AI order to cash automation, verified

Order to cash is six or seven handoffs long and every one of them can quietly cost money. A purchase order arrives by email with a SKU that no longer exists. A credit hold is released by someone who could not see the customer's total exposure. An invoice goes out at list price instead of the contracted price. A payment lands with no remittance detail and sits in a suspense account for five weeks. An LLM can work every one of these steps, and it will. It will also apply last year's price list with total confidence, and the first sign of trouble will be a customer's AP team disputing an invoice you already recognized as revenue.

Pebble builds order to cash as a verified environment: the cycle mapped into numbered steps, each step with its own verification check, run by an AI model inside that structure. The model does the work. The checks decide whether the work counts.

How does a verified environment run order to cash?

It walks numbered steps, and each step must pass its check before the run moves on. Here is a plausible step map for a B2B order to cash cycle, with the check type at each step.

Step 1: Capture and normalize the order. The run ingests the order from wherever it came - a PDF purchase order, an email body, an EDI 850, a portal export - and produces structured lines: customer, ship-to, SKU, quantity, unit price, terms, requested date. Checks: computed and classified together. Computed confirms every SKU resolves to an active item, that the ship-to is a valid address for that customer, and that each unit price matches the price list or contract in force on the order date. Classified catches what code cannot read - a smudged quantity, a free-text item description that maps to two possible SKUs. A line that fails either check does not become an order. It fails the step.

Step 2: Make the credit decision. The run computes the customer's current exposure, compares it to the credit limit, and either releases, holds, or escalates. Checks: computed, classified, and judged, split deliberately. Computed does the exposure arithmetic - open invoices plus this order plus in-transit, against the limit, with the aging profile attached. Classified handles a thin or new file where the decision leans on inference rather than balances, scored against your historical credit outcomes on held-out accounts. Judged covers the override: if the run recommends releasing a held order, the written rationale is graded against examples your credit team annotated, and where judge-human agreement is too low the credit manager stays in the check.

Step 3: Confirm and release the order. Availability, promised date, incoterms, and payment terms are set and the order is released to fulfillment. Check: computed. Code confirms the promised date is consistent with available-to-promise, that the terms written on the order are the terms on the customer master, and that nothing was released while a hold was still open.

Step 4: Invoice. The run produces the invoice from the fulfilled quantities. Check: computed, the gold standard. Do the invoice lines equal the fulfilled lines. Does the invoice total equal quantity times contracted price plus freight and tax, with the right discount applied. Does the tax jurisdiction resolve and the rate match it. Does the invoice validate against the e-invoicing format the customer's country requires. That last one has a deadline attached: the EU adopted the VAT in the Digital Age package on 11 March 2025, under which structured e-invoicing becomes the default and is mandatory for transactions inside the Digital Reporting Requirement from 1 July 2030. Schema validity stops being a nice-to-have and becomes a computed check that either passes or does not.

Step 5: Apply cash. Incoming payments are matched to open invoices. Check: computed. Does the remittance total equal the sum of the invoices it is applied to, to the cent. Is every applied invoice open and unpaid. Where a payment is short, the run codes the reason - deduction, credit memo, pricing dispute, freight claim - and that coding is a classified check, trained on your historical deduction outcomes and scored against held-out examples. This is the same three-way matching discipline as invoice reconciliation, pointed at receivables instead of payables.

Step 6: Collect and resolve disputes. Aged items get a dunning action, and disputes get a written response. Checks: computed for routing and eligibility, code confirms the account is genuinely past due by your policy, that no dunning goes out against a disputed or credit-held item, and that escalation follows the aging matrix. The customer-facing message is judged: your collections team annotates pass and fail examples of an acceptable message, Pebble trains a judge on them, and reports judge-human agreement on held-outs. Anything involving a threat to service, a legal step, or a write-off keeps a human in the check permanently.

What happens when a step fails?

The run stops at the failed step, gets fixed, and resumes at that step, not from scratch. If a unit price at step 1 does not match the contract in force, the run halts there. Someone confirms the correct price, the line is corrected, and the run resumes at step 1. The credit decision, the invoice, and the cash application never operated on the wrong price, because the wrong price never got past its check.

Compare that to the unverified version, where a mispriced line becomes an invoice, becomes recognized revenue, becomes a dispute, becomes a credit memo, and becomes a reconciliation someone does by hand in the following quarter. In a verified environment, when something goes wrong you get a step number. "Order 71903 failed at step 1, contract price mismatch on line 4" is a sentence a revenue operations lead can act on before the order ships. What makes a check trustworthy enough for a controller to sign off on is set out in approved checks.

What does finance actually get?

A run record per order: every step, every check result, every override, every human approval, in order. Three things follow from that. Internal controls stop being a narrative, because the control is the check and the evidence is the record. Days sales outstanding becomes diagnosable, because you can see which step the cycle stalls at rather than which team is blamed for it. And an auditor asking how a specific invoice was produced gets the record instead of a reconstruction.

Reliability also becomes a percentage read off run history - this many orders, this many clean runs, failures concentrated at these steps. That is what lets you decide where a human should stay in the loop based on measured failure rates rather than on nervousness.

What do you own afterward?

A dataset of your order to cash runs, and eventually an open-source model post-trained on it that belongs to you. Every completed run is a worked example of your cycle done correctly, with verified checkpoints at each step. Once the history is deep enough, that dataset post-trains a model you own, running the cycle at a fraction of frontier API cost per order, that no one can reprice or deprecate out from under you. When a stronger open base ships, the same run records train its replacement.

How does an engagement start?

With a scoping call, and if an environment is the wrong tool for your order to cash process, Pebble says so on the call. From there: about a week of discovery inside your systems under your access model - Pebble keeps the step map and none of your data - ending in a written proposal with a fixed price per work package. Then the build, exercised against real orders before it is called done. Then it runs, on your infrastructure or Pebble's, with a retainer.

Order to cash is one process. The same structure applies to claims processing in insurance and to customer onboarding in compliance.

Ready to see your order to cash cycle as a step map? Book a scoping call: [email protected].