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Case study

Financial Management Web Application

Invoices ingested from mail, OneDrive, and FreeAgent — extracted, matched, and written back with full audit.

C#BlazorDocument IntelligenceFreeAgent

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Challenge

Supplier invoices arrive in mailboxes, folders and bank feeds. Reconciling them by hand against the accounts is slow, error-prone and leaves no clear record of who approved what.

Approach

An internal web application that follows the document through the whole workflow. Ingestion collects invoices from several monitored mailboxes, a OneDrive folder and FreeAgent bank-transaction attachments, tracking what has been processed so read state is irrelevant, with a backlog import for older documents. Extraction uses Azure AI Document Intelligence behind a circuit breaker, with plausibility checks that reject implausible invoice-number reads and a re-extraction pass for documents processed before a parser fix. Matching performs global assignment across candidate invoice–transaction pairs rather than greedy first-match, with confidence scoring, token-based reference matching, a manual-match screen that records the reason for every rejection, and a full audit trail. Write-back sends confirmed matches to FreeAgent — attachments and explanations — through a dedicated integration service with its own retry and paging logic. A scheduled job verifies that archived documents still match their recorded fingerprints, and a Blazor dashboard gives one view of the whole process. Infrastructure is defined as code; the pipeline applies database patches, runs a post-deploy smoke test and fails on warnings.

Outcome

  • Ingestion, extraction, matching, review and write-back in one interface — reconciliation ends in the accounting system, not a spreadsheet
  • High-confidence matches applied automatically; the rest surfaced for a person
  • A full audit log on every match, review and approval
  • In active live proving: the core flow is operational, and targeted fixes continue as real documents surface edge cases

Where judgement mattered

  • Global assignment over greedy matching — the obvious algorithm produces confidently wrong pairings
  • Plausibility guards on AI extraction, because a model's confidence is not the same as being right
  • Access control and audit designed in from the first line, not added afterwards
  • Saying "live proving" rather than "done" while the edge cases are still arriving

Technology stack

C# / .NET 10 · Blazor on Azure Static Web Apps · Azure Functions · Azure SQL · Azure AI Document Intelligence · Microsoft Graph · FreeAgent API · Microsoft Entra ID · infrastructure as code · Azure DevOps CI/CD

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