AI Invoice Auditor.

The problem

During month-end close, a Controller exports the month's AP transactions from the ERP, pulls the matching PDF invoices, and walks through a risk-based sample side by side — checking that amounts, vendors, and expense categories match policy and that receipts look legitimate. A single Controller processes around 1,500 invoices a month.

The core pains: it's too slow to review the full population, so Controllers sample — but auditors increasingly want full coverage, not samples. Manual PDF-versus-transaction comparison eats hours, wrong expense categories are hard to catch at scale, suspicious receipts go undetected, and the audit trail is built by hand, slow and error-prone.

The solution

AI Invoice Auditor is a batch auditing tool built for the Controller's monthly review — not the AP clerk approving one invoice at a time. Users upload ERP transaction CSVs and a folder of invoice PDFs, and the system runs one AI call per invoice to return structured verdicts, quoted evidence, and status mappings.

Unlike a vendor's black-box score, it's built to fit a non-standard chart of accounts and expense policy, and to give the auditor reasoning they can quote. The output preserves an audit trail, supports per-item comments and re-runs, and exports audit-ready CSVs. It's framed as decision-support (Sheridan autonomy level 3-4): the AI narrows the population to flagged items and shows extracted values, and the Controller signs off and can edit any field.

How it works

For each transaction-PDF pair, GPT-4o-mini reads the PDF as a vision input and runs checks — amount_match, currency_match, vendor_match, category_fit, and legitimacy — each returning result (pass/fail/unclear), a 0-1 confidence, quoted evidence, and a short explanation, as strict JSON. The application, not the model, assigns statuses (matched, mismatched, category-flagged, suspicious, needs human review) using per-check confidence thresholds tuned by the cost of a false positive. If any item fails, the batch keeps running and the item gets an "AI error" status with the exact reason.

The expense policy is pasted into the system prompt for the capstone (RAG over the full policy is planned for later). Prompt iteration split out currency, added a pdf_data extraction block, made amounts separator-tolerant, moved vendor matching to brand level, and strengthened legitimacy with layout and arithmetic sub-checks plus few-shot examples. On a 30-item gold set (re-labeled independently by a finance Controller with 28/30 agreement), the v7 re-run reached 150/150 per-check agreement and 30/30 status correctness, closing every legitimacy miss from v6.

Who it's for

This is an internal product for one company — Controllers and senior AP Managers who own the monthly AP review and prepare evidence for SOX and external audits. The economic buyer is finance leadership: the CFO who funds the build and the VP Finance.

It deliberately targets the Controller's monthly batch review, a workflow off-the-shelf tools like Stampli, AppZen, and SAP Concur don't serve because they're built for the clerk approving one invoice at a time. Off-the-shelf alternatives also can't fit the company's non-standard chart of accounts and expense policy without heavy, brittle customization.

Why it matters

This isn't a commercial market play — it addresses internal AP workload growth and rising audit expectations. Volume should grow 10-15% a year, and auditors increasingly want full-population testing rather than samples, making manual review unsustainable within a year or two.

Two CFO-level KPIs anchor the case: a cleaner external audit report (reduce audit findings by ~150 per cycle) and a shorter close (save ~8 working hours on AP review). Vision and reasoning models are finally good enough for finance work, but the real bar is trust — controllers don't yet trust AI for work that has to hold up in an audit, which is exactly why quotable evidence, calibrated confidence, and a full history timeline are the product's backbone.

At a glance

Project

AI Invoice Auditor

Built by

Serhii Ovdiienko

One-liner

AI Invoice Auditor is a batch auditing tool for controllers and CFO workflows during month-end close.