Extract fields from vendor receipts. Vendor, invoice number, amount, date, category. Deterministic rules. No ML. No data leaves your machine.
Vendor receipts arrive as unstructured email text.
Every vendor sends receipts differently. Amazon, Stripe, Uber, restaurants — each has its own format. Manually copying fields into accounting software takes 40+ minutes per week.
Existing tools are overkill for small teams.
Expensify and Ramp are built for enterprises with dedicated finance teams. Small businesses and freelancers need something lightweight that just extracts the fields.
Feed it a plain-text email body from a vendor receipt. Get back structured fields:
Vendor name extracted from the From line.
Order or invoice number.
Total amount and currency (USD or EUR).
Receipt date (YYYY-MM-DD).
Auto-classified: office_supplies, software, travel, meals, marketing, other.
$ echo '{"text":"From: Amazon Business\nOrder Total: $156.78\nDate: 2026-01-15\nOffice Chair"}' | python process.py
{"vendor": "Amazon Business", "invoice_number": null, "amount": 156.78, "currency": "usd", "date": "2026-01-15", "category": "office_supplies"}
Deterministic regex rules. No model, no API call, no data leaves your machine. Same input to same output, every time.
An LLM extracting receipt fields is a black box you cannot audit.
When an extractor says "the amount is $156.78", you need to know why. A rule you can read is a rule you can trust, debug, and improve.
EUR49 one-time. Runs locally. No subscription, no cloud, no data collection.
Buy now - EUR49