Why Generic AI Gets PE Financials Wrong (and How to Fix It)
Four mechanical reasons generic AI misreports PE deal financials, the engineering fixes for each, and the questions buyers should ask any AI vendor before trusting a number.
Direct Answer
Generic AI gets private equity financials wrong for four mechanical reasons: it conflates forecast and actual periods that CIMs mix freely in the same tables, it answers period questions like '2025 revenue' without a basis or as-of date, it lets stale derived extractions shadow fresh source documents in retrieval, and it fabricates citations when retrieval returns nothing. Each failure has a specific fix: basis-first labeling with actual-first series selection, explicit period and as-of disambiguation, supersession rules for derived documents, and fail-loud behavior backed by page-cited extraction and eval-gated releases.
Failure 1: forecast-vs-actual conflation
CIMs present actuals and projections side by side; generic retrieval answers with whichever chunk ranks highest. Fix: basis-first labeling plus actual-first series selection.
Failure 2: period and as-of ambiguity
'2025 revenue' is not a well-formed question. Fix: answers state their basis and as-of date as a matter of format, and ambiguous questions are disambiguated, not silently resolved.
Failure 3: derived-document supersession
Stale extracted tables and cached analyses shadow fresh sources in retrieval. Fix: supersession rules that retire derived artifacts when sources change, plus scheduled reconciliation sweeps.
Failure 4: citation fabrication on empty retrieval
When retrieval returns nothing, some systems fabricate plausible page references. Fix: fail loud with 'not found in the provided documents' and page-anchored citations that resolve to real pages.
Frequently Asked Questions
What is basis-first labeling?
Basis-first labeling means every extracted financial figure carries its basis (actual, estimated, projected, pro-forma, budget) as structured metadata attached to the number itself. Answers then lead with the basis, so a projection can never be silently presented as a fact.
What is an eval-gated deployment with golden answers?
It is a release discipline where a suite of known questions with strict expected answers, drawn from real deal documents, runs before every software change ships. If a question stops returning the right figure with the right basis label and citation, the release is blocked.
Should PE teams review AI-generated outputs before use?
Yes. ReturnCatalyst is a decision-support platform. Deal, finance, legal, tax, valuation, underwriting, and portfolio conclusions should be reviewed by qualified professionals before use.