Selected work · B2B revenue engines

Revenue engines, built for scale.

The messy pipelines, untrusted forecasts, and leaking handoffs I've owned end to end, and what changed once the engine was rebuilt. Every engagement is one operator, all the way in.

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15+ yrs
In RevOps leadership
30+
Businesses transformed
$1.5B+
Revenue scaled
$2M→$100M
ARR architected
Real stories. Real results.

Every engagement scoped to move the revenue needle.

Uniphore
Enterprise AI · $110M revenue
Revenue intelligence

The forecast wasn't wrong. It was negotiated.

Eighty reps built the forecast bottoms-up in a spreadsheet, and a fifth of pipeline slipped every quarter. I replaced opinion with evidence (deal confidence from what buyers actually did) and rebuilt the planning and board-reporting layer underneath it.

68→80%+
Forecast accuracy
~20→<8%
Pipeline slippage
98%
On-time submission
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Keka
HR technology · $16.5M revenue
Revenue engine ownership

Four functions owned revenue. Which meant nobody did.

Sales, marketing, CS and finance each owned a slice of revenue, so nobody owned the number. I took ownership of the whole engine (the plan, the deal desk, the retention motion and the data) and the leaks closed.

103→112%
Net revenue retention
46→31%
Avg discount
21.6→18%
Gross churn
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Plum
InsurTech · $11M → $55M ARR
Founding RevOps

RevOps doesn't create growth. It decides whether you survive it.

I joined at ~$11M ARR as founding head of RevOps, with no plan, no systems, and 8% of new customers churning during onboarding. I built the infrastructure that let the company absorb 5x growth, starting with the sales-to-onboarding handover.

8→2%
Onboarding churn
120%
Of first plan
0→8
RevOps function
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Paris Gourmet
Specialty food · $35M revenue
AI prospecting data

The data didn't exist. So we manufactured it.

The data needed to prospect restaurants doesn’t exist for sale, so the channel sat blocked. An AI agent swarm manufactured 30,000 verified ICP records at ~$0.08 each, and a direct channel appeared where there had been none.

140
New direct accounts
24%
Outbound response
~$0.08
Cost per contact
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Work Wear Safety
Industrial distribution · $40M revenue
Data foundation

From one free-text field to a forecastable revenue engine.

A $40M distributor ran its entire commercial model as prose in one NetSuite notes field. I rebuilt it into a governed data model and gave the company its first forecastable view of revenue in twelve years.

150→120
Inventory days
4x→6x
Inventory turns
First view
Booked revenue
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Efficient Capital Labs
B2B fintech lending · Pre-Series-A
Signal-based allbound

They didn't need more leads. $50M were already in the building.

Borrowers were starting loan applications sales could never see, and 88% walked away unattended. I wired the application system to the CRM, scored and routed every application, and built a signal-based outbound engine on top.

~$50M
Pipeline surfaced
12→42%
Inbound conversion
$15M
Net-new disbursal
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A US B2B marketing agency
B2B marketing agency · $4M revenue
Retention & expansion

They hired me to build a retention practice. They needed one first.

A marketing agency was losing 28% of its own revenue to churn while planning to sell RevOps to clients. I unified every scattered health signal into one score with save-plays attached, and the agency’s own numbers became the pitch.

28→16%
Net revenue churn
62→78%
Billable utilisation
$300K
New service revenue
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UFP Industries
Building products · $6B revenue
Enterprise GTM

They had three implementation partners. What they needed was an architect.

Nine business units ran on seven disconnected systems, and no company-wide forecast existed. I ran the discovery, cut 15-20% of manual steps, then architected the consolidation and directed three vendors through it.

9→2 days
Quote cycle
86%
Forecast accuracy
12→9%
Avg discount
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