How a PE Team Surfaced Deal Precedents in Seconds

An investment team piped past memos and investment theses into a RAG layer on ChatGPT Enterprise — surfacing relevant precedents before first calls instead of after them.

Richer Deal Conversations

Delivered via AI deal research

4–8 weeks

Implementation Time

Natalia Quintero

Consulting Partner

Every

Connect ↓

The Challenge

A private equity investment team was managing every deal the hard way. When analysts needed context on past investments or comparable transactions, they searched SharePoint manually — a slow, unreliable process that depended on knowing where to look. Thesis formation took too long. First-draft investment memos required too many hands. The institutional knowledge existed; it just wasn’t accessible in the moment it was needed most.

What They Built

Every built a set of AI workflows operating entirely inside the firm’s existing ChatGPT Enterprise environment — no new infrastructure, no security concerns. The system ingests proprietary deal data, past memos, and investment thesis documentation, then makes it queryable in plain language. When a new deal arrives, analysts can pull relevant precedents instantly and generate a V1 investment memo already aligned to how the firm thinks. What nobody expected: the quality of deal conversations improved — teams arrived to first calls better prepared, asking sharper questions than before.

Every began by mapping the investment team’s research and memo-drafting workflows to identify where institutional knowledge was hardest to access under time pressure. Rather than building new infrastructure, the team operated entirely within the firm’s existing ChatGPT Enterprise subscription — eliminating security and IT approval hurdles from the start. The core work was ingesting and structuring historical deal data: past investment memos, thesis documentation, and comparable transaction records were organized so they could be queried in natural language rather than searched manually through SharePoint folders. Prompt engineering created workflows that analysts could run at deal intake — pulling relevant precedents, surfacing comparable transactions, and generating a V1 investment memo already structured to match how the firm thinks. The entire build took place over two to four months, leveraging only existing subscriptions and document repositories. No engineers or custom development were required.

AI Role

The AI system ingests proprietary deal data, historical investment memos, and thesis documentation, making it queryable in plain language through the firm’s existing ChatGPT Enterprise environment. When a new deal arrives, it retrieves relevant precedents and generates a first-draft investment memo already aligned to the firm’s investment framework and analytical style.

AI Model

ChatGPT

Infrastructure

ChatGPT Enterprise (existing subscription) • SharePoint (document repository)

Integration Points

SharePoint → ChatGPT Enterprise (document ingestion) • Deal data repositories → RAG query layer

Impact

Richer Deal Conversations

Investment teams arrived to first calls better prepared, with relevant precedents surfaced before the meeting rather than found afterward.

Built Inside ChatGPT Enterprise

The entire solution was deployed within the firm’s existing ChatGPT Enterprise subscription — no new tools, no IT approvals, no security reviews required.

Repeat Engagement

The client engagement extended beyond the initial build, reflecting the value delivered and the depth of the AI-native workflow integration.

Technology Utilized

Implementation Complexity

Time Savings

Revenue Growth

The solution is deployed entirely within an existing ChatGPT Enterprise subscription, requiring no new infrastructure, IT approvals, or security reviews. The primary effort is in workflow design and connecting the firm’s document repositories — not engineering or custom development.

Best Fit For

Private equity and venture capital firms that need to unlock institutional knowledge from document repositories without adding infrastructure or security risk.

Natalia Quintero

Consulting Partner

Every

Enterprise AI Partner at Every Inc., bringing extensive experience in technology innovation, venture strategy, and international business development.

Connect with the Expert

Industry:

Financial Services

Business Function:

Finance & Accounting

Executive & Strategy

Company Size:

51-250

Project Cost:

Cost not disclosed

Ownership:

Founder-Owned

Organization Type:

Private Equity Firm

AI Pattern:

Knowledge Management & Search (RAG)

Generative Design & Content

Value Type:

Time Savings

Revenue Growth

AI Model:

Frequently Asked Questions

How did a mid-market financial services firm surface deal precedents in seconds with RAG?

The experts ingested past investment memos, thesis documentation, and comparable-transaction records and structured them so analysts could query them in plain language instead of searching SharePoint by hand, with prompt workflows that pull precedents and draft a V1 memo at deal intake. The result was relevant precedents surfaced before first calls rather than after them.

What AI tools and models were used in this RAG project?

The system was built entirely inside the firm's existing ChatGPT Enterprise environment, with historical deal data from SharePoint ingested and made queryable in natural language. The approach was knowledge management and search (RAG) plus generative content, using only existing subscriptions — no new infrastructure or custom development.

What results did the investment team achieve?

The solution was deployed within the firm's existing ChatGPT Enterprise subscription with no new tools or IT approvals, the engagement extended beyond the initial build into a repeat engagement, and deal conversations improved as teams arrived at first calls better prepared with precedents surfaced in advance.

How long did the RAG deal-intelligence project take?

About two to four months, working entirely within existing ChatGPT Enterprise subscriptions and document repositories, with no engineers or custom development required.

Who is this RAG approach best for?

Private equity and venture capital firms that need to unlock institutional knowledge from document repositories without adding infrastructure or security risk.