Your users are already telling you what to build.
Understand why users stay, why they leave, and what they wish your AI could do next.
Otis listens to conversations and combines them with real user behavior to surface the improvements that matter most.
Here’s what they’re telling you.
They stopped trusting it.
Your logs show the drop-off. They don’t show what these users said on the way out.
“did it actually send all of them?”See the tasks they took back
@Otis which tasks do they drop first?
On exactly the tasks users were already checking by hand.
They accepted it, then rewrote it.
Your logs already show the 17%. They don’t show that most of those edits are users making one identical change.
“shorter, and drop the intro”See the correction
@Otis so it’s one prompt change, not a model problem?
One line in the system prompt closed most of the gap.
They asked the product, not you.
None of this reached your feedback inbox. They asked the copilot, got told no, and worked around it.
“Can you just do this for all of them?”See the accounts
@Otis none of these came through support?
Not because anyone filed a ticket. Because 37 accounts asked your product directly.
What you can’t see is shaping your product.
Six signals that never look like signals.
Your stack can tell you what ran. It can’t tell what delivered value.
That gap is where value is quietly won and lost.
Otis turns signal into ground truth.
Add Otis from your coding agent in minutes. First insights in hours.
# Claude Code or Cursor: npx @runotis/setup, then /otis-analyzeYour coding agent installs a light SDK and instruments your product surfaces; you review and merge. It runs async, so zero latency for your users, and there’s nothing to label or define. A short strategy onboarding, and you’re live. About 30 minutes.
The @runotis packages are private today: you’ll get access when you come on board. See the docs.
Otis knows what users do and say, not who they are. PII is redacted in the SDK and collector, before anything hits disk. SOC 2 in process, with HIPAA options for regulated teams.
Get Otis into your product.
Drop your email and pick a time with one of our cofounders: a look at how Otis works, and a conversation about what your users are actually doing.
Made in San Francisco for AI-native teams, by repeat founders who’ve built services from scratch and scaled them to 1B users.
