What this work looks like when it's done.

Selected results from leadership roles before Velocity Partners. Client results will be added as engagements conclude.

Situation: Customer data for a national automotive retail network was split across dealers, regions and the manufacturer. No one could act on a full view of the customer.
What we did: Built a first-of-its-kind cross-tier customer data platform and ran it as a product, with a roadmap, an adoption plan and revenue accountability.
Result: 2,500 dealers on one platform containing 80 million known customer records, which became the foundation for the company's AI products. 95% of dealers use at least one solution with CDP data integration.

Situation: Dealers had more data than they could use, and getting answers required analysts and reports.
What we did: Launched an AI-powered natural-language platform on top of the CDP, so dealer staff could ask questions in plain English. It was designed to minimize hallucination when using large language models to extract structured data.
Result: Patent pending.

Situation: Product work was driven by requests rather than business cases, with a small team stretched thin.
What we did: Installed business-case discipline and decision gates first, then built the team around them.
Result: A 50-person product management organization, and more than $100M in revenue growth over seven years.

Situation: Polk (IHS Markit) needed models that ran reliably in production, not one-off analyses.
What we did: Led data science and product teams that built the processes to deploy and maintain models at scale.
Result: 800+ machine learning models operationalized, in two families. Purchase-prediction models identified the consumers most likely to buy a targeted make, model or budget within 90 days, finding buyers 30 times more likely to purchase than average. Vehicle-ownership models predicted which vehicles a consumer owned.

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