What AI Should Check Before Your Investment Memo Reaches an LP

Have you ever been ready to send an investment memo, only to notice that a figure no longer matches the model or a cited source doesn’t quite support the claim? You’re then retracing the research, checking versions and wondering what else needs another look.

If an LP finds the discrepancy first, you may have to explain both the number and how it made it through your review.

AI can help bring those questions forward. Used carefully, it gives the team another way to challenge assumptions, compare documents and identify weak evidence before the memo goes out, while a human reviewer determines what needs to change.

For an LP-facing investment memo, the review should cover five areas:

The value comes from investigating what the review uncovers. A list of AI-generated objections is only useful if someone can distinguish a consequential problem from a misunderstanding.

Are the financial comparisons actually comparable?

Imagine you are explaining why one company is growing faster than another. One reports in US dollars, the other in local currency. Both growth rates may be calculated correctly, yet the comparison can mix operating performance with exchange-rate effects.

That changes the question you need to answer. How much of the difference reflects the businesses, and how much reflects the basis of comparison?

Interactive figure: which company is growing faster?

AI can help flag changes in currency, reporting period, unit or definition across the materials it receives. The analyst then checks the original disclosures and decides whether the figures need to be recalculated or the conclusion qualified.

Valuation comparisons deserve the same attention. A paragraph can move between equity value and enterprise value without acknowledging the difference. A transaction summary can place a primary capital raise beside a secondary share sale and imply that the two figures describe equivalent events.

These distinctions are easy to lose as research becomes a concise narrative. Asking AI to look for them gives the reviewer another opportunity to catch the problem. Arithmetic should still be reproduced with a spreadsheet, calculator or code, rather than accepted because a model’s explanation sounds convincing.

Does the source support the sentence you wrote?

You open the citation, find a reputable report and recognize the subject. It is tempting to move on. But does the report support the precise claim in your memo?

A study published in Nature in February 2026 gives a recent example. In its scientific-literature test without external retrieval, GPT-5 fabricated 39% of cited paper titles. The researchers also found that references to real papers could fail to support the generated answer. Those results describe a particular research task and setup, not the error rate of AI search generally or of investment memos.

The same study offers an encouraging finding: a purpose-built system called OpenScholar, which retrieves scientific papers and uses them to construct answers, improved citation accuracy and answer quality. The practical lesson for a memo is to evaluate how the tool uses evidence, rather than assume a plausible reference has been checked.

AI can help compare your wording with the relevant passage or table and flag where the claim goes further. A reviewer still needs to assess the source’s quality, context and limitations. A company’s own forecast, for example, needs to remain identifiable as that company’s forecast.

Are the links and evidence still current?

A broken URL is frustrating when you are trying to reconstruct a claim shortly before sending the memo. It is also a different problem from weak evidence.

Ordinary software can check whether links resolve. A failed request may reflect access restrictions or automated-traffic blocking, while a working link may lead to a revised page that no longer contains the information you used. Neither result settles the claim’s accuracy.

Check the publication date, reporting period and version behind material statements. If a newer disclosure exists, determine whether it changes the analysis. AI can help flag apparent date conflicts and references that deserve another look, but replacing an unavailable link requires checking that the replacement supports the sentence.

Keep enough source detail to reconstruct the evidence. That becomes especially useful when several people have contributed research or the memo has gone through multiple revisions.

Do the memo, model and deck tell the same story?

A management call changes an assumption. The model is updated, the memo follows, and an older figure survives in the deck.

Sometimes the inconsistency is less obvious. The model distinguishes contracted revenue from pipeline, while the narrative describes the combined amount as committed. Each document may look reasonable in isolation, but an LP reading them together receives a different impression.

An AI-assisted comparison can help identify discrepancies in numbers, definitions, dates and scenario labels. Ask for references to the conflicting passages so the team can investigate them.

The reviewer then decides whether the difference is an error, a legitimate distinction in scope or an update that needs to reach the other materials. Simply making every figure match can erase a meaningful distinction and create another problem.

What will an LP take away from the charts?

The chart may be the part of the memo a reader remembers. Review it alongside the underlying data and the sentence explaining its significance.

Interactive figure: same data, four edits

AI can help flag potential mismatches between a visual and the argument around it. The team should reproduce the calculations and inspect the rendered page. Scale, labels and layout can alter the impression even when the underlying numbers are correct.

Who decides what needs to change?

AI can introduce confident errors while reviewing someone else’s work. OpenAI’s September 2025 research on hallucinations describes the continuing problem of plausible but false answers. The jagged technological frontier study, published in Organization Science in March 2026, found that AI assistance improved performance on some consulting tasks and worsened it on another. That publication reports an earlier GPT-4 experiment with 758 consultants, rather than a test of today’s models.

The human reviewer needs to challenge the AI’s objections, inspect evidence and assess materiality. Agreement from another model is not independent corroboration. A human approval step is equally weak if nobody has checked what supports the conclusion.

Confidentiality belongs in the same decision. Use approved tools, appropriate access controls and only the material needed for the review. Convenience is not sufficient reason to upload confidential information to a service.

Where a tested, appropriately governed AI-assisted check improves the work, routinely leaving it out becomes harder to justify. The responsibility is to build a review process that produces more defensible analysis and to remain accountable for the result.

For your next memo, start with the claims an LP is most likely to question. Could you explain the comparison, retrieve the supporting evidence and reconcile it with the model without retracing the entire project?

At Sirotin Ventures, that is the practical case for AI-augmented strategic communications: helping the evidence, assumptions and presentation hold together when someone looks closely. To discuss your LP-facing materials, contact angelica@sirotinventures.com.