Agents work best when they can read from and write to the systems your team already uses.
* **CRM** — [HubSpot](/integrations/popular-integrations/hubspot) or [Salesforce](/integrations/popular-integrations/salesforce) — the system of record
* **Data sources** — [Apollo](/integrations/popular-integrations/apollo), [ZoomInfo](/integrations/popular-integrations/zoominfo), web search for enrichment
* **BI / analytics** — [Google Sheets](/integrations/popular-integrations/google-sheets) or your warehouse for pipeline reporting
* **Comms** — [Slack](/integrations/popular-integrations/slack), [Gmail](/integrations/popular-integrations/gmail) for alerts and ops notifications
See more about [Integrations](/integrations/introduction).
## Shape every surface
Each piece below changes how the Agent behaves. Run it on a small record set between every change — small iterations beat big rewrites.
Edit the system prompt to match your ICP, segmentation, routing rules, and pipeline definitions.
Add or remove the actions the Agent can take — query CRM, enrich from third-party data, write back fields, post to Slack.
Upload ICP, territory definitions, routing logic, pipeline schemas. The Agent draws on these whenever it works.
Run the Agent automatically — on record create, on stage change, on a freshness schedule.
Get notified when routing rules conflict, when forecast accuracy drifts, when dedup confidence is low.
Persistent context per record — past routing decisions, prior enrichment sources, change history.
## Chain Agents into a workforce
When a single Agent isn't enough — enrich, then score, then route, then notify — wire them together on the Workforce canvas.
Drag, connect, and run multi-Agent pipelines without code.
## Test before you trust
Set up evals so routing or enrichment regressions get caught before they propagate across thousands of records.
Define test cases in the UI. Run them on every change.
## Ready to go further?
When clicking through the builder starts to feel slow — when you'd rather have your AI coding assistant build and edit Agents for you, drop into custom Python or JS code blocks, or call Agents from your own app via the SDK — pick the **Build with AI** tab above.
You already work in an AI coding environment. Clicking through a UI to edit routing rules feels slow. Have your AI assistant build, shape, and orchestrate Agents directly from your editor.
## Programmatic GTM
Programmatic GTM is an MCP server that connects your AI coding environment to Relevance AI. Describe what you want in natural language; your AI assistant reads and writes your Relevance workspace — system prompts, Tools, knowledge, triggers, workforces, evals.
The fastest setup. Install the Relevance AI plugin and build Agents from your terminal.
Connect from Claude Desktop, Cursor, VS Code, ChatGPT, OpenAI Codex, or any MCP client.
Clone the skills repo so your coding assistant has built-in knowledge of Relevance patterns.
The full reference for what you can do programmatically.
## The four levels in revenue operations
How the [autonomy ladder](/guides) plays out across your pipeline:
A RevOps analyst asks the Agent which accounts in the new MQL batch look like a fit for the enterprise team — the Agent returns a list with reasoning.
A RevOps analyst asks the Agent to dedupe a list of 1,200 imported leads — the Agent flags clusters with merge recommendations, the analyst approves the merges.
A new lead enters the CRM → enriched, scored, routed to the right rep, posted to Slack, all without anyone touching it.
You set the objective — "improve forecast accuracy" — and the Workforce enriches, scores, and routes, with its deal-signal criteria tuned through evals as close-vs-slip outcomes flow back.
## Zoom into a single use case
Each use case below is a working entry point — clone a template, then run it, shape it visually, or build with AI depending on which tab fit you.
Spot stalled deals, missing fields, and at-risk forecast slippage before pipeline review.
Apply your routing rules — plus the judgment edge cases — to every new lead automatically.
Find and merge duplicates, fix inconsistent fields, surface the records that need a human.
For account-level enrichment, see the [CRM data enrichment](/guides/sales/crm-data-enrichment) use case in Sales — same shape, applies cleanly to RevOps use. For scoring inbound, see [Lead scoring](/guides/sales/lead-scoring).
## See it in production