How is this different from hiring a RevOps manager?

A RevOps manager operates within your existing systems. We redesign the systems. We bring AI-native architecture, predictive pipeline models, and cross-functional alignment frameworks that typically require a VP-level hire and a team of three. You get that capability fractionally, and we transfer it to your team so you can run it independently.

We already have HubSpot/Salesforce set up. Do we need this?

Having a CRM and having a revenue operating system are different things. Most companies use their CRM as a database, they enter data and pull reports. We turn it into an AI-native revenue engine with predictive deal scoring, automated pipeline health alerts, and forecast models that actually predict. The CRM stays. The intelligence layer changes everything.

How accurate is your revenue forecasting, and how do you measure it?

Our statistical forecast models target sub-5% variance against actual quarterly revenue. The model is trained on your historical pipeline data and improves each quarter as it accumulates more signal. We measure accuracy by tracking projected vs. actual revenue at quarter close, deal-level conversion probability calibration, and the standard error of the forecast over rolling four-quarter windows. Most companies forecast at 60-70% accuracy because they rely on rep self-reporting. We replace that with mathematical evidence from actual deal behavior.

How long does a typical engagement last?

Most engagements run 3-6 months. Month 1 is diagnostics and architecture. Months 2-4 are build and deploy. Months 4-6 are optimization and capability transfer. We're month-to-month, no long-term contracts. Some clients keep us on a lighter retainer for ongoing optimization, but the goal is always independence.

What if our data is messy?

It usually is. That's part of the job. We start with a data audit, clean the critical fields, build the attribution model, and implement data hygiene workflows so it stays clean. Messy data isn't a blocker, it's the first thing we fix.

Fractional Revenue Operations

Your CRM has data. Your pipeline has leaks.
Your forecast is a guess.

AI-native revenue operations that turn your go-to-market into a precision instrument. Not another dashboard, an operating system for compounding growth.

Built by operators who've run revenue at scale, 3 IPO exits, 95%+ forecast accuracy, and a track record of turning pipeline chaos into predictable, repeatable revenue machines.

Revenue data pipeline with flowing orange energy streams

Revenue operations isn't a tooling problem. It's an architecture problem.

You can have the best CRM, the sharpest reps, and a marketing team firing on all cylinders, and still miss your number by 30%. The issue isn't effort. It's the connective tissue between sales, marketing, and customer success. We build that tissue with AI-native systems that align your entire go-to-market around one number.

Most RevOps teams are paid to explain what already happened. AI-native RevOps shifts the function from reactive reporting to proactive orchestration. The same dashboards. A different job description.

Pipeline Architecture

Stage definitions, velocity benchmarks, and conversion models built on your actual data, not industry averages.

Predictive Forecasting

Statistical models that forecast revenue at 95%+ accuracy. Walk into board meetings with confidence, not hope.

AI-Powered Deal Scoring

Every deal scored on likelihood to close, days to decision, and risk factors, updated in real time from CRM signals.

GTM Alignment

Sales, marketing, and CS operating off the same data model, same definitions, same revenue targets.

The Architecture Dividend

What happens when the revenue operating system is built right. Not bolted on.

76%

Lower Cost per SQL*

$417/opp → $100/opp with AI-native pipeline generation

71% → 89%

Forecast Accuracy*

AI-driven signals replace gut-feel commit calls

18% → 31%

MQL-to-SQL Conversion*

AI enrichment + instant scoring at point of capture

25%+

Faster Close*

Cycle compression from automated follow-up and deal prep

*Aggregate results across operator engagements 2018-2024. Individual results vary.

What we build for you

Concrete deliverables, not strategy decks.

Revenue Data Model

Unified definitions for MQL, SQL, SAO, and opportunity stages
Attribution model connecting marketing spend to closed revenue
Data hygiene workflows that keep your CRM clean automatically
Single source of truth across sales, marketing, and finance

Pipeline Intelligence System

Real-time pipeline health dashboard with AI-generated insights
Deal velocity tracking with stage-level conversion benchmarks
Risk alerts for stalled, aging, or under-engaged opportunities
Rep-level performance analytics tied to pipeline contribution

Forecast Engine

Statistical forecast model trained on your historical data
Weighted pipeline, commit, and best-case scenario views
Quarterly accuracy tracking with model refinement
Board-ready reporting that finance actually trusts

GTM Operating Rhythm

Weekly pipeline review cadence and agenda templates
Monthly business review structure with leading indicators
Cross-functional SLA framework (marketing → sales → CS)
Escalation protocols for at-risk deals and accounts

The Agent Catalog

Six named agents. Each one replaces a job nobody enjoys doing.

When we say "AI agents handle entire workflows," these are the workflows. Concrete, named, scoped to where revenue actually leaks. Each one is built, instrumented, and handed off to your team using NIST AI actor harm mapping for RevOps and a RevOps AI risk register template so every agent has a named owner, a documented failure mode, and a tracked mitigation before it ships.

Inbound Lead Processing Agent

Triggers on form submission. Enriches the contact, scores against your ICP, routes to the right rep, drafts the first-touch message for review, logs the full chain in CRM. Replaces a five-minute manual ritual that was inconsistently done.

Outbound Research and Personalization Agent

Given an account list, researches each company, identifies the right contacts, drafts the sequence, loads it into the engagement tool. One rep now manages outbound volume that previously required two SDRs.

Meeting Prep Agent

Pulls CRM history, recent thread, LinkedIn, company news, deal status. Delivers a briefing thirty minutes before the call. The highest-adoption agent we deploy because the rep gets immediate personal value.

Post-Meeting Follow-Up Agent

Transcribes, summarizes, extracts action items, updates CRM stage and qualification fields, drafts the follow-up email. Eliminates the most-hated administrative task in sales.

Pipeline Review Agent

Runs weekly. Flags deals with no activity, deals where rep probability diverges from model probability, deals with missing qualification data. Produces the report the sales manager would have built by hand on Sunday night.

Renewal Risk Agent

Runs on a rolling 90-day window. Aggregates health score, support tickets, usage trends. Drafts the QBR prep doc. Tied directly to retention dollars that would otherwise silently churn.

The engagement timeline

PHASE 01Week 1

Diagnose

Full pipeline teardown
Data quality audit
Process mapping across GTM
Quick-win priority list

PHASE 02Weeks 2-8

Build

Revenue data model deployed
Pipeline scoring live in CRM
Forecast engine calibrated
Operating cadence established

PHASE 03Weeks 8-12+

Transfer

Team trained on all systems
Playbooks and documentation
30-day support runway
Independence achieved

FAQ

Common Questions

Ready to fix your revenue operations?

Your CRM has data. Your pipeline has leaks.Your forecast is a guess.