UC2: Invoice Data Extraction & Coding

The Problem

Manual invoice keying averages 4 to 6 minutes per invoice across header data (vendor, invoice number, date, amount) and line items (description, quantity, price, GL account, cost center). For a company processing 100K invoices annually, this translates to 7,000 to 10,000 hours of FTE. OCR alone is insufficient; invoices vary in format, and coding to the correct GL account requires judgment based on item description and historical patterns.

What the Agent Does

Ingests invoice image (PDF/JPG/scanned) and applies OCR to extract text
Parses header information: vendor ID, invoice number, date, total amount, currency
Decomposes line items: quantity, unit price, description, UOM
Matches vendor ID against vendor master to auto-populate vendor name, tax ID, payment terms
Analyzes line-item descriptions and applies rule-based and ML-driven GL account coding
Matches line items to open POs where available, pulls GL account from PO
For ambiguous line items, suggests 2 to 3 GL account options and confidence scores
Generates structured invoice data ready for ERP posting
Flags exceptions (amount variance, unmatchable vendor, ambiguous coding) for human review

Data Requirements

Data Sources:

Invoice images (PDF, scanned, email attachments)
ERP AP module , Vendor master file, open POs, historical invoice coding
Chart of Accounts , GL account descriptions, hierarchies
Cost center master , Departments, business units, cost center codes

Data Classification:

Public: GL chart, cost center names
Internal Structured: Vendor master, PO data, historical coding patterns, COA
Internal Unstructured: Invoice PDFs, vendor correspondence
External Third-Party: None
Sensitive/Regulated: Cost center hierarchies, GL account assignments (SOX-controlled)

Data Quality Requirements:

Timeliness: Within 24 hours of invoice receipt
Completeness: Vendor master must cover 95%+ of incoming invoices; PO data must be current
Accuracy: OCR accuracy target 95%+ on structured fields (invoice number, amount, vendor); coding accuracy based on historical training data quality

Integration Complexity: Medium , OCR tool integration (Rossum, DocuSign Intelligent Indexing, or in-house model), ERP APIs to pull vendors/POs/COA, and feedback loop to learn from corrected coding

Score Breakdown

Criterion	Weight	Score (1-5)	Weighted
Time Recaptured	15%	5	0.75
Error Reduction	10%	5	0.50
Cost Avoidance	10%	4	0.40
Strategic Leverage	5%	4	0.20
Data Availability	15%	4	0.60
Process Clarity	15%	4	0.60
Ease of Implementation	10%	4	0.40
Fallback Available	10%	4	0.40
Audience (Internal)	10%	4	0.40
Composite	100%		4.30

Why It Scores Well

Time recapture is massive: 70% reduction in keying time (from 5 min to 1.5 min per invoice) across 100K invoices = 8,750 FTE hours recovered annually. Error reduction is significant (fewer GL coding errors, fewer amount transcription errors). Process clarity is high because GL coding rules can be documented and continuously refined through feedback loops.

Regulatory Alignment

SOX 404: Requires documented and tested controls over transaction recording
GAAP: Invoice coding accuracy is foundational to accurate accrual accounting
ASC 606: Revenue recognition depends on correct invoice line-item coding

Sprint Factory Fit

Sprint 0 (2 weeks) + 1 build sprint (2 weeks)

Fits Sprint 0 + 1 build sprint. Discovery focuses on vendor-to-GL mapping rules and historical coding examples. Sprint 0 covers OCR integration and rule engine configuration. Build sprint (2 weeks) focuses on ML model training for ambiguous coding scenarios and feedback loop implementation.

Comparable Implementations

Rossum AI Invoice Extraction , 95%+ accuracy, reduces manual keying by 70%
DocuSign Intelligent Indexing , Automated document classification and data extraction

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