How KRNL uses RevenueCat to focus its AI app studio on what’s worth scaling

Key Outcomes

• Made RevenueCat the monetization source of truth across 7 apps
• Centralized revenue data across App Store, Google Play, and Stripe
• Scaled One More Shot AI to 710k monthly run rate in a year
• Used subscription retention, cohorts, payback time, pricing, and LTV to guide investment decisions
• Kept engineering focused on product and growth, not payment infrastructure

Building an AI app studio around focus

KRNL started in 2018 with Loopsie, an AI photo and video app built years before AI became the center of consumer software. Since then, the Milan-based team has grown into an app studio, launching products across photo editing, video creation and music.

The studio model only works if the team can spot signal quickly. KRNL needs to understand which products deserve more focus, which channels deserve more budget, and which users are likely to create durable value.

KRNL's current focus is One More Shot AI, a music video generator that has grown into a full content platform for music artists. Artists bring their music and create lip-synced music videos, lyric videos, album covers, social assets and promotional content.

After a year, Angelo says One More Shot AI reached 710k monthly run rate. For KRNL, getting there meant staying focused on the product and user niche showing the strongest signals.

“Right now, RevenueCat is the center. It’s the source of truth for our monetization.” — Angelo Gallarello

The cost of unclear revenue data

In a portfolio business, unclear revenue data can send the team toward the wrong app, the wrong campaign, or the wrong pricing model.

KRNL felt that pain in the early Loopsie days. The team was trying to understand revenue across platforms, track trial conversion, and optimize paid acquisition. Without a clean monetization layer, even simple questions were hard to answer with confidence.

That mattered because KRNL was bootstrapped and moving quickly. Revenue data shaped how much the team could spend, how fast it could scale, and when it should keep pushing on a product or move on.

“If your metrics about revenue are wrong, you’re basically going blind. At that point, you can even ignore the metrics and wait for the wire transfer at the end of the month.” — Angelo Gallarello

A monetization foundation for every new bet

KRNL adopted RevenueCat early and has kept it as a standard part of its app launch process. Today, Angelo says the company uses RevenueCat across all 7 apps.

“The first thing we usually do is build the app, add RevenueCat so that we know how to track the most important metrics, the revenue ones, and then we go on with the project.” — Angelo Gallarello

RevenueCat also removes work KRNL does not want to own every time it starts a new product. The team does not have to rebuild the same purchase infrastructure, maintain Apple server notifications, connect Stripe webhooks from scratch, or keep up with every platform change around proceeds, taxes, and payloads.

That gives KRNL a trusted foundation while the team focuses on the work that differentiates each app: the user experience, the creative output, the pricing model, and the growth strategy.

Turning monetization data into operating decisions

The value showed up first in acquisition. In the early days, Angelo remembers sending trial events as purchases because the team did not have a good server-to-server way to track trial conversion. Once RevenueCat was in place, KRNL could send the right events with the right numbers.

“When we started using RevenueCat, we started having the right events and sending the right events. Back then, the campaigns on Facebook changed. That was the moment where we were convinced of the value of RevenueCat.” — Angelo Gallarello

The same principle still applies. RevenueCat events, webhooks, and charts help KRNL understand cohorts, subscription retention, payback time, pricing, cash flow, and overall LTV. Those metrics influence how KRNL allocates spend, which products get attention, and which apps should be wound down.

“We used this to understand which app was doing better, which would make sense to keep working on, and which would make sense to kill. Without these numbers, it would have been difficult to make these decisions.” — Angelo Gallarello

Scaling One More Shot AI with trusted revenue signals

One More Shot AI gave KRNL a clear signal that something was different. Compared with earlier apps, Angelo saw stronger retention, stickier usage, and a clearer user persona around music artists creating promotional content.

The product also monetizes beyond a single subscription. Users subscribe for access and can buy additional packages when they want to keep creating more videos, covers, images, or edits. The important question is not only whether users subscribe. It is whether they keep using the product enough to come back and spend again.

RevenueCat helps KRNL see that behavior in the revenue curve. Angelo looks for a pattern where revenue drops after the first month, then rises again as engaged users return, create more, and buy additional packages.

“There you really understand that what you’re building is not just a subscription generator, but a product that people want to keep using. The more they use the product, the more they find it useful, and the more they start spending.” — Angelo Gallarello

RevenueCat gives KRNL the monetization confidence to keep building where the data is strongest. From Loopsie to One More Shot AI, that foundation helps the team stay focused on the products, channels, and users worth scaling.