Why we bought quote-to-cash.com

How companies charge has changed

In 2019 the companies charging on usage were mostly fintechs taking a percent of transaction volume and a handful of API businesses. Today almost every AI company has either shipped a usage-based SKU, is testing one, or plans to. What changed alongside it is the direction of the money. 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

That puts far more pricing flexibility into the contract than most tooling was built to carry. Billing systems rarely fail on their own. They can only be as accurate as the inputs they receive. The break happens at the handoff, when a deal closes with custom terms and someone has to turn that 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. And when product wants to launch a new pricing model, the constraint is usually whether the billing engine supports the logic. Each of those is a place the record can drift, and the invoice is where the drift 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. That is the part legacy tooling handles badly, because the downstream records were never connected to the upstream ones.

Reading the contract is not the hard part

There is a lot of noise right now 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 extract 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 you have described the problem rather than fixed it.

The same applies to coverage. If your quoting or billing engine handles 90% of your customers, the other 10% of your revenue is living in a spreadsheet, and your revenue recognition is living there with it.

Why 1 data model matters

Most platforms in this category came at quote-to-cash from the quoting side. The problem with starting there is that sales ends up able to quote terms the billing engine cannot service, and finance spends every month closing the gap by hand.

We built the billing engine first and put quoting on top of it. A quote can only contain pricing that can be billed, because both read from the same product catalogue. Custom terms configured at signing can always 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 most complex version we see is second order, where 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 also changes what happens after the invoice. Change an invoice and collections and revenue recognition move with it rather than being rebuilt by hand, and cash matching runs against the schedule the invoice came from rather than 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 that holds the process between them.

Where agents fit in

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

Bring your worst contract

If you want to test it properly, 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 anything. 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 occurs.

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.