{"id":46970843,"title":"Show HN: Fyno – Automate repetitive bookkeeping tasks","points":1,"user":"alicele27","time":1770784301,"time_ago":"10 hours ago","type":"link","content":"

Hey HN,

We spend a lot of time mapping cryptic bank transaction descriptions (AMZN MKTP US*2X4Y9Z) to a Chart of Accounts. It’s a repetitive task that currently relies heavily on manual data entry or fragile, rules-based automation.

We built Fyno to automate this using LLMs to understand the context behind financial documents, not just the text.

The Engineering Challenge: The main hurdle in automated bookkeeping isn't just OCR; it's contextual reconciliation. A bank statement provides a amount and a cryptic vendor string. A receipt provides a detailed breakdown of line items, tax, and a potentially handwritten tip.

Fyno solves this by bridging the gap between bank feeds and documents:

Multimodal Document Parsing: We use vision models to interpret handwriting, messy scans, and skew. We don't just extract text; we understand that a handwritten note on a restaurant slip is a tip, not a second transaction.

Contextual Mapping: We take raw receipt data (line items) and mapped it to the cryptic bank transaction string using contextual similarity.

Adaptive Learning: Fyno learns from your corrections. When a user re-categorizes a transaction or teaches the AI to handle a unique invoice format, that learning is applied to future transactions within their workspace.

Tech Stack: We are utilizing a multimodal pipeline (incorporating OCR and LLM vision capabilities) to categorize expenses with 99%+ accuracy.

Accountant Workspace: Built with total data isolation for managing multiple entities.

We’re looking for feedback from developers on our approach to transactional AI. What’s the weirdest bank statement format you’ve had to deal with?","url":"https://www.meetfyno.com","domain":"meetfyno.com","comments":[],"comments_count":0}