B2CProduct agent for CPOs, VPs of Product & PMs

Launch Feedback Agent

Know if what you just shipped is actually helping.

You’re shipping faster than ever. This agent tells you whether each release actually made things better for customers. It joins usage and revenue from your warehouse with what customers said in tickets, chats and calls into one readout: who adopted, who got stuck, why, and what to fix before the next release.

Paste in your AI agent

Copy this line into ChatGPT, Claude, Gemini or your coding agent. It takes it from there.

Set me up with rippit.com
Try Rippit free
or request demo
The problem

Shipping fast is table stakes. Knowing what landed is the edge.

80%

of features in the average software product are rarely or never used.

Only 31% of product organizations prioritize rapid experimentation and iterative learning.

Source: Pendo, Feature Adoption Report (615 software products, 2019); Atlassian, State of Product 2026 (1,000+ product professionals, US and Europe)
How it works

From ship to signal in days.

Usage tells you who tried your new feature. Tickets, chats and reviews tell you where they got stuck. Your billing data tells you which plans and platforms are on the line. The agent joins all three for every release, so you know what to fix before you ship the next one.

Ingest

Usage, revenue and what customers said about the launch

Product usage
Support + chats
Web research
Anything else
Rippit

Analyze

Who adopted, who got stuck and why

Your new feature · one row per segmentAgent adding fields…
SegmentUsageStuck at
Pro plan · AndroidTried onceSetup step 3
Free plan · iOSNever openedNavigation
Pro plan · iOSDailyNone
2,140 users tried it once, then contacted support · 68% on Android

Act

The fix drafted before the next release

Slack· #launch-feedback

Your new feature, day 3: 38% adoption, 64% positive. Biggest stuck point: setup step 3.

Message sent
Google Docs· full readout

Every account that tried it, where they got stuck and the quotes behind it.

Doc updated
+ Any connected MCP

File the setup bug in Linear with the 14 accounts it blocks.

FAQ

Questions product teams ask

What sources can it use?

Usage, CRM and billing data from Snowflake, the Ingest API or a CSV. Support tickets and chats from tools like Zendesk or Intercom, AI agent chats from Decagon, Ada or Sierra, sales and CS calls from Gong or Fireflies, Slack threads, and web research on reviews and forums.

Where do the results go?

Slack, email or a Google Doc. It can also file bugs in Linear and write to Notion, and update Jira or your CRM through a custom MCP connection.

Can I change when it runs?

Yes. Run it daily or weekly after launch. Each readout shows the days since release, so you can compare day 3, week 1 and day 30 side by side.

Does it work for B2B and B2C?

Yes. For B2B, feedback breaks down by account and ARR. For B2C, it breaks down by plan, platform or segment.

How do I know a finding is real?

Every number links back to the tickets, reviews, threads and usage records behind it, so you can check the source in one click.

Ship the next thing knowing the last one landed.

Connect your sources, pick when it runs, and get a readout on your next launch this week.

Paste in your AI agent

Copy this line into ChatGPT, Claude, Gemini or your coding agent. It takes it from there.

Set me up with rippit.com
Try Rippit free
or request demo