How we're making Spark smarter, with AI
The AI opportunity in experimentation
Every experiment generates data. But most teams only scratch the surface of what that data can tell them. We saw an opportunity to use AI to help teams learn faster from every experiment they run.
What we built
We've integrated AI across the Spark platform in three key areas:
Smart experiment design
Our AI analyzes your historical experiment data and suggests:
- Optimal sample sizes
- The best metrics to track
- Potential interaction effects with other running experiments
Automated analysis
Instead of waiting for a data scientist to analyze results, Spark's AI:
- Detects statistically significant results earlier
- Identifies unexpected metric movements
- Generates plain-English summaries of what happened and why
Predictive insights
The most exciting part — our AI can now predict:
- Which features are likely to win before the experiment concludes
- What the long-term impact of a change might be
- Where the biggest opportunities for improvement lie
The technical challenges
Building AI into a real-time experimentation platform isn't straightforward. We had to solve several hard problems:
- Latency: AI inference can't slow down feature flag evaluation
- Accuracy: Wrong predictions are worse than no predictions
- Privacy: We never train on individual user data
Results so far
Early adopters are seeing 40% faster experiment cycles and 25% more experiments reaching statistical significance. That translates directly to faster product iteration.
What's next
We're just getting started. In the coming months, we'll be rolling out AI-powered experiment recommendations and automated experiment pipelines.