How Clarify replaced its recorder and grew revenue 9x with Recall.ai

Executive Summary

Challenge

Clarify is building an autonomous CRM designed to eliminate the manual work that traditionally comes with managing customer relationships. Meeting data is central to that vision: conversations contain pricing, customer requirements, product feedback, deal context, and other information that would otherwise have to be entered into a CRM manually.

Clarify initially tried building parts of its meeting recording infrastructure itself, including a desktop recorder created during a hackathon. The solution worked, but it was brittle, difficult to maintain, and generated support escalations. Meeting detection, audio quality, transcription, and changes from providers such as Microsoft Teams all created engineering work.

Building a production-grade version internally would have required continued investment, along with engineers and support resources dedicated to maintaining it.

Solution

Clarify uses Recall.ai for both meeting-bot recording and desktop recording.

Recall.ai provides the capture infrastructure that feeds meeting data into Clarify, while Clarify focuses on turning that data into CRM intelligence: meeting summaries and briefings, automated CRM updates, follow-up, reporting, permissions, and agentic workflows.

When Recall.ai released its Desktop Recording SDK, Clarify replaced its homegrown desktop recorder. Because the underlying Recall.ai integration was already in place, a working desktop implementation took only a few hours to get running.

Results

By building on Recall.ai, Clarify:

• Moved its meeting recording infrastructure from active development to largely maintenance mode, freeing engineering capacity for its core product
• Got its first Recall.ai proof of concept into market in weeks instead of spending months building the infrastructure internally
• Replaced a brittle homegrown desktop recorder rather than dedicating engineers to rebuilding and maintaining it
• Added desktop recording capabilities that helped remove deal blockers for customers who did not want bots joining their calls
• Made its desktop application a more important part of the product, with roughly half of weekly active users using the desktop app
• Uses meeting data to power workflows across meeting prep, follow-up, CRM updates, sales automation, and post-sale use cases
• Supported hundreds of customers while Clarify scaled rapidly, with Recall.ai helping the company keep engineering focused on revenue-driving product work

Meeting data is foundational to Clarify’s product

Clarify is an autonomous CRM built around a simple idea: much of the information sales teams manually enter into a CRM already exists somewhere else.

Meetings are one of the richest sources.

A customer might discuss pricing, requirements, objections, next steps, or product feedback during a conversation. Historically, a salesperson would have to remember that information, take notes, and later decide what belonged in the CRM.

Clarify reverses that workflow. It captures the conversation, extracts the relevant information, and uses that context to update the CRM automatically.

“Meetings are probably one of the most critical parts of the data that we capture at Clarify,” said Patrick Thompson, co-founder and CEO of Clarify.

Senior Staff Engineer John Jiang described meeting data as an unstructured source of information that can be transformed into structured product value. Instead of asking users to repeatedly enter information that has already been discussed, Clarify can extract it directly from the conversation.

That creates value far beyond meeting summaries.

Once Clarify has a history of customer conversations, it can use that context for meeting briefings, follow-up, CRM field updates, performance analysis, reporting, and other workflows.

“Clarify doesn't exist without meeting data,” Thompson said. “It's effectively what powers our entire product.”

Clarify learned firsthand what it takes to build meeting recording infrastructure

Clarify did not arrive at its build-versus-buy decision for desktop recording theoretically.

The team had already built its own desktop recording functionality during a hackathon.

The prototype demonstrated what was possible, but turning it into reliable production infrastructure created an entirely different engineering problem.

Clarify encountered issues with meeting detection, audio quality, transcription, and recordings that did not start correctly. In the most serious cases, that could mean lost meeting data. The team also had to account for changes from meeting providers. A Microsoft Teams release, for example, broke Clarify’s integration.

“It was brittle, it was hard to maintain, and actually caused a lot more support escalations,” Thompson said.

The problem was not getting a recorder to work once. It was owning everything required to keep it working across different providers and environments over time.

Jiang described it as a “whack-a-mole problem”: every additional meeting environment and provider change created another surface Clarify needed to maintain.

When Recall.ai released its Desktop Recording SDK, the decision was straightforward.

Recall.ai moved meeting recording from a product initiative to infrastructure Clarify can rely on

Before Recall.ai, meeting recording required meaningful engineering capacity.

Clarify had engineers working across its bot recorder and desktop recording functionality. After adopting Recall.ai, neither area requires a full-time engineer.

“Literally everything else that we're working on is because of the fact that we can outsource the complicated nature of meeting recordings to Recall.ai,” Jiang said.

That changes what the team can prioritize.

Instead of investing engineering time in meeting-provider integrations, Clarify can work on permissions, reporting, onboarding, and the user-facing experience around meeting data.

It can focus on questions such as how transcripts should be presented, how recordings should be shared, and how captured information should feed the rest of the CRM.

The distinction matters for an early-stage company.

