Private On-Premise AI for Hedge Funds: Query the data room without sending it anywhere.

Diligence materials, broker research, expert-call notes, internal memos, and LP correspondence are exactly the corpus a language model is good at reading, and exactly the corpus you cannot paste into one. Black Lily installs a complete model on hardware your fund owns, so the material never crosses your perimeter.

The documents you would most like to query are the ones your NDAs, your MNPI policy, and your data-room terms forbid you from uploading. A model on your own hardware is not a third party, so the question does not come up.

What your team uses it for

• Ask one question across an entire diligence data room instead of opening one PDF at a time, with the source passage cited back so an analyst can check it.
• Run an overnight job that pulls every new EDGAR filing on your names and has the diff against last quarter waiting before the open.
• Keep a project per name or per deal, holding its documents, its chats, and its outputs together, so the work survives the analyst who did it.
• Draft LP letters, DDQ responses, and side-letter comparisons from prior versions, so the first draft already uses the fund's own language.
• Search years of internal research at once: old memos, killed ideas, the reason you passed on a name in 2023, and the conversation where somebody worked it out.
• Spin up a dashboard for the Monday meeting, share it across the office, and let it expire on a timer instead of joining a pile of stale internal pages.

Why this one has to run in your building

• An outbound prompt is a data path your MNPI policy has to cover. Section 204A of the Advisers Act requires an adviser to establish, maintain, and enforce written policies reasonably designed to prevent the misuse of material nonpublic information. Every prompt sent to an outside model is a new route that material can travel, and a new control you have to write, test, and defend on exam. A deployment inside your perimeter removes the route rather than papering over it.
• Your NDAs already answered this question. Data-room access agreements and expert-network terms routinely bar transmitting confidential material to third parties without consent. Uploading a CIM to a hosted model is a transmission to a third party, whatever the vendor's retention settings say. A model running on a machine your fund owns is not a third party, so there is nothing to seek consent for.
• The prompt itself is the leak. Even where the document is public, the question is not. A prompt that names a ticker, a target, or a counterparty tells whoever receives it what your fund is working on and roughly when. On your own hardware, nobody receives it.
• Business records should land on systems you already capture. Since 2021 the SEC's off-channel communications cases have reached more than 100 firms and over two billion dollars in penalties, over business records that lived outside the systems the firm preserved. A local deployment writes prompts and outputs onto infrastructure your retention program already covers, instead of into a console you have to remember to export.

What hedge funds ask us

Our analysts already use a frontier model for public filings. Why change?

For genuinely public material, do not change. The case for a local deployment is the work you are currently not doing with a model at all, which for most funds is the larger and more valuable half: the data room, the expert notes, the internal research archive. Running both is a normal outcome, and scoping is where we mark the line between them.

Will an open-weight model actually handle a diligence document?

For reading long documents, answering questions against them, summarizing, and drafting from your own prior work, the current open-weight models hold up. For frontier reasoning on a novel problem they are still behind the best hosted models, and we will say so plainly in scoping rather than after you have bought a machine.

What does our ODD questionnaire say about this?

It gets easier to answer, because the honest response to whether client or fund data is processed by a third-party AI service becomes no. We give you the deployment diagram, the model provenance, and the data-flow description in a form you can hand to an allocator without redrafting.

About Black Lily

Black Lily installs private, on-premise AI systems from Philadelphia, Pennsylvania: a complete language model running on hardware the client owns, for hedge funds, registered investment advisers, and family offices whose most valuable documents are the ones they are not allowed to send to an outside service. Founded by William Dorman, Founder & CEO.

Phone: (432) 234-3779 · Book a free 30-minute scoping call: cal.com/black-lily/30min

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