RevenueOS AI-NATIVE REVENUE OPERATIONS

Turn GTM complexity
into measurable revenue.

We build RevenueOS — the operating system behind predictable B2B revenue. Forecasting, pipeline governance, CRM architecture and automation, connected end to end.

Find where your revenue leaks See RevenueOS First diagnostic in 14 days
Revenue systems built with teams at
RevenueOSTHE OPERATING SYSTEM

Market to revenue. One unbroken chain.

Seventeen steps sit between a market you've defined and a number your board believes. Most companies instrument four of them. RevenueOS connects all seventeen, so every metric downstream inherits the truth from the one above it.

01 MARKET
  • TAM / SAM
  • ICP & personas
  • Database intelligence
02 DEMAND
  • Campaign strategy
  • Lead generation
  • Lead scoring & routing
03 QUALIFY
  • BDR qualification
  • AI L1 call scoring
  • SQO
04 PIPELINE
  • Stage intelligence
  • MEDDPICC / deal intelligence
  • Trial / POC
05 CLOSE
  • Deal desk / commercials
  • Forecast intelligence
  • Closed won
06 REVENUE
  • ARR / revenue
  • Executive & board intelligence
17 CONNECTED STEPS ● AI-INSTRUMENTED STEP

What a connected revenue engine is worth.

Two kinds of numbers. The first four came out of real engagements. Everything after that is a direction we commit to moving, measured from your own baseline — because a team at 40% forecast accuracy and a team at 85% are not solving the same problem.

Qualified pipeline growth
Full-market coverage, sharper targeting and demand programmes that compound instead of restarting each quarter.
+30%
DCC to SQO conversion
AI-assisted call scoring, L1 qualification intelligence and coaching aimed at the gaps the scoring exposes.
+18%
Win rate
Qualification discipline, stage governance, deal intelligence, structured trials and consistent seller execution.
−30%
Data acquisition cost
Vendor mix, dedupe discipline and automated enrichment, benchmarked against documented before-and-after spend.
What we commit to moving in every engagement
Full TAM coverage Data completeness Contactability Duplicates Forecast predictability Forecast variance Manual ops hours CRM adoption GTM stack cost Reporting effort Validated data migration ROI measured from baseline
A typical maturity path — measured from your baseline, not a fixed promise
METRIC
BASELINE90 DAYSAT MATURITY
DCC to SQO conversion
45%58%75%
Forecast variance
28%15%<5%
Data health
67%95%100%
Manual ops per week
44h25h1h

Your CRM has data.
Your GTM needs answers.

Nobody's system is broken in the abstract. It breaks in four specific, expensive places — and the same four show up in almost every engagement.

SYMPTOM 01Three teams, three pipeline numbersMarketing reports one figure, sales another, finance a third. The first twenty minutes of every leadership meeting goes to reconciling them instead of acting on them.
SYMPTOM 02The forecast is a Thursday night spreadsheetBuilt by hand, defended by gut feel, wrong often enough that the board has quietly started discounting it. Nobody can explain what moved between two weeks.
SYMPTOM 03Deals stall and nobody notices until QBRNo stage governance, no aging signal, no next-best-action. Slipped quarters get discovered in the review rather than prevented in the week.
SYMPTOM 04Your analysts are assembling, not analysingTwenty to forty hours a month exporting, pasting and reformatting. The insight arrives after the decision it was meant to inform.

Six engines. Each ships as working infrastructure.

Engagements start where the gap is widest and compound from there. Nothing here is a deliverable in a deck — every engine runs against live data and your team owns it at the end. The metrics under each are what we instrument and optimise, set against your baseline.

01

Market & Data Intelligence

TAM and SAM sizing, ICP definition, personas, segmentation, territories and named accounts — on a database that's architected, enriched, deduped and governed.

Right market, trusted data
ICP coverage Whitespace identified Completeness Duplicates Contactability
02

Demand & Qualification Intelligence

Campaign strategy, ABM, attribution, intent, lead scoring and routing — then AI L1 call scoring, MEDDPICC discipline and champion identification at the handoff.

More pipeline, better SQOs
Qualified pipeline Cost per SQO DCC to SQO L1 score MEDDPICC coverage
03

Pipeline & Forecast Intelligence

Stage conversion, aging, velocity, deal scoring and stalled-deal detection, feeding commit, best case and upside with rep forecast measured against AI forecast.

Predictable revenue
Stage conversion Velocity Win rate Forecast accuracy Slippage
04

Seller Performance & Revenue Planning

Rep scorecards, call coaching and enablement gaps on one side; AOP, capacity, territories, quota architecture and compensation design on the other.