Meeting capture is essential to Clarify, but Clarify does not see the underlying recording infrastructure as its competitive advantage.

“We didn't think this was a core competency, nor did we think that we could do it better than Recall,” Thompson said. He compared the decision to using providers such as AWS or OpenAI rather than building foundational infrastructure internally.

For Jiang, the alternative would have meant “months, quarters, maybe years of investment” plus ongoing engineering and support resources.

The Desktop Recording SDK replaced homegrown infrastructure in hours, not months

Recall.ai also changed the economics of adding desktop recording to Clarify.

Jiang said Clarify’s initial Recall.ai implementation took roughly two weeks, but would have taken far less time with the Recall.ai MCP. Once that foundation existed, getting the Desktop Recording SDK running took only a few hours.

He was able to build a demo and ship a version quickly rather than beginning another desktop infrastructure project from scratch.

That speed has continued after implementation.

Maintaining the desktop integration is now largely a matter of incorporating Recall.ai updates. Jiang estimated that when the Recall.ai team announces a new desktop release, he can update Clarify’s application and cut a new release in around ten minutes rather than spending his time diagnosing why an individual meeting failed to record.

For Clarify, that is the startup ROI of buying infrastructure instead of owning it: the team can get the capability into customers’ hands quickly, then keep allocating its limited engineering capacity to the product experiences that differentiate the company.

Thompson said Clarify was able to get its first proof of concept out “in a matter of weeks,” versus the months it expected an internal build to require, while getting direct engineering support from Recall.ai when it needed help getting unblocked.

Desktop recording became a deal-closing feature

The Desktop Recording SDK did more than reduce engineering work.

It opened Clarify to customers for whom a meeting bot was not the right experience.

Clarify works with customers including venture capital firms and financial advisors who may want to capture client conversations without adding a visible bot to the meeting. Desktop recording gives those users another way to capture the conversation directly through Clarify’s application.

In some cases, that capability directly affected purchasing decisions.

“We definitely had customers where it was a deal breaker to not have desktop recording functionality,” Jiang said. “This was a deal-closing feature that we had to implement.”

It also gave users a stronger reason to install Clarify’s existing desktop application.

Before local recording, Thompson described the desktop app as a “nice-to-have.” With recording integrated, it became a much more important part of the product.

About half of Clarify’s weekly active users use the desktop application, and Clarify believes the local call recorder is one of the largest drivers of that adoption.

Meeting intelligence helps Clarify deliver more value without adding another point solution

The business impact of Recall.ai extends beyond the recording feature itself.

Clarify has bundled meeting intelligence directly into its CRM instead of requiring customers to assemble separate systems for CRM, call recording, and downstream automation.

The captured meeting data powers meeting briefings, follow-up, deal-stage and field updates, personalized campaigns, and agentic workflows across both pre-sale and post-sale use cases.
That has become increasingly important as integrated meeting intelligence has shifted from differentiation toward a capability customers expect.

Thompson said Clarify was among the first CRMs to offer an integrated call recorder, but the category quickly became table stakes. Having Recall.ai underneath that capability allows Clarify to keep meeting intelligence inside its broader product while investing its own engineering effort in areas such as prospecting, managed agents, marketing automation, and sales automation.

Meeting data also gives Clarify the context required to make those systems useful.

For early-stage customers, Clarify can analyze customer conversations for product feedback, churn signals, deal likelihood, forecasts, and next actions. That turns meeting capture from a recording feature into a source of structured business intelligence.

Recall.ai helps Clarify keep engineering focused on growth

Clarify has grown to hundreds of customers, and Thompson said the company was on track for 9x revenue growth this year. He described Recall.ai as infrastructure that helps unblock revenue by allowing Clarify to offer integrated meeting intelligence while concentrating its resources on the rest of its product.

For an early-stage company, that opportunity cost is significant.

Clarify can “rent” meeting infrastructure at what Thompson described as a fraction of the cost of building it internally. Engineering resources can then go toward improving the autonomous CRM, getting new capabilities to market, and delivering the product experiences that help Clarify win and retain customers.

That is ultimately how Clarify evaluates the build-versus-buy decision.

The company already proved that it could build its own recorder. It also learned how much work would be required to turn that recorder into infrastructure it could depend on.

Rather than continue investing in a capability another company specialized in, Clarify chose to put its engineers where they could create the most differentiated value.

“If another startup asked me why they should partner with Recall.ai versus just going and building their own solution, I'd lean back on the experience that we had,” Thompson said. “We had a solution in market, we had lots of customers using our call recorder, and we decided to actually rip it out and replace it with Recall.ai.”

For Clarify, Recall.ai is not simply a faster way to add recording.

It is the infrastructure that lets the company treat meeting data as a foundational part of its autonomous CRM without making meeting recording infrastructure the thing its engineers have to spend their time building.