B2CProduct agent for in-app product and AI agent owners

In-app Chat Insights

Turn what your AI agent can't do into what you build next.

In-app Chat Insights reads every conversation users have with the AI agents in your product, from assistants that answer questions to agents that take actions for them. It surfaces the missing features, bugs, knowledge gaps and improvement opportunities in those chats, and drafts the tickets and docs to act on them.

Paste in your AI agent

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

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The problem

Your AI agent hears what users want every day. Your roadmap never does.

1 in 3

teams building AI agents say quality is their biggest barrier to production.

Only 37% evaluate how their agents perform in real user conversations.

Source: LangChain, State of Agent Engineering (1,340 professionals, November–December 2025)
How it works

Every gap sorted into what to build, fix or document

Three steps. It reads every conversation with your in-product AI agent, finds where it fell short, and drafts the ticket or doc that would close each gap.

Ingest

Every chat with the AI agent in your product

AI assistant
Agentic features
Knowledge base
Backlog
Anything else
Rippit

Analyze

The gap behind every request it couldn’t handle

One row per requestAgent adding fields…
RequestPlanNext step
“Split my order across two days”All plansDraft ticket: split deliveries
“Add this recipe to my cart”Plus membersBug ticket with the failed runs
“How do substitutions work?”All plansDraft doc: substitutions
3,100 chats with a gap this week · 7 tickets and 6 docs drafted

Act

Tickets and docs drafted before the gap grows

AI agent digestProduct and AI agent owners, in Slack

640 chats with a gap across 18 issues, led by bulk user import. 9 tickets and 4 docs drafted.

Digest sent
Draft ticketsLinear

9 issues drafted in Linear, from missing features to bugs.

Tickets filed
+ Any connected MCP

Suggest an update to the AI agent's instructions, or post the top gaps to your product channel.

See MCP connections →
FAQ

Questions

What sources can it use?

Conversations from the AI agents in your product, your knowledge base, support conversations and your Linear backlog. Usage and billing come through your warehouse, the Ingest API or a CSV, and a Jira backlog through a CSV. See integrations →

How does it know the agent fell short?

It reads each conversation in full: whether the agent said it couldn't help, gave an answer that didn't fit or failed to finish an action, and whether the user rephrased, gave up or contacted support. Each gap is checked against your docs to see if an answer already exists.

Does it work with any in-app AI agent?

Yes. It works with an agent you built yourself, an assistant from a vendor, or agentic features that take actions for users. Decagon, Ada and Sierra connect directly, and any other agent’s conversation logs come in through the Ingest API or a CSV.

How is this different from support bot analytics?

Support bot analytics track deflection and resolution. This agent looks at what users ask the AI in your product to do, and turns what it couldn't handle into product work: missing features, bugs, knowledge gaps and improvements.

Where do the results go?

The digest goes to Slack or email. Draft issues go to Linear, or to Jira through a custom MCP connection. Draft docs go to your Intercom help center, Notion or Google Docs, all for your review. It runs weekly by default, or daily. See MCP connections →

Learn from every request your AI agent couldn't handle.

Connect your in-app AI conversations and knowledge base, and get your first digest with tickets and docs already drafted.

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