Efficient growth
Pipeline per AE Attainment Ramp time Capacity coverage
05

GTM Systems & AI Automation

CRM architecture, integrations, the GTM hub, workflows and AI agents that take over report assembly, hygiene checks, deal signals and alerting.

One connected GTM engine
CRM adoption Manual ops hours Systems of record Stack cost
06

Revenue Command Center

CEO and CRO dashboards covering ARR, pipeline, forecast, funnel and productivity — plus the board, WBR and QBR intelligence that runs your operating cadence.

One source of revenue truth
ARR Reporting time Decision speed Attainment

Migrate the stack. Onboard the team. Cut the spend.

Most GTM stacks grow by accident — a tool per problem, four systems holding the same record, and a licence bill nobody has audited in two years. We consolidate it into one connected hub, move the data without losing history, and get the team actually using it.

BEFORE · FRAGMENTED STACK
SalesforcePardotHubSpot MarketoZoomInfoLegacy BI Spreadsheet forecastSales NavigatorSecond CRM instance
Annual licence spend$500K+
Systems holding the same record4
CRM adoption46%
  1. 01Audit & mapEvery object, field, workflow and licence
  2. 02MigrateHistory preserved, validated, parallel run
  3. 03OnboardRole-based training, playbooks, adoption tracking
  4. 04OptimiseLicence right-sizing and vendor renegotiation
AFTER · ONE GTM HUB
GTM HUBCRM · data · workflows · AI agents
MarketingSalesDeal deskFinanceCSBoard reporting
Effective stack cost−32%
Systems of record1
CRM adoption94%
6–10 WEEKS VALIDATED DATA MIGRATION PARALLEL RUN BEFORE CUTOVER ROI MEASURED FROM BASELINE

How the engine gets built.

A sequence, not a retainer. Every phase ends with something in production.

01

Diagnose

Two weeks inside your CRM, funnel and forecast. You get a ranked list of what's leaking and what it costs.

02

Design

The target operating model: metric definitions, stages, data model, cadence and ownership.

03

Build

CRM, dashboards, forecast model and deal desk shipped and running against live data.

04

Automate

AI agents and workflows take over the assembly work — reporting, hygiene checks, deal signals.

05

Scale

Operating cadence, quota and capacity models, and an internal team trained to run it without us.

Built by operators who carried the number.

OpsMetric is a revenue operations company. The work behind it comes from a decade inside high-growth B2B SaaS and AI businesses — running forecasting, pipeline governance, CRM transformation and board reporting as the team accountable for the number, not the agency advising on it.

That's the difference you'll feel in the first week. We've built the RevOps function from zero, sat in the forecast call when the number was short, and produced the analytics that went into investor diligence. We know which shortcuts hold and which ones fail at scale.

Every engagement ends with your team owning the system, not renting it from us.

Less reporting. More operating.

ForecastingPipeline governanceAOPDeal deskCRM transformationBoard reporting

Engagement profile

ModelBuild, automate, hand over
Company stage$0–100M ARR
We report toCEO / CRO / CFO
First diagnostic14 days
Full engine build6–10 weeks
Core stackHubSpot · Salesforce · Marketo
AnalyticsPower BI · SQL · Excel
AI layerClaude · Notion · Fireflies
CoverageUS · EMEA · APAC
14 DAYSFIXED SCOPENO SYSTEM CHANGES

The 14-day RevenueOS diagnostic.

Every engagement starts here. Two weeks inside your data, ending in a ranked list of where revenue is leaking, what each leak costs, and what to fix first. You keep the output whether or not we build anything together.

How the two weeks run

DAYS 1–3Access and baselineRead-only access to CRM, marketing automation and reporting. We measure where you actually are today.
DAYS 4–8Funnel and forecast teardownStage conversion, aging, velocity, data health, forecast variance and reporting effort, quantified against your own numbers.
DAYS 9–12Leak analysisEach gap sized in pipeline or revenue terms, then ranked by cost and effort to fix.
DAYS 13–14ReadoutA 90-minute session with your leadership team, and the written plan in your hands.

What you walk away with

  • A baseline scorecard across market, data, demand, pipeline, forecast and systems
  • Your revenue leaks, ranked and sized in pipeline terms
  • Forecast variance and data health measured, not estimated
  • Manual reporting hours counted and mapped to automation candidates
  • A 90-day build sequence with owners and effort estimates
  • A GTM stack assessment with consolidation and licence savings

Find where your revenue engine is leaking.

Fourteen days, inside your data, ending in a ranked list of what's costing you pipeline — and what to fix first.