Private AI for investor relations and board reporting
This post covers using a self-hosted model to draft board decks and investor updates. It is not financial or securities-disclosure advice, and it doesn't address what you're legally required to disclose or when. Talk to counsel about that. What it does address is a narrower, practical question: once you've decided what needs to go in a board pack or an investor update, should the drafting of that document run through a general-purpose cloud AI tool or through infrastructure you control.
What's actually at stake
A board deck in progress routinely contains material non-public information: unreleased financial results, a pending acquisition, a fundraise still in progress, or a churn number that hasn't been shared outside the leadership team. An investor update draft often has the same problem in miniature, a candid line about a missed target or a customer at risk of leaving, written for a small, trusted audience and not meant to exist anywhere else. Running that draft through a cloud AI tool creates a copy of MNPI on infrastructure outside your control, and depending on your company's insider trading policy and information barriers, that alone may be worth flagging to whoever administers those policies, separate from any general privacy concern.
There's also a practical leak-surface issue: a board deck usually goes through several drafts before the numbers are final, and an early draft with a wrong or soon-to-be-revised figure sitting in a third-party log is a liability even if the final version is accurate and properly disclosed.
What self-hosting changes
Running the drafting model on your own infrastructure keeps draft financials, deal status, and board commentary inside the environment your finance and IR functions already control, with no vendor retention question and no need to separately clear a specific AI tool for use with MNPI. It doesn't change your disclosure obligations or your insider trading policy, those still apply exactly as they did before, it just removes a third party from the drafting workflow.
Where it's genuinely useful
The strongest use is formatting and consistency: turning a spreadsheet of quarterly metrics into board-deck-ready prose with the numbers stated correctly, or checking that this quarter's update uses the same metric definitions as last quarter's, a mundane but real source of investor confusion when it slips. It's also useful for drafting a first-pass narrative around numbers that finance has already finalized, freeing up the CFO's or head of IR's time for the parts that need real judgment: how to frame a miss, what to emphasize, what tone the board needs given where the company actually stands.
It's a poor fit for anything touching disclosure judgment, deciding what's material, how to characterize risk, or how to phrase forward-looking statements. Those decisions need a human who understands the company's specific legal exposure, and ideally counsel's review, not a model's best guess at standard IR language.
Quality tradeoffs, honestly
For turning finalized numbers into readable board-deck prose or checking a draft against last quarter's terminology, a self-hosted model performs reliably. It's weaker at the persuasive, high-stakes narrative writing that goes into framing a difficult quarter for a board or investor group, that kind of writing benefits from a frontier model's broader exposure to how similar situations get communicated well, and even then it needs heavy editing by someone who actually knows the audience. Use it for the mechanical first draft and keep narrative judgment with your CFO or head of IR.
Setup effort
Most of the value comes from giving the model your last several quarters of board decks and investor updates as reference material, so it can match your established format and tone instead of producing something generic. That's a straightforward document-loading exercise, typically a day or two, not a technical integration. Keep the model's access scoped narrowly to whoever prepares these documents; this isn't a workload to expose broadly across finance or the company.
Where the hardware fits
Board packs with multiple quarters of historical data attached are long documents, and a single Spark's 128GB of unified memory keeps that history in context for consistency checking without needing to split it up. At $0.79/hour in EU-Central, running this dedicated around board cycles, active during prep week, idle otherwise, keeps the cost trivial next to what a leaked draft financial figure could cost in investor trust.
A note on scope
Keep access to this tool limited to whoever already has legitimate access to the underlying numbers, finance, the CFO's office, and the head of IR. Self-hosting solves the third-party exposure problem; it doesn't solve an internal access-control problem, and MNPI still needs the same need-to-know discipline inside the company that it would need if no AI were involved at all. Treat the model as another system that touches sensitive data, subject to the same access review as any other finance tool.