Why we bought quote-to-cash.com

How companies charge has changed

In 2021, the only companies charging on usage were mostly fintechs taking a % of transaction volume and a handful of API businesses. Today almost every AI or SaaS company has either shipped a usage-based SKU or is planning to roll it out. What changed alongside it is the direction of the money and that the marginal cost of software is no longer 0. Because of the cost profile of building on top of LLMs, these companies are charging in advance, selling credits upfront that draw down over the term rather than billing monthly in arrears.

Where quote-to-cash breaks

All of that flexibility ends up in the contract, and most tooling was never built to carry it. Billing systems rarely fail on their own. They can only be as accurate as what gets handed to them. The break happens at the handoff, when a deal closes with custom terms and someone has to turn those into a billing schedule.

The handoff everyone talks about is sales to finance. It is not the only one. Onboarding and CS hold context finance never sees, so a customer gets invoiced and chased while their implementation is stalled. Data and engineering own the meter the usage charge depends on. RevOps run deal desk and reporting off the same contracts. When product wants to launch a new pricing model, the question is whether the billing engine supports the logic. Each one is a place the record can drift, and the invoice is where it shows up.

The happy path is fine. A quote goes out, the contract is signed, a schedule is created, the invoice is generated. The question is what happens when something changes. One of our customers had to reissue 23 invoices, which on its own was not a major task. They then had to issue 23 credit memos, their AR reporting changed and their revenue recognition changed with it. Legacy tooling handles that badly because the records downstream were never connected to the ones upstream.

Reading the contract is not the hard part

Everyone is talking about reading a contract with AI and generating a billing schedule off the back of it. We built AI contract intake because customers asked for it, and it works because it reads against the product catalogue. On its own it does not solve much. You can pull out a 3 year agreement with a 4 month free trial, 10% off in year 1 and milestone billing in year 2, and if the billing engine underneath cannot hold those terms, all you have done is describe the problem.

Coverage is the other one. If your quoting or billing engine handles 90% of your customers, the other 10% of your revenue is sitting in a spreadsheet, and your revenue recognition is sitting there with it.

Why 1 data model matters

Most platforms came at quote-to-cash from the quoting side. Start there and sales can quote terms the billing engine cannot service, so finance closes the gap by hand every month.

We built the billing engine first and put quoting on top of it. A quote can only hold pricing that can be billed, because both read the same product catalogue. Whatever gets agreed at signing can be billed against, from the CRM through to the ERP.

Credits, top-ups and drawdown on 1 contract

A single contract can commit a customer to a package of credits for the year, take a mid-year top-up when they run out, and draw down against usage the whole time. That is a subscription, a one-time charge and a usage product on one agreement. Model them in separate systems and reconciliation stays manual forever. The hardest version we see is second order. Tiered unit pricing sits under a monthly minimum commitment, and the discount on those tiers moves with the resolution rate of the customer's agent.

What changes when the records connect

Kapa closed the gap between a deal being signed and the billing schedule existing, which had been costing them $56,000 a year in revenue leakage. Raspberry AI runs hundreds of B2B contracts with 1 person in finance. Attention charges seat upgrades to the card on file the day they happen and recovered 8% in receivables.

Holding the entire workflow changes what happens after the invoice too. Change an invoice and the collections and revenue recognition workflows move with it instead of being rebuilt by hand. Cash matching runs against the schedule the invoice came from, not a bank statement and a guess.

The stack most finance and RevOps teams are moving toward is 3 systems. The CRM, the ERP, and the quote-to-cash layer between them.

Where agents fit in

None of that is an AI feature. It is what you get when the quote, the invoice and the revenue schedule read the same record. Agents take on the manual work around it and the calculation stays deterministic, with the same inputs and same output every time, traceable back to the clause it came from.

Bring your worst contract

The best way to test it is to book a demo and bring your worst contract. Edge cases are what trip up quote-to-cash, so we would rather run your ramps, minimums and drawdowns in a sandbox before you buy. The docs are open if you want to read first.

FAQ

What is quote-to-cash?

Quote-to-cash is every step between a price being agreed with a customer and the revenue being collected and recognised. That covers quoting, contracting, invoicing, usage metering, payment collection and revenue recognition.

How is that different from CPQ?

CPQ is the quoting half. It configures pricing and produces the order form. Quote-to-cash includes everything that has to happen after signature, which is where most revenue leakage happens.

Where does Sequence sit relative to a CRM and an ERP?

Between them. Deals close in your CRM, Sequence handles pricing, billing, metering and revenue recognition, and the output syncs to your ERP through our connectors. Sequence replaces neither.

Can Sequence handle credit and usage-based pricing?

Yes. Credits sold upfront, mid-term top-ups, drawdown against usage, tiered rates, minimum commitments and prorated upgrades can all sit on the same contract.

Why does the data model matter for AI agents in finance?

An agent can only act on what it can read. When quoting, billing and revenue recognition each hold their own version of a contract, an agent has no single record to work from. On 1 data model it does, and through Sequence MCP a finance team can query and act on that record from the tools they already use, with every action logged before anything runs.