Invoices, Bills

Invoices and bills look standardized until you process them at scale. Vendors change templates without notice, PDFs arrive as scans, line items are inconsistent, and critical details (tax, totals, payment info) must be correct every time. In practice, invoice automation fails not because OCR can’t “read text,” but because real invoices require layout understanding, normalization, and business validation.

Enterflow has hands-on experience extracting structured data from invoices and bills across formats, languages, and industries. We build workflows that produce reliable, posting-ready outputs and route exceptions clearly—so automation is safe in production.

What we typically extract (and why it matters)

Header fields (identity and references)

• Supplier name, address, VAT/tax ID

• Invoice number, invoice date, due date, payment terms

• PO number / project reference (when present)

• Currency and payment method indicators

Why it matters: These fields drive posting, matching, approvals, and audit trails.

Financial totals (the “must be correct” layer)

• Subtotal, total tax/VAT, grand total

• Discounts, shipping/freight, surcharges

• VAT breakdowns (multiple rates, exemptions)

Why it matters: Totals are where errors become financial leakage.

Payment details

• IBAN / bank account details, bank name

• Payment reference / remittance reference

• Sometimes: SWIFT/BIC, local bank identifiers

Why it matters: Payment data must be accurate and securely handled.

Line items and tables

• Item/service descriptions

• Quantities, unit prices, unit of measure

• Tax rates per line, totals per line

• Item codes/SKUs (where present)

Why it matters: Line items unlock PO matching, analytics, and cost transparency—but they are also the hardest part.

Our approach: extraction plus validation (not “OCR only”)

In our experience, high-quality invoice processing requires three layers:

1) Document understanding

• Handle mixed quality PDFs, scans, photos, rotated pages, stamps

• Identify invoice structure (header zones, totals region, line-item table boundaries)

• Detect multi-page invoices and continuation tables

2) Field extraction tuned to invoice reality

• Vendor template variability and “look-alike” layouts

• Multi-language labels and regional formatting (dates, decimals, thousands separators)

• Inconsistent naming (e.g., “Total Due,” “Amount Payable,” “Grand Total”)

3) Accounting-grade validation and exception routing

We implement checks that catch the issues that matter:

• Header total vs sum of lines vs tax reconciliation

• VAT/tax ID presence and format checks (configurable by region)

• Duplicate invoice detection (supplier + invoice number + amount + date patterns)

• Currency and rounding tolerance rules

• Missing PO references, mismatched terms, or unusual charges flagged for review

This is what turns extraction into something finance teams can rely on.

Common invoice edge cases we design for

Based on typical invoice/bill variability, we account for:

• Vendor-specific tables (merged cells, wrapped descriptions, multi-rate VAT)

• Credit notes and negative totals, partial refunds, and adjustments

• Pro forma invoices and statements that resemble invoices but behave differently

• Multi-page line items and “continued on next page” patterns

• Bank details in footers with inconsistent formatting

• Scanned invoices where text quality varies widely

• Bundled PDFs (multiple invoices in one file) and email threads with attachments

What you get as output

We deliver outputs that are immediately usable downstream:

• Structured JSON for header fields and totals

• Normalized line items array with consistent schema

• Optional table export to CSV

• Confidence signals and rule outcomes (pass/fail reasons)

• Optional traceability (page references / evidence mapping for review)

Key data we track (so accuracy is measurable)

For invoice and bill workflows, we typically report:

• Field-level accuracy for critical fields (invoice number, total, VAT, supplier)

• Straight-through processing rate (no human touch)

• Exception rate with root cause categories

• Cycle time from receipt to posted/approved-ready record

• Cost per invoice vs manual processing

• Recovered value (duplicates prevented, credits captured, mismatches flagged)

Integrations we commonly support

Invoice/bill extraction is most valuable when it feeds your systems:

• ERP/accounting platforms

• AP automation tools and approval workflows

• PO/receiving matching systems

• Data warehouse / analytics pipelines

We integrate via API, webhooks/events, secure file handoffs, or adapted patterns for legacy environments.

Ready to automate invoices and bills reliably?

If you share a representative sample set (20–50 invoices across your main vendors, plus a few edge cases), we can quickly outline:

• extraction coverage and expected accuracy,

• the validation rules that will prevent errors,

• how exceptions will be handled,

• and an implementation plan from pilot to scale.

Contact: info@enterflow.ai
Website: https://enterflow.ai/