VeriLayer E-Invoice Auditor

### Project Story & Concept

In the labyrinthine world of B2B transactions, a single procurement often generates a multitude of documents: a Purchase Order (PO) outlining the initial agreement, a Delivery Note confirming goods received, and finally, the E-Invoice requesting payment. For many small to medium-sized businesses, reconciling these disparate 'layers of reality' manually is a painstaking, error-prone process, creating financial 'monsters' of overpayment, unnoticed discrepancies, or even subtle fraud.

Inspired by 'Frankenstein,' which built life from disparate parts, and 'Inception,' with its concept of layering realities, 'VeriLayer E-Invoice Auditor' seeks to reconstruct the 'truth' of a transaction by comparing its fragmented documentary evidence. Unlike a simple 'Flight Tickets' scraper that compares similar items, this system cross-references entirely different document types to ensure consistency across the entire procurement journey.

### How it Works

1. Input Collection (The 'Scraper' & 'Frankenstein's Assembly'): The system ingests various document types related to a single transaction:
- E-Invoice: Structured data (e.g., UBL XML, JSON, or other compliant e-invoice formats).
- Purchase Order (PO): PDF document (preferably text-searchable).
- Delivery Note: PDF document (preferably text-searchable).

Users can upload these documents, or integrate via email parsing or basic API connections for larger volumes.

2. Data Extraction & Harmonization (The 'Scraper's Core):
- For the structured E-invoice, the system directly parses the relevant fields (vendor, buyer, invoice number, line items, quantities, unit prices, totals).
- For PDF POs and Delivery Notes, the system employs lightweight, rule-based text extraction (using open-source libraries like `PyPDF2` or `pdfminer.six` in Python). It 'scrapes' key information such as PO number, line item descriptions, quantities, unit prices, and relevant dates.

3. Layered Comparison & Discrepancy Detection (The 'Inception' Engine):
- The core engine identifies common identifiers across documents (e.g., PO number, vendor ID, item codes) to link them effectively.
- It then performs a multi-layered comparison:
- Quantity Check: Does the quantity billed on the E-invoice match the quantity received on the Delivery Note and the quantity ordered on the PO?
- Price Check: Does the unit price on the E-invoice match the agreed-upon price on the PO?
- Item Match: Are all items on the E-invoice present and accounted for on the PO and Delivery Note? Are there any unbilled items on the Delivery Note/PO that should have been invoiced?
- Vendor/Buyer Verification: Ensures consistency in counterparty details across all documents.
- Total Reconciliation: Verifies the calculated totals based on quantities and prices across documents.

4. Anomaly Reporting (The 'Monster' Revealed): A clear, concise report is generated, highlighting all detected discrepancies. This report pinpoints exactly where the 'reality' of the invoice diverges from the PO and delivery details, allowing businesses to easily identify overcharges, under-deliveries, or potential fraudulent billing before payment.

### Implementation & Earning Potential

- Easy to Implement by Individuals: The initial version can be built using Python with readily available open-source libraries for PDF parsing (`PyPDF2`, `pdfminer.six`) and XML/JSON handling. The comparison logic is primarily rule-based. It can be hosted on low-cost serverless platforms (e.g., AWS Lambda, Google Cloud Functions) or a small VPS.
- Niche: Targets small to medium-sized businesses (SMBs) who lack sophisticated ERP systems but regularly manage supplier invoices, providing automated reconciliation that prevents costly errors and fraud. It's more specific than a generic e-invoice system, focusing on the audit aspect.
- Low-Cost: Requires minimal infrastructure. Development relies on open-source tools. The cost to run is negligible for individual developers.
- High Earning Potential: Businesses are eager to save money and time. This tool directly prevents financial losses due to errors, overbilling, and fraud, while significantly reducing manual administrative effort. A tiered subscription model (based on the number of documents processed per month) or a freemium model can generate substantial recurring revenue from a large SMB market, providing a clear ROI for users.