# RevOps for Growth Teams | Working Mono > Machine-readable mirror of https://www.workingmono.com/revops-for-growth > Working Mono is the AI-native firm behind PATCH. We do the work. You own the machine. Working Mono · an AI-native firm # RevOps that turns growth signal into pipeline. Your product knows who is ready to buy; the handoff is where that dies. PQL definitions live in a notebook, scoring in a spreadsheet, routing in a Zapier chain nobody trusts. We build the handoff as code on one joined model in your warehouse, and every experiment feeds the memory the routing reads. Book a 20-minute call -> What is RevOps for growth? We do the work. You own the machine. First route live inside month one. ## Proof 30 + Commercial systems delivered Month 1 Foundation live, guaranteed 100 % Owned by you 270 + Workflow patches shipped to client systems Trusted by AI-native teams The definition ## What does RevOps look like for a growth team? One model both teams trust. For a growth team, RevOps is the handoff made mechanical: product events, CRM, and billing joined in one model, PQL definitions written as tested code instead of tribal knowledge, routing that fires the moment a threshold crosses, and every experiment logged to the same memory, so what growth learns changes what sales does this week, not next quarter. How it compounds ## The signal-to-pipeline loop. Instrumented end to end. Definitions, scoring, routing, and experiments run against one joined model in your warehouse. Every weekly patch is judged by what reached sales and what it closed, and the loop tightens on that evidence. SIGNAL PATH · CHARTED 01 / MAP Step 01 ### Map the signal path We interview growth, sales, and ops while reading Segment, the CRM, and billing directly, then chart where signal actually dies: the PQL definition three people remember differently, the score nobody recomputes, the routing rule that quietly stopped firing in March. Output: the signal path, charted end to end Step 02 ### Join product to pipeline Events, accounts, opportunities, and revenue join into one model in your warehouse, with identity resolution handled once instead of per-tool. A PQL stops being a spreadsheet tab and becomes a queryable row with its full history attached. Output: one joined model under growth and sales Step 03 ### Define PQLs as code Qualification logic becomes versioned, tested code: thresholds, exclusions, decay, and the reasoning in the comments. When the definition changes it changes in one place, recomputes the queue, and shows the diff: who entered, who dropped, and why. Output: PQL logic you can test and diff Step 04 ### Ship the handoff Scored accounts route to owners in Attio or HubSpot with context attached: the triggering signal, the usage trend, the billing state, the suggested play. Alerts land in Slack as thresholds cross, so sales acts the same day growth generates the signal. Output: the handoff, live as routing code Step 05 ### Measure what converts The weekly trace follows the funnel you actually run: signals fired, routed, worked, converted. Thresholds earn their values from closed-won evidence, and the misses stay visible instead of quietly composting at the bottom of the CRM. Output: signal-to-close, traced weekly Step 06 ### Feed experiments back Every experiment writes its outcome into the same memory the routing reads. A pricing test reshapes scoring; an activation change moves the PQL threshold. The loop tightens every cycle, in infrastructure your team owns and can extend without us. Output: a routing model your experiments train Growth built the signal. Ship the handoff. Scoped in one 20-minute call. Book a 20-minute call Ask, then act ## One revenue memory. Every surface. GTM inputs attio stripe metronome clearbit apollo snowflake segment postgres slack hubspot mixpanel intercom zendesk linear bigquery salesforce posthog amplitude attio stripe metronome clearbit apollo snowflake segment postgres slack hubspot mixpanel intercom zendesk linear bigquery salesforce posthog amplitude + and more 01 / 05 > Month one ## Month one closes the loop. Signal routing by week two. The build starts at your loudest leak: warehouse live, model joined, the first PQL route firing on production signal. All of it committed to your repo, and guaranteed inside month one. **Week 1** ### Chart the signal path Interviews and system reads locate where signal dies today. The warehouse and joined model stand up beneath it. **Week 2** ### First route fires The worst leak becomes the first workflow: PQLs scored in the warehouse, routed with context, on live events. **Week 3** ### One funnel view Slack alerts and the query surface open to both teams. Growth and sales read the same number from the same model. **Week 4** ### Loop owned Definitions, routing, and docs commit to your repo. Foundation live or your money back, next experiment queued. D01 WEEK 1 / 4 · CHART THE SIGNAL PATH Interviews and system reads locate where signal dies today. The warehouse and joined model stand up beneath it. Week 1 Chart the signal path d07 · leaks located Week 2 First route fires d14 · pqls routing Week 3 One funnel view d17 · both teams reading Week 4 Loop owned d28 · in your repo You own the machine. Growth teams have been burned by tools that hold the model hostage, so the ownership is structural: the joined model in your warehouse, the PQL definitions and routing workflows in your repo, the runtime on your infrastructure, and enrichment billed at raw API cost with no per-credit markup. Cancel any month and the system keeps running. Your experiments keep training a model you keep. In production ## Real systems, running today. This loop already runs in production: 30+ owned commercial systems, 270+ workflow patches shipped to client repos, operated week over week. operating ledger systems 34 runs · 7d 0 utc --:--:-- id system motion schedule last run runs·7d status ▮ standing by… The next step ## Ship the handoff your funnel deserves. In 20 minutes you'll know where your signal path leaks, what one joined model looks like on your stack, and which PQL route ships first as code you own. Booking · intro call ◆ 20 min · direct with the team reading the calendar… prefer email? contact@workingmono.com We do the work. You own the machine . #### Product How PATCH compounds RevOps automation The GTM data join Working Mono home #### Company How month one works In production Proof Contact #### Get in touch contact@workingmono.com Book a call (c) 2026 Working Mono. Official Attio Founding Expert Partner.