Data & AI

Portfolio companies are data-poor.

Most PE-backed companies run their revenue on numbers nobody fully trusts. Attribution is broken, reporting depends on management's own submissions, and the sponsor, the board and the operators each work from a different version of performance. Every commercial decision inherits that weakness.

The commercial engine runs on data, or it does not run.

Pipeline, pricing, marketing spend, forecasting: every lever the value creation plan pulls depends on measurement the board can trust. Building that layer is not an IT project. It is commercial infrastructure, and it is the durable advantage in a portfolio.

AI deployed on clean data. In that order.

Boards want an AI position; portfolio companies answer with pilots. Revenue-side AI fails on dirty commercial data, and building that data layer is our whole firm. So we make companies AI-ready first, then deploy where the return is provable: forecasting, lead scoring, pricing, reporting. And we kill the pilots that will never pay back.

Start with a read.

The AI Readiness assessment scores data quality, infrastructure, use cases and org structure, one company at a time or across a whole portfolio. The Revenue Blueprint builds the foundation. Every engagement starts with a read, and our team stays to build and run what it finds.