Pasta & GuitaPasta & Guita
Day to day

Categories that fill themselves in

Every transaction arrives classified. You only correct the misses.

How the app categorizes

Categorization uses a 5-layer pipeline, in priority order:

1. File metadata β€” if your bank includes a category in the CSV, it's used directly 2. Your rules β€” ones you created manually or approved 3. Merchant database β€” 440+ known merchants by country 4. Learned patterns β€” corrections you made before (β‰₯2 times = pattern) 5. Keyword classifier β€” last-resort heuristic

Default categories

The app comes with 36 hierarchical categories (14 parents + 22 children). You can use them as-is, customize them, or start from scratch.

Automatic rules

When the app detects you always categorize the same merchant the same way (β‰₯3 times, β‰₯80% consistency), it suggests creating a rule. Rules are created disabled β€” you approve them before they apply.

Machine Learning (on-device)

Three models trained with 3,197+ samples and 7,680+ phrases:

β—†Merchant classifier β†’ identifies parent category and subcategory
β—†Phrase classifier β†’ understands full descriptions in natural language
β—†Entity extractor β†’ detects amount, date, merchant, and account from free text

Everything runs on your device. No data ever leaves it.

Financial DNA

The app observes each category's behavior and builds a probabilistic profile:

β—†Is it a fixed or variable expense?
β—†Is it essential or discretionary?
β—†How much do you typically spend?
β—†Is the pattern changing?

If it detects a category isn't behaving as you classified it, it alerts you with an insight.