$3.6B PE Firm Creates $12.5M+ of Value on a Single Deal

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Three sequential AI products replaced manual sourcing, scattered knowledge, and slow document review.

$12.5M+ on one deal

4–6 months

Implementation Time

Brian Benedict

Co-Founder & CCO

Eliza

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The Challenge

Sourcing was manual and time-intensive, with analysts assembling company profiles individually and consuming thousands of hours annually. Institutional knowledge was trapped across DealCloud, SharePoint, and personal folders, and diligence required manual review of thousands of VDR documents.

What They Built

Eliza built three AI products sequentially: a proprietary sourcing engine evaluating 2,000+ companies per search with scored shortlists, an institutional knowledge search spanning 20 years of deal history, and VDR intelligence giving natural-language access to data room documents with page-level citations.

Eliza built the three products sequentially and workflow-native, so there was nothing new to learn. The sourcing engine scores 2,000+ companies per search against configurable, thesis-specific criteria; the knowledge search unifies 20 years of history across DealCloud, SharePoint, and CIM archives, ranked by relevance and linked to sources; and VDR intelligence adds page-level citations and automated financial analysis. The first two products were built over 18 weeks and VDR intelligence in 10.

AI Role

AI sources and scores targets against the firm's thesis, searches 20 years of institutional knowledge, and reads data-room documents with page-level citations, while the deal team keeps decision authority.

Infrastructure

DealCloud deal CRM, SharePoint document storage, virtual data rooms, and 20 years of historical deal folders and CIM archives

Integration Points

Built into existing DealCloud, SharePoint, and diligence workflows with no new systems to learn; results ranked and linked to source documents with page-level citations; continuous data refresh after deployment

Impact

$12.5M+

Value created on a single deal

6,000 hrs/yr

Annual search time eliminated (137-275x ROI cited)

1-2 wks/deal

Diligence compressed per deal ($625K-$875K/yr; VDR adds $5-10M+ carry/deal)

Technology Utilized

Implementation Complexity

Revenue Growth

Time Savings

Best Fit For

Middle-market private equity firms seeking to compress sourcing and diligence and unlock institutional knowledge with governed, citation-backed AI tools.

Brian Benedict

Co-Founder & CCO

Eliza

Co-founder of Eliza, a boutique AI transformation firm and OpenAI Advanced Tier Partner. A two-time founder and former Hugging Face, he helps enterprises put AI to work through agentic engineering.

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Industry:

Financial Services

Business Function:

Executive & Strategy

Operations

Project Cost:

Cost not disclosed

Organization Type:

Private Equity Firm

AI Pattern:

Knowledge Management & Search (RAG)

Document Processing & Extraction

Decision Support & Scoring

Value Type:

Revenue Growth

Time Savings

Frequently Asked Questions

How did a private equity firm create $12.5M of value on a single deal with AI?

The experts built three AI products in sequence: a sourcing engine, an institutional knowledge search, and a virtual data room intelligence tool. On a single deal the work generated more than $12.5M in value.

What AI tools and approach were used?

The solution combines decision support and knowledge search: a proprietary sourcing engine evaluating 2,000+ companies per search with scored shortlists, an institutional knowledge search across 20 years of deal history, and VDR intelligence with page level citations.

What results did the private equity firm achieve?

More than $12.5M in value generated on a single deal and 6,000 hours of annual search time eliminated. These figures are firm reported estimates.

How long did it take to see results?

The products were deployed sequentially, with the first two built over 18 weeks and VDR intelligence delivered in 10 weeks.

Who is this deal intelligence approach best for?

Private equity firms with manual sourcing, institutional knowledge scattered across systems, and heavy diligence document review.

Originally published by

Eliza