Rippit vs. Fireflies for Conversation Analysis and Agents (2026): Differences, Pricing, and When to Use Both

Can you use Rippit and Fireflies together?

Yes. They do different jobs. You keep Fireflies for meeting notes, summaries and search,[1] and use Rippit to analyze the support tickets, chats and calls it connects to directly,[12] asking questions that span the whole customer journey. Rippit does the mass-scale enrichment, analyzing every conversation in scope[2] and storing each judgment as a field on that conversation,[4] and runs ad hoc deep dives on up to 10,000 conversations at a time for questions no existing field answers yet.[3] A direct integration to Fireflies is planned.

In Rippit's review of 2,345 of its own sales conversations from 2025–2026, prospects who captured meetings in Fireflies wanted to analyze those transcripts alongside support tickets and CRM data, without manual exports.[5]

Using both fixes two common gaps:

• Meeting notes and support conversations live in different tools. Fireflies holds what was said in meetings, and your helpdesk holds what happened after. Rippit brings the ticket and chat history into the same analysis as your calls.[12]
• Churn signals show up after the handoff. Checkr combined Rippit with Snowflake operational data to find churn signals.[15] Brex feeds predicted CSAT and churn signals from Rippit into its customer success workflows.[16]

How is Rippit's analysis different from AskFred?

Global AskFred answers questions about your meetings. It searches across past meetings, connected apps like Gmail, Slack and HubSpot, and the web, and it can identify trends and recurring topics over time, charged in AI credits per request.[13] AI Skills turn meetings into structured outputs from 200+ templates, after each meeting or on a schedule.[8] Fireflies' conversation intelligence dashboard, on Business and Enterprise, tracks metrics like talk-to-listen ratio, questions asked and sentiment.[9]

Rippit starts from a plain description of the insight you want and adds it as a field across every conversation.[3] One pass tags each conversation across 10+ dimensions, including intent, root cause, sentiment, urgency, churn signal and resolution, and a new category applies to past conversations in a single run.[4] Because each result is stored as a field, you can count it, trend it and filter by it instead of asking again. For cross-journey questions, you define a field like "promise made in sales" or "onboarding friction" once and read it across calls, tickets and chats.

This speed pays off with unplanned questions. When Checkr's CEO asked a late-night question about background check issues in a specific state, the team delivered a detailed report by the next morning.[15]