How ElevenLabs Outgrew Internal Solutions to Turn Customer Feedback into Product Decisions

Executive summary

ElevenLabs is one of the defining names in AI voice, expanding from foundation models into cinto a product millions of consumers and businesses user every day. But a lean team shipping updates every week was drowning in feedback arriving faster than anyone could read it. They tried solving it themselves first, running their reviews and tickets through off-the-shelf LLMs. The summaries were fine. The prioritization wasn't. ElevenLabs brought in Enterpret to unify every feedback channel and connect that qualitative signal to metrics like activation, retention, and conversion, so the whole team worked from one source of customer truth. The result: the app went from $0 to $10M ARR in under six months (top 0.2% of mobile apps for revenue growth), a reworked monetization strategy, and an automated weekly digest that replaced 30 to 45 minutes of Monday prep.

The challenge

You've probably heard an ElevenLabs voice without realizing it. It's openly proud of two things: the quality of what it ships, and that it pays the creators behind the voices.

But rapid growth created a new challenge: how do you keep making the right product decisions when you're shipping every few weeks and customer feedback is arriving faster than any team can read it?

In this case study, ElevenLabs’ Mobile App Product & Growth Lead, Tanmay Jain, shares how the team tried to build its own customer intelligence stack, why it fell short, and how customer feedback became a competitive advantage.

When ElevenLabs set out to grow its flagship mobile app, the team hit a question most never face at that speed: how do you know you're building the right things, and not letting the rest slip, when you ship updates every two to three weeks and a new capability every month?

The team was lean, about three and a half engineers with a single lead, and the feedback was not: hundreds of thousands of reviews, tickets, and comments across app stores, support, and social. One person owned product, marketing, acquisition, and revenue, and couldn't be the funnel every signal ran through.

Where internal solutions fell short for trusted product decisions

Like many AI-native teams, ElevenLabs assumed they could build the answer themselves. They ran a build-versus-buy evaluation using off-the-shelf LLMs against their customer feedback. It worked well enough to summarize reviews, but not well enough to prioritize.

The problem wasn't generating summaries. It was creating a shared understanding of customers that everyone could make decisions from. The question they still couldn't answer was the one that mattered most:

"Where do we actually focus, and why?"

What they needed instead

ElevenLabs brought in Enterpret because the problem wasn't summarizing feedback. It was building one trusted view of customers for product, marketing, and engineering.

The goal was clear:

• Unify every feedback channel, from app store reviews to support to social, at a scale a team this size could never read by hand.
• Get to the "why" behind the metrics, putting qualitative feedback on top of signals like activation and retention.
• Make it part of the daily work, so customer truth lived where decisions got made.

Enterpret unified feedback from every customer touchpoint, from app store reviews and support tickets to social conversations, and connected those qualitative signals to metrics like activation, retention, and conversion. Instead of chasing feedback across disconnected tools, the team could finally understand why the numbers were changing.

Just as importantly, customer context stopped living with one person. It became part of the team's daily workflow, giving every user the same starting point every time they needed to make a decision.

That shared understanding started showing up across the product, but nowhere was it more visible than monetization.

How ElevenLabs uses Enterpret

Unified customer context changed the monetization strategy

ElevenLabs already had pricing that worked, but it was hitting unit-economics walls. The obvious read was that users hated that everything burned credits, and support heard the same. So the team made consumption clearer, and growth took a hit. They did it again. They were stuck in a loop.

The surprise came when they stopped trusting the surface. The real driver wasn't price at all.

"People hate spending on things they think they could get for free. But past that, the real issue was clarity: users didn't understand why or when a credit is consumed, how it maps to a bit of voice, video, or image, or how to control it."

Tanmay Jain, Product & Growth, ElevenLabs

That reframe set off small, precise changes: they swapped the always-on credit counter for a warning that fires only above 25% of a balance, and made clear that regenerations don't burn credits. And when the data surfaced a segment of mostly Android users across Southeast Asia, Africa, and Latin America who didn't want a subscription at all, the team started offering one-time credit packs for them rather than write the revenue off.

Making customer context operational

What makes Enterpret stick is where the insight lives. Enterpret sits inside the team's Slack. Ask why conversion dropped in a country and someone tags Enterpret in the thread, pulling the answer and the numbers together, instead of three teams chasing it through Facebook Ads Manager, a Grafana dashboard, and each other.

It goes deeper than chat: the team runs Enterpret headless over its MCP server, and every Monday an automated job drops a digest into Slack that sets feedback next to the analytics with a root-cause voiceover, saving 30 to 45 minutes of kickoff prep.

The impact

• The mobile app went from $0 to $10M ARR in under six months, in the top 0.2% of mobile apps for revenue growth
• Grew from about three and a half engineers to a six-engineer, one-designer team, with no loss of small-team speed
• Turned a stalled credits strategy into a feedback-to-revenue loop, including a new one-time credit pack option for a segment they'd nearly written off
• From 30 to 45 minutes of manual kickoff prep to a fully automated weekly digest

What's next

These changes did more than fix a paywall. They changed what customer intelligence is for at ElevenLabs. It used to run through one person. Now it's the shared context that keeps a fast team from tripping over itself: at this pace, without it, product, marketing, and engineering drift apart within weeks, each on its own version of the truth.

That's the kind of product team this points to: a small crew managing a set of agents, with a live read on customers feeding the whole stack. And it's worth remembering where it started: three and a half engineers, buried in reviews, who decided the voice of the customer was too important to leave in one person's inbox.

If you're a small team shipping fast and flying half blind on feedback, this is what one shared source of customer truth looks like. Talk to our team.

‍