We’re building DataRiver, a tool that converts PDF bank statements into structured, Excel-ready data (transactions, balances, dates), with a strong emphasis on privacy.
Unlike generic PDF extractors, DataRiver runs on a privately developed AI model trained specifically on bank statement layouts. Uploaded files are processed by our own model and are not sent to third-party LLM APIs, nor reused for training.
This approach came from working with sensitive financial data where accuracy and data isolation matter more than general-purpose flexibility. Many real-world statements include inconsistent tables, wrapped descriptions, or mixed formats that break rule-based tools.
Key points:
Private, domain-specific model for financial statements
High accuracy on messy, multi-column PDFs
No reuse of customer data for training
Files processed transiently and not stored long-term
DataRiver is used by accountants, auditors, and founders who need clean data without risking data exposure.
We’d appreciate feedback from anyone dealing with financial PDFs, especially on edge cases, privacy expectations, or compliance requirements.
