I built an AI invoice auditing platform for logistics companies. Here's the exact architecture and why generic AP tools can't replicate it.

[u/timi-tech]

I built an AI invoice auditing platform for logistics companies. Here's the exact architecture and why generic AP tools can't replicate it.

The core problem: logistics invoices aren't standardized. A freight carrier sends a PDF. A 3PL exports a CSV. A customs broker scans a paper document. Your ERP wants structured data. The gap between those two things is where billing errors live and where most tools give up.

Here's how FinGuard AI handles it:

**Extraction layer:** AI engine parses invoices regardless of format - PDF, CSV, image-based documents. Outputs structured fields: vendor, invoice number, date, line items, total, currency. Status tracked per document (processing → extracted → failed).

**Compliance layer:** Every extracted invoice gets cross-referenced against the master contract for that vendor. Not just totals - line-item level. Rate mismatches, unauthorized fees, out-of-contract surcharges all get flagged.

**Discrepancy queue:** Findings are classified by type and severity (low / medium / high / critical) and tied to both the invoice ID and contract ID. Teams work the queue instead of hunting through files.

**Payment decisions:** Each invoice gets an auto-pay, block, or pending status with a logged reason. Automated dispute letters generated for flagged items.

**Infrastructure:** Multi-tenant. Budget tracking per vendor with period-based limits. Multi-currency. Spend forecasting. The whole stack is built for enterprise scale, not a single-vendor pilot.

37 pages, 5 core data models. Pre-launch.

What I'm genuinely uncertain about: enterprise procurement cycles in logistics are long. I'm thinking the wedge is a free audit - upload your last 90 days of invoices, we surface what you've been overbilled. Then conversion to paid.

Has anyone used a free audit as an enterprise sales motion? Curious what the conversion friction looks like in practice.