Finance / Accounting

Statements that reconcile themselves

Client details anonymized at their request

Modelled

Manual Entry

Projection — not a delivered client result

Based on a system we run: The TBWX platform settlement parsing and reconciliation pipeline.

Assumptions

  • 3 staff at $3,000/month spending 50% of time on data entry
  • 2,000 invoices per month
  • 4% manual error rate as the baseline

Industry

Finance / Accounting

Services Used

Intelligent Document ProcessingInvoice AutomationAI Data ExtractionERP Integration

What we already run. TBWX receives settlement statements from multiple delivery platforms every cycle, in inconsistent formats, across dozens of outlets. We built the pipeline that parses them, reconciles the numbers and surfaces the discrepancies — commission errors, unauthorised discounts, missing payouts.

Why it transfers. An accounting firm re-keying invoices is doing the same job: structured data trapped in documents, extracted by hand, with a human error rate nobody measures.

What it could be worth to you. Three people spending half their time on data entry, at $3,000/month each, is about $54,000 a year of capacity locked in re-typing. The larger figure is usually the errors: at a 4% error rate on 2,000 invoices, that is 80 corrections a month, each costing chase time and occasionally a client relationship.

Exceptions still need a human. The point is that only the exceptions do.

Projection based on the assumptions listed, not a delivered result.

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