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

Can you use Rippit and Dust together?

Yes. They do different jobs, so they don't overlap much. Keep Dust for agents that work across company knowledge and tools.[1] Add Rippit to turn customer conversations into metrics you can track.[3]

In Rippit's review of 2,345 of its own sales conversations from 2025–2026, prospects who already used Dust described building recurring reports and ticket dashboards in it.[19] They came to Rippit for counts and trends across every ticket, without uploading data by hand.[19]

Using both closes three common gaps:

• Search for real-time human interaction. Leaders need patterns and analysis. A Dust search returns the documents most relevant to a question, and Dust notes that "RAG scans for relevance, not completeness."[20] Rippit does the mass-scale enrichment: it analyzes every conversation in scope[2] and stores each judgment as a field on that conversation,[11] so you see how often an issue happens and whether it's growing.
• New questions need history. In Rippit, a new category applies to past conversations in one run,[11] so the trend line starts with your full history rather than the day you thought to ask. Rippit also runs the deep dives: ad hoc analysis on up to 10,000 conversations at a time, for questions no existing field answers yet.[3]
• Connect Dust agents to Rippit via MCP. Dust admins can add remote MCP servers as tools for their agents,[21] so a Dust agent can pull Rippit's conversation metrics into its workflow.[22]

Doesn't Dust already analyze support tickets?

Dust syncs support conversations, and its agents are strong at finding and acting on them. Its connections cover Zendesk tickets,[12] Intercom conversations[13] and Gong transcripts.[14] Dust's customer support solution also lists ticket insights, to "turn support interactions into learning opportunities."[7]

The difference is method. Dust's search retrieves the documents that seem most relevant to each question, and its docs state that the system "is designed to understand and generate language, not to count occurrences across documents."[20] For Zendesk, Dust says that "quantitative questions such as how many tickets about X topic have been assigned to Y are not supported out of the box."[12] Its Extract data tool runs a model on up to 500k tokens of data,[15] and its table queries run SQL on structured data but are "not suited for questions on unstructured / raw text data."[16]

That's the right design for finding an answer. A reliable rate needs something else. "What share of this month's tickets involved a billing error?" requires every ticket classified the same way, then counted. That's what Rippit does.

Do Rippit and Dust work with Claude?

Yes, in different ways. Dust runs Claude as one of its 20+ models,[1] and its remote MCP server lets compatible MCP clients work with Dust on behalf of the signed-in user.[18] Rippit supports MCP, so Claude can query your conversation data directly.[22]

Scale is why the analysis layer matters. A general-purpose assistant can only hold a limited amount of text in its context window at one time,[23] so it can't read thousands of tickets in one pass. Rippit runs the analysis across every conversation and gives Claude the results, so every number traces back to specific conversations.

What do teams use Rippit for?

• Checkr broke dissatisfaction down into specific "atomic problems." One billing cluster showed 47% predicted CSAT and a 58% unresolved rate, which the team took to its Chief Product Officer. When the CEO asked a late-night question, the team had a report by morning.[24]
• Brex replaced manual QA sampling with AI review of 100% of conversations and found churn risk in 3% of them. It uses custom AI classifiers for onboarding friction, product gaps and competitor mentions.[9]
• Klaviyo went from reviewing fewer than 2% of its 600,000+ annual support incidents to analyzing 2.5 million conversations that had never been looked at. It analyzed a full month of product-launch conversations in two hours and reports a productivity gain of about 40% in under 12 months.[8]