Automatic wire-to-invoice
reconciliation for AR teams.
The moment a customer pays, you find out who sent it, how much, and which invoice it clears. FIFO when multiple invoices match the amount. Uncertainty flagged explicitly. No autonomous writes to your accounting system. The human approves, the sync records.
Stop guessing which invoice a wire paid.
Your AR manager used to stare at bank transfers, cross-reference invoices by hand, and guess when the payer name didn’t match the billing entity. Mistakes compounded: invoices flipped to paid on the wrong customer, open balances drifted, credit decisions got blurred.
This blueprint replaces the manual lookup with a four-second pipeline. Every wire scanned. Every match reasoned. Every uncertainty surfaced.
Translated to your operation: roughly two hours a day of manual AR work replaced with a Slack skim. If you clear 30+ wires a month, that’s a full workweek reclaimed every quarter, without changing your accounting system or growing your team.
Why it matters.
Manual wire reconciliation is the invisible tax of multi-entity AR. It does not require judgment. Yet your AR manager spends their day doing it anyway.
Mechanical work, replaced cleanlyThe work is mechanical, it breaks down at scale, and the cost of a miss compounds: invoices flipped to paid on the wrong customer, open-balance drift, stale credit decisions. Automating the match does not replace the AR manager; it replaces the manual lookup, so the AR manager spends time on what actually requires judgment.
Speed matters too. When the CFO asks “did Acme pay yet?”, the answer is already in the Slack channel, already attributed, already reasoned. Decisions that used to wait for a weekly reconciliation happen on the day the wire lands.
How it actually works.
Four steps, on a 5-minute heartbeat. Deterministic where possible, AI judgment only where it’s earned.
Each bank is polled on its own cadence: API banks walked page by page until the feed is empty, file-based banks ingested via SWIFT, MT940, or emailed statements. Every wire deduped by transaction ID.
The payer name is run through a map of known entity-vs-legal-name mismatches. Clean hits pass forward; misses fall through to amount-only matching with the uncertainty flagged.
The open-invoice cache is scanned for this customer. One match: picked. Multiple matches: FIFO, oldest unpaid wins. No match: flagged unmatched with full context for review.
A Slack message names the sender, amount, currency, and matched invoice. Ambiguous cases carry an explicit note (“amount matches invoice X but payer name differs”) so the human can verify before recording.
Trust is structural, not promised.
AR work has a low tolerance for surprise. The system is designed so that safety lives in the architecture, not in assurances.
No credential ever reaches the LLM.
API tokens live in a dotenv file. Python handles every API call. A prompt injection in an invoice description or a wire memo cannot exfiltrate a credential. The credential is never in the prompt.
No LLM ever updates the accounting system.
The LLM produces a Slack proposal. Writes to invoice status happen on the next accounting-system sync after the human approves. The LLM literally cannot mark an invoice as paid.
Uncertainty is surfaced, not hidden.
When the payer name does not map, the Slack message says so, in the message itself. Silence on ambiguity erodes trust; explicit flagging builds it.
Every match is traceable.
Every scan logs the cursor advance and the API payload. Every LLM decision logs the reasoning. Every Slack message can be traced backward through the state file to the exact wire that produced it.
The human is always the final judgment layer.
Every match is a proposal. The human reads, acts, or ignores. No autonomous write to money or AR records, ever.
What you give it, what you get back.
- A way to read your incoming transactions from each bank: modern API, SWIFT, MT940 files, or emailed statements
- API access to your cloud accounting system
- A Slack workspace with a finance notifications channel
- A payer-name map for known entity-vs-legal-name mismatches (starts small, grows as aliases are confirmed)
- Real-time Slack notifications on every incoming wire, with the matched invoice
- Explicit uncertainty flags when the match is not clean
- Full cursor-paginated coverage (no missed wires)
- An audit trail deep enough to trace every Slack message back to its API source
This blueprint fits when…
You receive customer payments by bank wire, across one or more banks, whether through a modern API or a legacy file feed.
Payer names on wires don’t always equal the billing entity on your invoices, so someone has to reason about every match.
You often have multiple open invoices per customer and can’t distinguish which got paid without a manual lookup.
Your accounting system (Zoho Books, Xero, QuickBooks Online, NetSuite) exposes open invoices via API.
Your customers pay exclusively by card. No wire matching needed.
You only ever have one open invoice per customer. Nothing to reconcile.
Questions you’re probably asking.
How much does it cost?
It depends on the shape of your stack: how many banks, which accounting system, and whether any feeds need a custom adapter. We walk through pricing on the first call, once we’ve scoped the work. Pilots are fixed-fee; ongoing operation scales with your wire volume. No surprise invoices, no hidden line items.
Which accounting systems does it work with?
Anything with an open-invoice API. Cloud systems like Xero, QuickBooks Online, NetSuite, Zoho Books, and Sage Intacct are native. For on-prem or SAP-style setups, we build a thin adapter during the pilot. If you’re unsure, the fit call will confirm it in five minutes.
What happens when the match is wrong?
Two layers of protection. First, no match is ever auto-posted to your accounting system. Every result is a Slack proposal the human approves. Second, when the sender or amount is ambiguous, the message says so explicitly (“amount matches INV-#### but payer name differs, verify before recording”). Wrong matches, if they happen, stop at the human layer.
What about audit trail and compliance?
Every wire, every LLM decision, and every Slack post is traceable end-to-end. Cursor advances, dedup keys, match reasoning, and exit codes all persist in state files. Credentials never touch the LLM. They live in environment-level storage, read only by Python code. If your auditor asks “why was this invoice flipped to paid?”, the answer is a mechanical trace from the Slack post back to the bank API payload.
How fast is rollout to production?
Typically two weeks from signed scope to live posts in your Slack. Week one: adapter build, payer-name map seeded from your historical wires, and a dry-run against historical data to tune the map. Week two: supervised live operation with your AR manager approving every post. After that it runs on its own rhythm.
Do you need our banking credentials?
Yes, but they never leave your environment or touch the LLM. Credentials live as environment variables in infrastructure you control (your cloud, your VPN, your on-prem) and are only read by the Python scripts that make API calls. If you use an MT940 file feed or emailed statements instead, you can skip API credentials entirely.
Does it handle FX, partial payments, and prepayments?
Yes, with explicit rules. FX wires preserve both sent and received amounts for reconciliation. Partial payments match to the nearest open invoice by customer and leave the invoice open if the amount doesn’t zero it out. Prepayments that don’t match any open invoice are flagged as unmatched with full context, ready for a human decision.
Can we start small?
Yes. The pilot is one bank, one currency, one entity. Once you see it running on a subset of your wires, you decide whether to expand to more banks, more entities, or more currencies. No multi-year commitment to find out if this fits.
Want this on your accounts receivable?
This blueprint is live in production today. If you receive wires, manage multi-entity AR, and have an accounting system with an API, we can scope a version for your operation in a 30-minute call.