Gamma generates viral conversion growth with warehouse-native experiments for AI features

Gamma is an AI-powered platform that helps users quickly turn ideas into polished presentations, websites, and other digital content. Co-founded by Jon Noronha, James Fox, and Grant Lee, Gamma helps users bring their ideas to life in presentations through intuitive, AI-native tools. The company operates with a lean team of around 30 employees and competes in a space dominated by industry giants. Gamma differentiates itself by turning prompts and ideas into impressive visual outputs, without the manual effort required by traditional tools.

Gamma is an AI-native startup that continuously tests new generative models as they’re released and rapidly integrates the best ones into the product. However, these models are unpredictable, variable in cost, and not always production-ready. Gamma needed to safely experiment with new ideas and technologies without compromising product quality or user trust.

The team also wanted to involve more roles and departments in experimentation. Instead of limiting tests to a few data scientists or engineers, they envisioned a culture where everyone, from product managers to designers, could validate ideas with real users.

By integrating LaunchDarkly Experimentation with their Snowflake AI Data Cloud, Gamma built an experimentation engine capable of evaluating model performance across multiple dimensions: latency, cost, user retention, satisfaction, and downstream business metrics. LaunchDarkly became the control center, enabling small-scale rollouts, real-time model comparisons, and confident fallbacks if something went wrong. With this foundation, Gamma could architect and reinforce its budding experimentation culture.

One early example was the use of AI-generated images. Initially, the team hesitated to introduce image generation due to quality concerns that are common with AI-generated images, such as distorted hands or illegible text. However, by testing it on a small cohort of users, they discovered that the feature outperformed traditional stock photos in both user satisfaction and retention.

Beyond model performance, Gamma uses LaunchDarkly to test various prompts, feature configurations, and UI variations. Because experiments are embedded in the product and tied directly to Snowflake data, the team can analyze outcomes like activation, upgrade rate, and feature engagement.

Gamma now runs experiments on an average of 20–25 AI models at any given time, testing competing models for cost, quality, and user impact.

For example, the team tested a new model (Claude 3 Haiku) against their existing setup to improve user satisfaction on longer, more complex prompts that generated 50% negative and 50% positive feedback. The test revealed a 30% increase in user satisfaction, which translated into a 20% lift in free-to-paid conversions after the feature was fully launched.

And the results go beyond technical wins. LaunchDarkly has helped Gamma scale its AI capabilities across the organization. Engineers and designers who are not AI specialists can build and test new features without waiting on a centralized team:

Safely testing and learning through experiments is naturally aligned with Gamma’s scrappy startup mentality. Instead of discussing what might work, the team now builds, ships, and tests. The result is a faster feedback loop, stronger user engagement, and a product team that keeps finding new ways to engage customers.

Today, Gamma continues to punch above its weight in a highly competitive category—not by having more resources, but by making every idea testable and every decision data-driven.