Fractional Revenue Operations
Your CRM has data. Your pipeline has leaks.
Your forecast is a guess.
RevOps leadership that turns go-to-market into a precision instrument: leading signals, not lagging dashboards. Built for B2B SaaS between $10M and $75M ARR.
Run by an operator who took a $180M forecast from 68% to 97% accuracy, lifted qualified pipeline 320%, and was employee #6, #11, and #19 at three companies that went public.
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. If terms like pipeline architecture or cost per qualified opportunity are new to your team, our RevOps and GTM metrics glossary defines every number the way a board reads it.
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 97% 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 New Reality
RevOps got harder while the headcount got smaller.
What changed
- Your revenue data lives in five systems that do not talk, and the AI you bought can reason over none of them.
- The forecast is still assembled by hand in a spreadsheet the night before the call.
- Attribution is relitigated every quarter because marketing, sales, and finance each keep their own math.
- Ops headcount is frozen while the target went up. The team is drowning in swivel-chair work.
What you need now
- One source of truth wired so the AI can actually reason over it, not another dashboard on top of dirty data.
- A forecast built from deal signals, not rep sentiment, that survives contact with the quarter.
- An attribution model finance signs off on once, so the quarterly argument ends.
- Automation that removes the manual stitching, so a lean ops team runs a larger machine.
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
68% → 97%
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
- Every metric computed in SQL: one written query per number, so it is auditable and returns the same answer every run
- 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. If you need the leadership seat, not just the system, see fractional CRO services. For the bigger picture on why agents, not chatbots, are the unit of work, read the agentic AI revolution. New to the vocabulary? Start with the definition of an agentic workflow in our AI agents glossary.
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
Diagnose
- Full pipeline teardown
- Data quality audit
- Process mapping across GTM
- Quick-win priority list
Build
- Revenue data model deployed
- Pipeline scoring live in CRM
- Forecast engine calibrated
- Operating cadence established
Transfer
- Team trained on all systems
- Playbooks and documentation
- 30-day support runway
- Independence achieved
Related resources
Keep exploring the frameworks and engagements connected to this topic.
AI RevOps Architecture 2026
How agents change the operating model.
Strategic RevOps Transformation
Rebuilding the full revenue motion.
Metrics That Decide Whether Growth Compounds
The numbers that actually matter.
ForecastIQ
Predictive forecasting from $75 per seat per month.
Signal Stack
Buyer intent and competitive signals.
Pipeline Modernization
SDR, AE, and CSM systems rebuilt.