── THE BUILDS

Fifteen years of GTM. Every one with a receipt.

An operator who builds. Four company turnarounds, from a $100M+ region at Uber to an AI-native GTM engine at Mixmax, and the systems and plays behind each number. Read one and you will see exactly how it was done.

── THE CAREER, IN FOUR NUMBERS
$100M+
15 markets, #1 region in the country
UBER EATS
~5x
Americas revenue, from a cold start
AIRWALLEX
145%
growth, marketplace to real SaaS
NOTCH
2x
win rate, on ~80% fewer selling heads
MIXMAX
── COMPANY TURNAROUNDS

What I moved, and the brands I moved it with.

Two ways to read this. The turnarounds above are what I moved. The systems and plays below are how — the AI-native machinery behind the numbers, each one a deep dive into the problem, the build, and the receipt.

── SYSTEMS & PLAYS

16 deep dives into how the numbers happened.

FLAGSHIP · AI-NATIVE SYSTEM
The Account Scoring Engine

Sourcing to a sent sequence: two scoring models, the enrichment waterfall, and the brief a rep pushes into Claude, then the sequencer. The whole machine, end to end.

A forecast you can defend·brief 20 min → 0Open the engine →
PRODUCT-LED MOTION
The Product Channel
Thousands of people used the product every week and nobody in sales ever knew.
~47% win rate · $1M+ sales-qualified pipeline
PRICING + PACKAGING
Reprice to De-risk
Conventional wisdom says raise price and you win fewer deals, slower.
Bigger deals + higher win rate + shorter cycles
RETENTION + NRR
Proactive Retention
The year I took over, churn and downgrades were larger than everything the new-business engine brought in.
Churn and downgrades down 28%
DEAL VELOCITY
The Deal Hub
The quote in one place, the security review in another, the mutual plan in a thread, the approval in someone's inbox.
Final stage 25%+ faster
SCORING EVIDENCE
The Win/Loss Backtest
Everyone trusted the fit score.
The #1 predictor of a win was not in the model
AI-NATIVE THESIS
The 7th Analyst
Anyone can build AI agents that do the work.
AI-native GTM isn't six analysts. It's the 7th.
DATA VENDOR STRATEGY
The Enrichment Bake-Off
We needed a verified email and cell for every contact.
A $0.33 bake-off, and the answer wasn't pick one
BUILD RELIABILITY
The Governance File
Most of the briefs shipped as broken stubs, a report index silently collapsed, and almost every link was dead.
It failed three ways in a week. Then it couldn't.
LENS: DEAL EFFORT
The Love Test
I ran an analysis I called the love test: were reps earning attention through multi-step prospecting, or just reacting to whoever already showed interest? The answer was uncomfortable.
My reps were reactive to love, averaging about 2 touches
LENS: COMMUNICATION
Nobody Feels 68.3%
Reports are full of stats nobody feels.
Nobody feels 68.3%
EXPANSION + NRR
The Expansion Score
Most expansion motion re-sells the champion you already have.
Active users at 175% of paid seats
TAM + PRIORITIZATION
Scoring the TAM
A rep opens the pipeline and sees a thousand accounts that all look the same.
The whole TAM, ranked: a tier and a next move per account
GTM OPERATING RHYTHM
Reporting That Drove Action
Most revenue reporting tells you what already happened, to an audience that cannot change it.
One weekly read that drove sales and CS, not a rear-view deck
ICP + PERSONAS
The ICP Playbook
Most teams call a firmographic filter an ICP: company size, a vertical, a tech tag.
A real ICP is a person and a problem, not a headcount band
AEO + GEO STRATEGY
Getting Found in the AI World
AEO and GEO are not SEO with new keywords.
$2M+ win-back and a prompt budget fully working the positioning
THE OPERATOR BEHIND THE RECEIPTS

If the receipts landed, let’s talk.

Fifteen years building GTM engines, now building them AI-native. If you are hiring for a GTM leader who ships, this is the whole track record, in the open.