use case
Pull line items out of invoices without building a data pipeline.
Frenchie parses invoices and receipts into structured Markdown so your agent can extract line items, totals, tax, and dates straight into your accounting tool.
the problem
Why this is a pain.
A vendor sends you an invoice as a PDF scan. Your accounting agent has to extract the line items, the totals, the VAT, the due date, and post it to Xero. Generic OCR gives you a blob of text with misaligned columns. You spend more time cleaning up the extraction than the invoice saves you.
Invoices come in every template under the sun. Most OCR tools give you text; few preserve the table structure that makes line items addressable. Tools that do structured extraction lock you into their schema — you can't point them at a slightly-weird invoice without retraining.
the workflow
How Frenchie handles it.
- 1
Drop the invoice (PDF, image, or scanned receipt) into your agent.
- 2
Your agent calls ocr_to_markdown via Frenchie. Tables preserve as Markdown tables — line items stay in their columns.
- 3
Your agent reads the Markdown, extracts the fields your accounting tool cares about (vendor, date, line items, subtotal, tax, total), and posts them via your accounting API.
- 4
If anything looks off — missing VAT number, unusual line item, total doesn't match — your agent flags it instead of silently posting wrong data.
example
What it looks like in practice.
Your prompt
/ocr ./invoices/acme-services-2026-04-15.pdf Extract vendor, invoice number, date, line items, subtotal, VAT, total. Post it to Xero as a draft bill.
Agent response
Invoice parsed. Fields extracted: - **Vendor**: Acme Services Ltd - **Invoice number**: INV-2026-0417 - **Date**: 2026-04-15 - **Due date**: 2026-05-15 (Net 30) - **Line items**: 1. Platform fee — April 2026 — $2,000.00 2. Support retainer — 10 hours @ $150/hr — $1,500.00 3. One-time onboarding setup — $500.00 - **Subtotal**: $4,000.00 - **VAT (7%)**: $280.00 - **Total**: $4,280.00 Posted to Xero as draft bill #DRAFT-8821. VAT rate on line 3 differs from the other two lines in the source doc — flagged for your review before approving.
What lands in your workspace
$frenchie ocr methodology.pdf
# Section 4 — Results The collected dataset confirms the hypothesis across all three runs. | Run | Accuracy | Latency | | --- | -------- | ------- | | 1 | 94.2% | 118ms | | 2 | 95.0% | 121ms | | 3 | 94.7% | 119ms | *Figure 3.* Distribution shifts across the validation cohort.
tips
Things worth knowing.
- A scanned receipt is usually 1-2 pages. At 1 credit per page, OCR on a year of receipts for a small team runs under $20.
- The quality of extraction depends on your agent's prompt — ask for specific fields by name and it returns structured data you can pipe straight into your accounting tool.
- For weird invoices (handwritten, photo-of-phone-screen), expect slightly lower confidence on totals. Always pass through a review step before auto-posting.
questions
Common questions.
Does it work on photos of receipts?
Yes. JPG, PNG, WebP all work. Skewed, wrinkled, or low-light photos still transcribe in most cases — accuracy drops for extreme cases.
Can I extract to a specific accounting schema?
Your agent does the schema mapping. Frenchie returns clean text; the agent pulls named fields via prompt. Works with any accounting tool that has a documented API — Xero, QuickBooks, FreshBooks, Wave.
What about multi-currency invoices?
Currencies come through as they appear in the invoice. Your agent handles conversion if your accounting tool needs it.
How does this compare to dedicated invoice-capture tools?
Dedicated tools like Dext or AutoEntry preprocess on their schema and are great if they support your vendor templates. Frenchie is more flexible — any invoice shape works, but your agent does the extraction logic. Use Frenchie if your vendors are weird or your schema changes often.
Try it with a real file of yours.
100 free credits on signup. No card. Drop a PDF or image from your own workflow and see the Markdown your agent gets back.