AI document intelligence and financial data extraction platform
A multimodal platform that turns financial reports, filings, and investor decks into structured, validated data - with Excel export and natural-language document Q&A.
- Client
- An investment firm (anonymized)
- Techniques
The problem - Getting information out of financial reports, filings, and investor presentations was slow and manual. Dense, inconsistently formatted documents made pulling numbers into anything analyzable error-prone - the kind of work that stalls diligence.
What we built - A multimodal extraction pipeline that reads document layouts visually and pulls out structured data, preserving tables, headers, and relationships. Extraction is schema-validated with Pydantic contracts and structured JSON, so every value passes type and structure checks before export; validated output flows to Excel via pandas and openpyxl. An upload-once, query-many design on the Gemini File API allows repeated extraction and Q&A without reprocessing, plus automated classification, summarization, and table detection.
The outcome - Converts complex documents into validated datasets on demand, lets users check and export clean Excel workbooks, and answers natural-language questions over anything uploaded - compressing hours of manual work into a repeatable, checkable pipeline.