Unifying 20 Years of Data for a PE Firm's AI Strategy

The Problem

A mid-market private equity firm managing billions in assets had two decades of proprietary deal, portfolio, and market data fragmented across legacy systems and inconsistent formats. Investment teams were spending excessive time manually gathering and reconciling data, slowing down the decision-making process at critical moments in fundraising and deal evaluation.

Our Approach

• Designed and led the firm-wide AI strategy across the general partner level and portfolio companies, aligning priorities across multiple stakeholders
• Unified the data storage layer in Snowflake, restructuring 20 years of proprietary data across multiple formats and enriching it with targeted third-party sources
• Built development environments for internal AI applications spanning deal sourcing, portfolio monitoring, and fundraising workflows
• Led a team of four engineers and three interns to deploy tools across critical business functions

The Outcome

Achieved 50% faster data retrieval and 60% cost reduction across proprietary workflows. Internal AI applications contributed to above-market performance for three different funds and became a highlight in the roadshow for raising $8 billion for a new fund. Established a foundational AI program with strong governance frameworks that continues to scale.