u/timi-tech

I talked to 20 logistics finance teams about invoice auditing. Every single one was doing it manually in spreadsheets.

Not small companies either. Teams moving hundreds of millions in freight spend annually.

The process was always some version of this:

• Download invoices from 6-12 different vendor portals
• Pull the master contract from a shared drive (if it's been updated)
• Cross-reference line items by hand
• Email the vendor when something looks off
• Wait. Follow up. Wait again.

The average team I spoke with had 2-3 people spending 30-40% of their time on this. One controller told me they'd stopped disputing charges under $500 because the labor cost wasn't worth it. Their vendor count was 47.

That's not an edge case. That's the industry standard.

The tools that exist either solve for AP automation (not contract compliance) or require a 6-month enterprise integration before you see anything useful. Nothing is built specifically for multi-vendor logistics billing at scale.

So I built FinGuard AI - an auditing platform that extracts invoice data across formats, cross-references it against master contracts automatically, and generates dispute letters when it finds a mismatch. The discrepancy queue flags by severity. Payment decisions (auto-pay, block, pending) get logged with reasons.

Still pre-launch. But the problem is clearly real and clearly underserved.

Has anyone here dealt with this in logistics or supply chain finance? Curious whether the $500 dispute threshold thing resonates - or if there are failure modes I haven't mapped yet.

Logistics companies are getting quietly overbilled by vendors and most of them will never know it.

Not because of fraud, necessarily. Because of scale.

When you have 40+ active vendors, each sending invoices in different formats - PDFs, CSVs, scanned documents, portal exports - and each contract has its own rate tables, fuel surcharge formulas, and accessorial fee schedules, the math becomes genuinely unauditable by hand.

So teams make a decision, usually implicit: audit the big invoices, let the small ones through.

The problem is that billing drift doesn't come from one $50,000 error. It comes from 300 invoices each carrying a $180 unauthorized fee. That's $54,000 that never gets disputed because no individual line item clears the threshold worth fighting over.

Existing AP tools don't catch this. They process invoices - they don't compare them against contracts. ERP integrations flag duplicate invoices. They don't flag a fuel surcharge that's 2.3% above the contracted rate.

This is the gap FinGuard AI is built for. AI extraction engine that parses multi-format invoices, maps line items to contract terms, and surfaces mismatches by severity - critical down to low. Dispute letters generated automatically. Multi-vendor, multi-currency, multi-contract.

Building this as an enterprise SaaS specifically for logistics and supply chain. Pre-launch.

Curious if anyone has data on what percentage of logistics invoices actually contain billing errors - I've seen estimates ranging from 3% to 12% and the variance is huge.

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.

hey can someone test this game i made out js need some feedback.