Inbox to Cash.

The problem

Small-business accounts receivable teams spend their mornings in a manual "stare-and-compare" detective game: matching bank deposits to open invoices using remittance details scattered across a cluttered AR inbox, PDFs, and portals. Payments arrive with cryptic references, transposed invoice numbers, or none at all.

When cash doesn't match the invoice balance, clerks manually calculate net versus gross for short-pays, unearned discounts, and lump-sum payments, then re-key everything into the ERP — slow, error-prone work. Enterprise tools like HighRadius and Billtrust are too expensive for this segment, leaving Excel and Outlook as the status quo.

The solution

Inbox2Cash is an AI-assisted cash application tool that converts messy, unstructured data into clear reconciliation decisions. It ingests bank files, the open AR ledger, and remittance messages, then recommends invoice matches with confidence scores and plain-language reasoning.

Unlike legacy OCR systems, it is template-agnostic — the LLM understands any remittance format — and it performs semantic matching, recognizing that a bank sender like "ABC Holdings" maps to "Alpha Beta Corp," or that a $200 variance is an authorized early-payment discount. High-confidence matches route to a straight-through-processing queue for one-click bulk approval, while low-confidence items go to a Conflict Resolver for human review, with an audit trail for every posting.

How it works

A three-way reasoning engine cross-references bank line items, email remittances, and open AR invoices. The core model is GPT-5.2 for complex reasoning, with GPT-5 mini for simpler, high-volume tasks like exact invoice-number extraction. The system enforces zero-tolerance arithmetic — matched invoices minus justified deductions must equal the bank amount exactly, or the match is flagged Low confidence.

Strict XML-tagged prompting isolates inputs to prevent injection, and a "safe failure" rule treats correctly flagged uncertainty as a successful outcome that routes work to a human rather than creating a bad accounting record. Because this is a financial workflow, low-confidence matches are never auto-posted. Evaluation targets include >99.5% entity extraction accuracy, 0% hallucination on high-confidence matches, and a false auto-post rate under 2%.

Who it's for

The primary end user is the AR Specialist or clerk, often overwhelmed managing hundreds of payments, whose goal is a high straight-through-processing rate and a clean open-AR list by end of day. The economic buyer is the CFO or Controller, who cares about efficiency, DSO reduction, and accurate reporting.

The product is B2B SaaS with tiered pricing based on transaction volume, targeting the "Business Banking" segment of companies with $5M–$40M in annual revenue — roughly 260,000 companies in the US per the SBA.

Why it matters

The B2B payment automation market is projected to grow at roughly 10–15% CAGR through 2030, with the $5M–$40M segment accelerating as sophisticated AI tools become affordable for smaller balance sheets. The democratization of LLMs enables unstructured data extraction without expensive OCR templates or rigid rules engines.

The stakes are financial integrity: hallucinated figures or unsafe auto-posts create bad accounting records. Inbox2Cash addresses this with confidence thresholds, required evidence, and human-in-the-loop control, launching first as a limited pilot with 5 clients before scaling.

At a glance

Project

Inbox to Cash

Built by

Claudio Melzer

One-liner

Inbox to Cash is an AI-assisted cash application tool for small-business accounts receivable teams.