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 inside the firm's existing ChatGPT Enterprise environment that ingests proprietary deal data, past memos, and investment theses, making them queryable in plain language and enabling instant V1 investment memo generation.
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 entire solution was deployed within the firm's existing ChatGPT Enterprise subscription — no new tools, no IT approvals, no security reviews required.
Infrastructure
• ChatGPT Enterprise (existing subscription) • SharePoint (document repository)
Integration Points
• SharePoint → ChatGPT Enterprise (document ingestion) • Deal data repositories → RAG query layer