Fund operations teams handle a relentless stream of documents: capital account statements, quarterly reports, capital calls and distribution notices, each arriving in its own layout from a different general partner. The instinct is to reach for off-the-shelf OCR. It rarely holds up. Here is why, and what a governed approach looks like instead.
Generic OCR was built for a different problem
Optical character recognition was designed for clean, repeatable forms: a passport, an invoice template, a tax document where the same field sits in the same place every time. Private markets documents are the opposite. A capital account statement from one GP looks nothing like the next. Figures live in tables, in narrative paragraphs, and in footnotes that change the meaning of the number above them.
So OCR may read the characters correctly and still get the answer wrong, because it has no understanding of what a "called capital" line means versus "recallable", or which currency a sub-total is in, or that a negative in brackets is a distribution rather than a fee. The reading is solved. The interpretation is not.
The hidden cost sits after extraction
When extraction is unreliable, the real work moves downstream. Someone has to check every figure, reconcile it against the source, and key the corrections by hand. The tool promised to remove manual effort and instead added a verification step on top of it. Worse, the output usually lands in a spreadsheet, disconnected from any system of record, so the same data is re-entered again later.
What a governed approach changes
A document-extraction approach built for private markets does three things differently:
- It reads in context. It recognises the document type and the meaning of each field, not just the characters, so a capital call is understood as a capital call.
- It routes exceptions to people, not everything. High-confidence items pass through; only the uncertain ones surface for human review. The team moves from transcription to oversight.
- It writes into one governed dataset. The output is structured, attributed to its source document, and auditable, rather than scattered across spreadsheets and inboxes.
That is the design behind Extractor AI and DataHub within daappa Studio+. Extractor AI does the reading with human review on the exceptions; DataHub holds the result as a single governed dataset; Analytics turns it into reporting without a manual rebuild each quarter.
It sits above your fund accounting, not in place of it
A reasonable worry is that adopting this means replacing the back office. It does not. daappa Studio+ is administrator-agnostic and sits above existing fund accounting. The accounting engine stays where it is; what goes is the manual document and data layer sitting on top of it, the part that still runs on spreadsheets and email.
That is the daappa view in one line: your fund accounting is covered. The layer above it isn't. Document extraction is where that layer usually starts to hurt, and where it is most worth fixing first.