A fast-growing GP with private equity and credit arms ran its deal pipeline through a CRM, Excel trackers, and email attachments.

Every deal pulled from a sprawl of disconnected sources (CIMs, internal models, prior memos, CRM, portfolio monitoring, external research), but none of it was connected or queryable together. Cursory use of Claude on individual documents helped summarize at the margins but couldn't do the work that mattered: first-pass analysis across all the inputs at once. That bottleneck hit hardest at screening, where both arms needed efficient first-pass reads to decide what to advance. Credit was screening high opportunity volume and couldn't keep up. Equity was screening fewer but wanted to get a deeper view quicker on the deals worth pursuing. Deal teams were rebuilding the picture from scratch on every opportunity, by hand, before any judgment could happen.

Soal Labs built a deal screening and memo-generation engine for the GP's private equity and credit arms. Using Azure OpenAI and Anthropic models, it ingests every deal input, extracts and validates the deal's shape against firm business rules, stores it in a structured deal database, and drafts each memo from pre-screening through investment committee.

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