There's a new CRM in town that doesn't charge for the CRM.

No seat fees. No record limits. You pay when their AI does work for you.

The founder behind it, Patrick Thompson, previously led design for Jira at Atlassian and left to co-found Iteratively with Ondrej Hrebicek, a customer data platform. They sold to Amplitude in 2021 and decided to team up again with Clarify in 2024.

Front-row seat to the same problem: fast-growing companies have fast-growing headcount. More activities mean more data, and it spirals fast. Plus, that mess comes with a huge bill: 3–6 months of migration, configuration, and integration before a CRM delivers any value - despite the 100-seat, $10,000/mo charge starting day one.

Here’s the strategy behind one of my favourite examples of an aligned AI pricing model: outcome-based pricing.

Enjoy.

— Tom

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The what: A TLDR explanation of the strategy

The $100B CRM market is the deeply entrenched crown jewel of SaaS. Major players have locked in the logic of seat-based pricing so firmly that almost nobody questions it.

But when AI agents can do the work, the ‘pay-per-human-seat’ breaks.

Clarify is the next-gen CRM that’s giving their product away for free.

The CRM itself costs nothing except their AI. Anytime their AI agent does work for you, you pay for it in ‘credits’.

In the early days of Atlassian, Patrick saw their legacy stack buckle under the weight of their PLG motion. Existing solutions weren’t designed to handle the volume of data being created and quickly became unusable.

So at Iteratively, they built a Customer Data Platform (CDP) to wrangle messy user data, billing, and other disparate data sources into CRMs, MAPs, and DWHs.

It was a successful product with an exit to Amplitude, but he could still never shake the feeling that CRMs were never architected to handle the full picture of a customer.

Most CRMs are relational databases with a pretty interface. They store contact names, deal stages, last email sent.

But they struggle to ingest signals that actually tells you whether a customer is about to churn, ready to expand, or silently delighted.

Clarify is different. It natively ingests emails and call recordings, but its infrastructure is also flexible enough to support event data, product usage, Stripe billing, marketing analytics, etc. into a single context layer.

That data infrastructure enables the AI to do all sorts of useful work, which is what makes their “free” pricing model possible.

When you've got the full customer picture in one place, you can actually build AI agents that move deals forward, detect buying intent, update pipelines, and run personalised sequences.

Work worth paying for.

The how: The strategic playbook boiled down to 3x key takeaways

The CRM market's dirty secret is that the category is what you get when you start with a spreadsheet and iterate for 20 years. Relational data only. Contact name, company, deal stage, last activity.

This means companies with any serious product usage build “Frankenstacks”.

Data gets ETL’d into a warehouse → Joined with website behaviour from whatever analytics tool → Materialised by a data team into views → Then reverse ETL'd back into the go-to-market tools.

It’s a lot of time/money spent on plumbing.

Clarify built a platform that handles:

Put it together and the customer picture is a living, breathing canvas and not just a slice of JSON.

That distinction matters enormously when you're trying to build AI agents on top. If your CRM only sees one slice of the customer, your agents only have one slice to reason from.

The "fallacy of SaaS", as Thompson calls it, is that you pay for users whether they use the product or not.

Most CRM’s charge seat-based fees that scale with your headcount. Regardless of adoption, regardless of value delivered.

Clarify inverts the model.

The CRM is free. No seat limits. No record limits. No call recording minutes.

You sign up, you use it, you pay nothing for the platform itself.

The meter only starts running when their AI agents do work for you. AI Credit usage revolves around:

→ Meeting intelligence - Briefings, summaries, and follow-ups

→ Deal intelligence - Deal summaries, detection, creation, and field updates

→ Record intelligence - AI field generation and summarisation

→ Prospecting - Email creation and sending (lead import coming soon)

This pricing structure tackles 2 important strategic objectives:

Credit-based usage is the stepping stone toward a model where you'd pay per deal influenced or per meeting booked. Likely fairest version offered today while the market figures out how to price AI-driven outcomes.

There's a term in startup circles, popularised by Parker Conrad of Rippling, called the "compound startup."

Basically instead of building a point solution, you build a core platform with a defensible unit of work at its centre, then expand outward with product spokes.

Some examples:

Clarify’s customer record is what connects the deal with the email thread, the call recording, the product usage event, the Stripe transaction, the LinkedIn interaction.

Every GTM motion sales, marketing, customer success, support orbits around it.

Build the best customer record infrastructure in the market and you have a platform you can launch spokes off indefinitely.

The benefit of the compound startup model is that new spokes require roughly 20% net-new product work. The other 80% (data layer, agent infrastructure, enrichment database, MCP connections) is already built.

More spokes, more surface area for AI to do useful work. Which means more value delivered to the customer, which means more revenue for Clarify.

Fully aligned incentives.

The where: 3x high-signal resources to learn more

Patrick writes about company-building with more candour than most founders dare.

If you want the unfiltered operator perspective behind everything in today's breakdown, this is where he puts it.

Madrona's investment memo is a structural thesis: Why bolt-on AI can't overcome a brittle legacy architecture.

The competitive landscape framing is fascinating too.

Brendan from The Signal spent time with the latest Clarify release and breaks down:

This is the piece that made Clarify’s AI pov really click for me.

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