Create Docs from Invoices with Infofla

This guide shows how to convert vendor invoices (PDF/JPG/PNG) into structured data and human‑readable docs (Markdown/JSON/CSV) using Infofla. You’ll set up field extraction, validate results, and generate consistent documentation your team can publish or pipe into downstream systems.

Reference: Infofla website

---

Prerequisites

• A set of representative sample invoices (at least 5–10, covering different vendors/layouts)
• An Infofla account and access to a workspace/project
• Decision on output format(s): Markdown for readable docs, JSON/CSV for data pipes

---

1) Define your target schema (fields you care about)

Start by deciding the exact fields you want in your final docs. Keep names stable and explicit.

json

Tip: Capture optional fields you might need later (e.g., purchaseOrder, paymentTerms, bankIban).

---

2) Create an Infofla project and upload samples

1. Create a new project (e.g., “Invoice → Docs”).
2. Upload a diverse set of invoices: different vendors, formats, and qualities.
3. Group similar layouts if you expect multiple templates (e.g., Vendor A vs Vendor B).

Goal: Give the extractor enough variety to learn anchors and patterns.

---

3) Configure extraction: anchors, patterns, tables

Use a mix of anchors (labels like “Invoice #”, “Total”), regex patterns, and table detection for line items.

yaml

Tips:

• Add multiple synonyms for robustness.
• Normalize currencies and dates.
• For variable layouts, prefer multiple weak anchors over a single strict one.

---

4) Test and validate on samples

Run extraction on your sample set and review field‑by‑field:

• Spot‑check high‑impact fields: invoiceNumber, total, tax, dueDate
• Confirm table parsing: row count, numeric totals, currency symbols
• Add post‑processing rules (e.g., trim whitespace, uppercase VAT IDs)

Iterate: adjust anchors/regex/date formats until accuracy is acceptable (≥99% on key totals).

---

5) Map data to a documentation template

If you want human‑readable docs, design a Markdown template. Keep it deterministic and concise.

md

Notes:

• Use a templating syntax supported by your Infofla setup (e.g., Handlebars‑style shown above).
• Keep frontmatter minimal and machine‑readable.

---

6) Choose outputs and destinations

Common outputs:

• JSON: for pipeline ingestion (ETL, data warehouse)
• CSV: for quick analysis/spreadsheets
• Markdown: for readable docs in repos/wikis
• Webhook/API: trigger downstream automations

Decide destinations:

• Git repository (docs as code)
• Object storage (S3/GCS/Azure Blob)
• Knowledge base or wiki
• Finance tools (AP/ERP) via webhook

---

7) Run at scale and monitor quality

• Batch process new invoices on a schedule or webhook
• Track extraction accuracy on key fields (totals, dates, identifiers)
• Add alerts when confidence drops or validations fail (e.g., subtotal + tax ≠ total)

Example validation ideas:

yaml

---

Example: From invoice to doc

Input (snippet):

text

Extracted JSON (key fields):

json

Rendered Markdown (excerpt):

md

---

Troubleshooting

• Numbers parse incorrectly: tighten currency/number regex and set locale.
• Dates flip day/month: explicitly list accepted dateFormats and prefer ISO output.
• Line items misaligned: add/rename header synonyms; increase header detection confidence.
• Missing totals: add anchors for both “Total” and “Amount Due”; consider multi‑anchor matches.

---

Next steps

• Expand schema with purchase order, payment terms, and IBAN.
• Add vendor‑specific templates for stubborn layouts.
• Automate publishing to your docs site or repo.

For platform details and updates, see the Infofla website.