[EXAMPLE ISSUE - EN] Open-Source AI-Powered Invoice & Delivery Note Parser

[jmtdev0]

[EXAMPLE ISSUE - EN] Open-Source AI-Powered Invoice & Delivery Note Parser

💡 IMPORTANT NOTE: This issue is an illustrative example simulated from the responses of our ecosystem survey. While it represents a very common, real-world operational bottleneck, it uses a fictional company name and does not represent any real company or entity.

📌 Context & Origin

• Source: Community Tech Survey - Response to Question 7 & 8 ("Monotonous tasks" & "Wishlist").
• Company: Fake Company 123 (fictional example company).
• Industry Sector: Last-Mile Logistics & Distribution (Madrid, Spain).

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🛑 The Problem (The "Pain Point")

Our administrative and logistics team spends between 2 to 3 hours every single day manually transcribing data from physical and digital delivery notes (albaranes) and provider invoices into our internal ERP.

Because we work with dozens of different local suppliers, there is no standardized format. Some send clean PDFs, others send unstructured emails, and many drivers hand over crumpled, handwritten, or poorly printed physical receipts.

Why this is a problem:

• It is highly monotonous, error-prone, and adds zero strategic value to our daily operations.
• Delays in manual data entry lead to inventory mismatches and late payment flags in our accounting system.

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🎯 The Wishlist (User Story)

"I wish we had a simple, drop-in digital assistant where we could just dump any document—be it a crisp PDF or a quick smartphone photo of a delivery slip—and it would instantly recognize the vendor, extract the line items, quantities, and totals, and format it perfectly for our system."

High-Level Requirements

1. Multi-Format Ingestion: The tool must accept standard PDFs, JPG/PNG images (scans or photos taken by delivery drivers), and raw text.
2. Intelligent Extraction: Without needing rigid templates for every single provider, it should use LLM/Vision capabilities to extract:
   • Vendor Name & Tax ID
   • Document Date & Reference Number
   • Line items (Item description, Quantity, Unit Price, Total Price)
   • Tax breakdown and Final Amount
3. Human-in-the-Loop Validation: A simple, intuitive UI side-by-side view (Original Document vs. Extracted Data) where a human operator can quickly verify accuracy and click "Approve" before exporting.
4. Clean Export: Ability to download the approved data as a standardized JSON or CSV file that can be easily mapped to any legacy ERP.

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🚀 Proposed Open-Source Vision

Instead of buying an expensive, rigid enterprise OCR SaaS that charges per page, we want to build a lightweight, self-hosted, and community-driven solution.

• Privacy First: It should ideally support local, open-source Vision LLMs (like Qwen2-VL or Llama-3-Vision running via Ollama/LM Studio) so small businesses don't have to leak sensitive financial data to third-party APIs.
• Extensible: A modular structure where developers can easily add new export adapters or validation rules.

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🛠️ How You Can Help

If this project sounds like something you or your company would benefit from, let's build it together!

• Developers: We need help setting up the document processing pipeline and local LLM orchestration.
• UI/UX Designers: We need a dead-simple validation interface for non-technical office staff.
• Logistics Professionals: Share your sample anonymized invoices or edge cases to help us test the parsing accuracy!

💬 Drop a comment below or leave a reaction if you want to collaborate on this repository.