I build the
system a
company
runs on.

Building the revenue engine for the company standardizing AI governance

Credit ratings made debt investable. Patent indices made intellectual property investable. Enterprise AI has no such instrument — no accepted, quantitative way to say how well a company governs the systems it is betting on. AIQA Global was founded to build it, by the team behind the NYSE-listed Ocean Tomo 300® Patent Index. I own the commercial side of that: the revenue strategy, the sales organization, and the platforms it runs on.

Inside the CRM

• Role-based access for executives, market segment leaders, territory managers and stakeholders, on passwordless identity
• A prospect database of 1,907 contacts across 825 companies, on an idempotent weekly refresh
• A ten-stage pipeline with weighted forecasting and required fields enforced at the database layer
• Product-level deal construction carrying negotiated discounts through to weighted value
• Pre-call briefs routed to the executive team for feedback before the call happens
• Prorated weekly activity targets, scheduled recap emails, and a live coverage dashboard

Inside the Learning Center

• Level one: product knowledge at 80%, plus a foundations exam at 85%
• Level two: 85%, a written tough-questions exam, and two graded discovery role-plays
• Level three: 90%, a final exam, and a live pitch to two certifiers
• Role-plays scored against a fixed rubric — opening, question quality, listening, stakeholder mapping, the bridge to AIQA, and voice
• Grading runs on the server against shared rules, so a score cannot be argued with
• Executives see the certification funnel, score alerts, and the questions people miss most

A vehicle business intelligence ecosystem for real-time dealer decisions

Dealers run seven- and eight-figure inventory on gut feel, last month’s auction report, and whatever the store down the road is listing. By the time the numbers show up in a monthly report, the money is already spent. I built a vehicle business intelligence ecosystem to change the foundation those decisions sit on: a live read of the market, every vehicle tracked from the day it lists to the day it sells, so a buyer, a GM, or a lender decides what to buy, what to pay, and when to cut price on what is happening today.

SMRx: an AI acquisition engine that answers every lead in under 60 seconds

Dealers do not lose vehicle-acquisition leads on price. They lose them on speed. Industry first response runs near 47 hours, roughly 78% of sellers go with whichever dealer reaches them first, and over half of inbound leads are never called at all. I sized it as a capacity problem, not an effort problem — 3,500 call attempts required each month against 1,320 a staffed team can actually place — then built the product, the pricing, and the operating model that closes the gap without headcount.

“The first time I've had that lightning-in-a-bottle feeling since I started AutoTrader.com.”

Chip Perry · Chairman, sellmyride · founding CEO, AutoTrader.com

Miles: an AI agent that answers every seller lead in five seconds

Dealers buy cars from consumers by putting a value tool on their site and waiting. The lead arrives, the number is delivered, and then the follow-up depends on whether a human has the time — which overnight, on weekends, and during a busy Saturday, they do not. We built an agent that treats the number as the opening of a conversation instead of the end of one, and we built the guardrails before we built the charm.

See the demo 0:35

An AI-native operating system for multi-location fitness

Operators ran the business on a stack of disconnected point solutions — billing in one place, scheduling in another, member history somewhere else, and no reliable view of the enterprise. I founded the company to replace the stack with a single operating system, and led it from concept through launch and full enterprise diligence with a strategic acquirer.

Three products for a trade that still ran on spreadsheets

The coin and collectibles trade moves serious money on certified inventory, and the back office behind it was browser tabs, broken formulas, and a file from 2019. We built three things against that: a marketplace where collectors bid on graded coins, a wholesale marketplace where dealers trade with each other, and the AI-native inventory system that sits underneath both and is now sold on its own. I modeled the cost of the old stack at roughly $600K a year per house and built the business case from there.

Building the data, CRM and process backbone of a company that had none

There was no data warehouse. No CRM. No business intelligence. No enterprise reporting, no FP&A function, no performance metrics of any kind — and no documented intake process or inventory system — inside a house clerking five or more auctions a week, every week of the year. Consignments arrived, lots were cataloged from memory and spreadsheets, buyers lived in inboxes, and leadership decided on instinct and lagging information. I was brought in as CIO to build all of it from nothing.

I pioneered the company's first data warehouse and CRM, its first BI layer, its first KPI and performance-metrics framework, and the operating structure and documented processes underneath them — intake, cataloging, inventory, invoicing, and settlement. With the business finally legible, leadership could see which categories, consignors, and sales actually made money and act on it inside the week. That instrumentation is what carried the company past $60M+ in ARR.

21 to 150+ locations

Eight years running real multi-unit operations before ever building software for them. I joined at 21 clubs and left with the footprint past 150 across 15 states — over 500,000 members, 3,000+ employees, and $20M+ in monthly recurring revenue, grown both organically and through acquisition.

At that size the enemy is drift: 150 clubs quietly becoming 150 different businesses. My work was the operating standard that prevented it — how a club opens, sells, staffs, schedules, collects, and reports; what a manager is accountable for every week; and what the number has to look like before a new site gets approved. I ran operations planning and multi-unit budgeting against a recurring-revenue P&L, held margin and payroll ratio club by club rather than by regional average, and as managing partner carried business development for the growth of the footprint itself — including folding acquired clubs onto the same standard instead of leaving them as permanent exceptions.

This is where the operating instinct came from — and why every platform I have built since starts from how the work actually gets done in the field rather than how it looks on an architecture diagram.