Document extraction for multi-family offices and wealth managers

Financial document processing is uniquely challenging. A single portfolio report can contain dozens of products, each with its own ISIN, currency, maturity date, and coupon structure. Bank statements vary wildly between institutions. Structured product term sheets are dense, multi-page documents with nested tables.

The manual reality

Most multi-family offices and wealth managers still process these documents by hand. An analyst opens a PDF, locates the relevant data points, and types them into a spreadsheet. For a single client with 15 products across 4 banks, that's hours of work — repeated monthly.

What AI extraction changes

With schema-driven extraction, you define what you need once — client name, product type, ISIN, currency, maturity, nominal value — and the system handles the rest. It reads each document visually, extracts the relevant fields, and outputs clean, structured data.

Handling subtables

Financial documents often contain line items within line items. A portfolio report might list 30 positions, each with 8 fields. Orkom's subtable feature extracts these nested structures cleanly — each position becomes a row in a subtable with its own columns.

Cross-document aggregation

Once extracted, you can use formulas to compute across documents — total AUM per client, weighted average maturity, exposure by currency. The extraction layer feeds directly into a computation layer.

Security considerations

Financial data demands the highest level of confidentiality. All processing happens on encrypted European infrastructure. Documents are never stored by AI providers and never used for training. Per-organization data isolation is enforced at every layer.

Getting started

The fastest way to evaluate is to upload a few representative documents and define your extraction schema. Within minutes, you'll see structured output with confidence scores — and know exactly whether the system handles your document complexity.