Show HN: Chibi, AI that tells you why users churn

[kiranjohns]

Hey HN,

I’ve been a PM for 3 years, and one hard part was always understanding why users churn, drop off and behave the way they do!

Session replays had the answer, but watching hours of them was painful.

I chatted with a bunch of founder friends and PMs and they too had similar troubles.

So I built Chibi an AI that watches replays and tells you what’s broken, confusing, or causing drop-off.

Long Term: I'm thinking if Chibi could evolve into an AI product manager co-worker that can detect and prioritize issues, think through features and even run A/B tests.

Tech Stack: Elixir + Phoenix, rrweb and gemini

Would love to know what you think :)

Happy to answer any questions too

[roncron]

This sounds great in theory. It's been my job to review screen recordings of user sessions and it's often inconclusive.

You can diagnose users not using some features or encountering bugs,, but churn seems out of reach. for example, a champion leaving/being laid off, budget cuts, lack of functionality, etc.

[thsvrrck]

Hey, great idea. Main question that I have: how do you let an LLM determine causality?

[kiranjohns]

Great question! To be clear, the LLM isn't discovering causality. What I do is feed it structured event data and session replays. Then we cross-check those findings across multiple sessions to filter out noise. The advantage here is that it dramatically reduces the time you’d otherwise spend guessing or digging through raw replays.