AI for private equity, built for the firm itself — not just the portfolio.

Not portco automation — the sponsor’s own four walls. AI reads the firm’s inboxes, call transcripts, SharePoint deal folders, and CRM, then answers the questions partners actually ask: who knows someone at this company, what have we seen like this deal, what did we conclude last time. The portfolio-side rollout — proving AI in one company and carrying the playbook across the companies you own — is a separate motion, covered on our PE-backed companies page.

Relationship intelligence brings approved contact, email, and meeting data into a searchable view of who the firm knows. Answers should link to supporting sources and make gaps or stale records clear. Coverage depends on connected sources, permissions, and refresh timing.

Banker outreach stops dying in the inbox. Each approach is read, filed into the CRM, and summarized — risks, takeaways, action items — then scored against who the firm knows and what it has done before. Screening a new deal means checking it against your own history: have we done anything specific here, and does it validate or invalidate the thesis? Partners spend their time on the deals the firm is positioned to win.

Through your gate, not around it. We run the vendor-vetting process your COO already applies: SOC 2 report, due-diligence questionnaire, and a deployment where everything — data, models, logs — stays inside your own Microsoft 365 and Azure tenant. LP personal data is excluded from scope by design. The compliance partners who are behind where the models are get documentation, not promises. Our on-premise AI page covers the deployment posture in detail.

The buyer’s own name for it was an enterprise, firmwide brain — one intelligent system that stores the firm’s data and answers across all of it, instead of a separate retrieval mechanism per system. Think of it as an associate that never forgets anything you’ve ever told it. It is not another platform to adopt; it is a layer over the systems you already run.

Because ad-hoc chatting sends every question — including the deep, analytical ones — to the most expensive frontier model. One person at the sponsor hit $400 in a month without knowing how. A firm brain fixes the economics as part of the build: an ingestion layer that doesn’t token-max the expensive model, and routing that shifts load to lower-cost models where answer quality allows it. We benchmark against firms your size and set the initial token budget with you.

Adoption fails when AI arrives as a new place to work. This answers where partners already ask — Teams, email, text — and starts with the questions they already ask out loud: who knows who, what have we seen like this. The comfort curve — when to ask AI versus when to do it yourself — comes from seeing it work on the firm’s own data, live, which is what the pilot is for. The buy-in question — why it’s worth investing, how it helps do more with less — gets answered with the firm’s own numbers, not a deck.