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LedgerFlow — Invoice Processing Pipeline

CI

An automated accounts-payable pipeline on Azure: supplier invoices are captured, read with Azure AI Document Intelligence, reconciled against purchase orders and goods receipts by a three-way match, and either posted straight to the ERP or held in a review queue for a human. Everything inside tolerance flows through without manual typing; only exceptions surface.

Reference implementation. This is real, working engineering written to demonstrate the architecture — not a client deliverable. The customer scenario and all figures are illustrative; the code, tests, and infrastructure are not. Processing live documents needs your own Azure resources (see Configuration).

Architecture

   mailbox / upload
        │  (drops a PDF in blob storage)
        ▼
  ┌────────────────┐   Service Bus    ┌────────────────────┐
  │ CaptureFunction │ ───────────────▶ │ ProcessInvoiceFunc │
  │  (blob trigger) │   invoices-in    │  (queue trigger)   │
  └────────────────┘                  └─────────┬──────────┘
                                                │  extract (Document Intelligence)
                                                ▼
                                      ┌────────────────────┐
                                      │   ThreeWayMatcher   │  invoice × PO × receipts
                                      └─────────┬──────────┘
                                   auto-post ◀──┴──▶ exception queue (Azure SQL)
                                      │                     │
                                   ERP API            LedgerFlow.Api  ◀── React review UI

The three-way match is the heart of the system and lives in LedgerFlow.Core — pure, dependency-free, and covered by unit tests. Every Azure boundary (document extraction, messaging, ERP, reference data) sits behind an interface so the domain stays testable and the cloud stays out of the core.

Projects

Project Role
LedgerFlow.Core Domain model + three-way matcher and tolerance rules. No I/O.
LedgerFlow.Infrastructure EF Core (Azure SQL), Document Intelligence extractor, Service Bus, ERP + reference-data clients.
LedgerFlow.Functions Isolated-worker Functions: blob capture → Service Bus → extract → match → post.
LedgerFlow.Api Minimal API backing the exception queue (list / approve / reject) + /api/analytics aggregates.
web/ React + TypeScript + Vite review-queue UI.
infra/ Bicep for the whole estate; managed identity throughout, no secrets in app settings.

Build & test

Prerequisites, a clean-machine setup and troubleshooting are in INSTALL.md. Short version:

dotnet test LedgerFlow.sln      # matcher + per-supplier policy + pipeline orchestration tests
cd web && npm ci && npm run build

The pipeline orchestration (extract → match → post-or-queue, including duplicate suppression) is covered end-to-end in InvoiceProcessorTests against an in-memory database and a fake ERP. Per-supplier tolerance overrides live in SupplierPolicies — a strategic partner can carry a looser price band while a repeat over-biller gets zero headroom.

Both run in CI on every push, along with az bicep build over the infrastructure.

Run locally

docker compose up -d                       # SQL Server + Azurite
dotnet run --project src/LedgerFlow.Api    # exception-queue API on :5080
cd web && npm run dev                      # review UI on :5173, proxied to the API

The Functions worker and live extraction need real Azure resources — see below.

Configuration

LedgerFlow.Functions reads (via managed identity in Azure, local.settings.json locally):

Setting Purpose
DocumentIntelligenceEndpoint Azure AI Document Intelligence resource URL
ServiceBusConnection Service Bus namespace
BlobServiceUri Storage account for the invoice inbox
SqlConnectionString Azure SQL exception-queue database
ErpBaseUrl ERP posting + reference-data API

Provision everything with az deployment group create -g <rg> -f infra/main.bicep.

Design and code by Nick Zivkovic.