PE-backed FIRE
Insurance, lending, mortgage, real estate, and financial services teams with messy data, EBITDA pressure, and real operating leverage.
Predictive AI for revenue systems
Moneyball for marketplaces, lenders, insurers, and revenue teams.
Your revenue system makes decisions every day. Many are still driven by rules, habits, defaults, and underused data. I find where those decisions are leaking money, then turn the best opportunity into a model, system, or performance-backed intervention.
$20M+
attributable revenue
18%
conversion lift
5x
CTR improvement
40%
cost reduction
5 yrs
independent sponsor
The wedge
The leak is usually not obvious. It lives in defaults.
Round robin routing. Old pricing tables. Rep judgment. Stale bid rules. Spreadsheets no one trusts but everyone uses. These choices quietly shape revenue every day.
MIT math plus five years sourcing middle-market PE deals in insurance, real estate, software, and industrials. I know where the data gets messy and where the money usually hides.
Lead routing sends the best prospects to the wrong rep.
Pricing treats high-risk and high-value customers the same.
Bid systems chase volume instead of profitable outcomes.
Marketplaces match on availability, not likelihood to transact.
Best fit
Built for data-rich businesses where the buyer cares about revenue.
Marketplaces
Businesses where matching, ranking, supply quality, lead scoring, or conversion drives the whole P&L.
Performance media
CTV, paid search, and ad platforms where better bidding, pacing, and allocation can turn directly into margin.
Flagship offer
Revenue Model Intervention
A high-stakes diagnostic and model build for one measurable revenue lever. You walk away with a working model, a business case, and a path to a performance-backed deal.
Typical structure
$50K+
Paid engagement plus performance upside when the math justifies it.
01
Find the lever
Pick one money metric and identify where better prediction can move it.
02
Model the decision
Build the scoring, ranking, matching, pricing, or bidding logic.
03
Explain the upside
Translate the model into lift, risk, operating changes, and board-ready math.
04
Ship the path
Hand over the model, implementation spec, tests, and next operating rhythm.
Aligned incentives
Fixed fee to prove the lever. Upside only if it moves money.
The first engagement proves whether there is a real revenue model hiding in the data. If the math holds, I help turn it into a production decision system and structure part of my economics around measured lift.
Best fit
Baseline first
No fuzzy attribution. We define the metric, the counterfactual, and the lift window before anyone talks upside.
01
Prove the lever
Paid diagnostic and model build. You get the model, assumptions, business case, and operating logic.
02
Productize the decision
If it works, we turn the model into a durable system: your stack, my productized layer, or both.
03
Share the upside
Success fee, revenue share, savings share, warrant, or equity. Only against measured improvement over baseline.
What this is
Not AI theater. Not a dashboard. A better way to make money decisions.
What you get
- A real model tied to one revenue lever.
- A plain-English business case the CEO can repeat.
- A deployment spec for product, data, or engineering.
- My judgment on whether the opportunity is worth chasing.
What I avoid
- Generic "AI transformation" decks.
- Open-ended custom app work with mystery scope.
- Models that cannot be tied to cash movement.
- Consulting teams where you get juniors after the sale.
Proof
I have already done this in the exact places where the math matters.
18% lift in a core conversion metric
Architected an ML and LLM system for real-time content optimization, producing $900K+ in incremental pipeline.
$20M+ attributable revenue
Built an ML-powered SEM optimization platform using gradient descent to improve spend allocation at scale.
5x CTR and 40% cost reduction
Designed dynamic ad generation and landing-page optimization using bandit-style testing and predictive scoring.
Why Zavient
MIT math.
PE scar tissue.
Builder hands.
I have worked across private equity, B2B SaaS, marketplaces, martech, and AI. I spent five years as an independent sponsor sourcing and structuring middle-market deals across software, SaaS, insurance, real estate, and industrials.
I can write the model, explain it to an investment committee, and tell you when the data is too shitty to trust.
MIT Mathematics
Texas A&M MBA
CIVC / Stonehenge / BlackRock
$7M-$30M+ EBITDA targets
AI / ML / SQL / Python
PE-backed operator
Start here
Send me the metric you want to move.
Company URL. Revenue motion. The metric that matters. I'll tell you where I would look first and whether an engagement is worth it.