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

Short answer: Glean helps employees find and summarize information across company knowledge: docs, Slack, tickets and calls.[1]

Rippit is built for one job: analyzing and acting on every customer conversation at scale.[2] It reads every conversation, turns what it finds into structured data you can count and trend,[3] and runs AI agents that monitor, analyze and act on that data.[2]

Glean answers "where's the doc?" Rippit answers "how many customers, why, and is it getting worse?" Many teams use both. Rippit is self-serve with a free plan.[4] Glean pricing is by demo.[5] Where Glean and Rippit overlap is in GTM and Customer Experience analysis and Agents.

Take a spike in refund complaints. In Glean, a support agent asks "how do we handle partial refunds?" and gets a cited answer pulled from a few sources. In Rippit, a support leader sees how many refund contacts came in this month, whether that's rising, which policy change is driving them, and which customers are at risk. The first is an answer. The second is a number you can put in front of a product team.

Can you use Rippit and Glean together?

Yes. They do different jobs, so they don't overlap much. Keep Glean for search, answers and agents across company knowledge.[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 Glean described it as their company-wide search and assistant layer.[22] They came to Rippit for something search doesn't give them: counts and trends across every ticket, and a clear view of what's changing.[22]

Using both closes three common gaps:

• Search for real-time human interaction. Leaders need patterns and analysis. A search returns the most relevant tickets for a question.[17] 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 Rippit Agents to Glean via MCP. Some Rippit Agents benefit from calling the Glean MCP for additional internal knowledge to improve analysis and action.

Doesn't Glean already analyze support tickets?

Glean indexes support conversations, and it's strong at finding them. Its connectors cover Zendesk tickets and comments,[12] Intercom conversations and tickets,[13] and Gong calls and transcripts.[14] Glean's support solution also positions it to "uncover common customer frustrations and the root causes behind recurring questions."[15]

The difference is method. Glean's search and agents retrieve the most relevant results for each question, then summarize them.[17] A Company Search step retrieves about 10 results by default, and teams can raise that number.[17] For analytical questions across indexed files, Glean's docs state that it "uses text-based retrieval and synthesis (RAG), not code execution," and doesn't support numerical aggregation across multiple indexed files.[18]

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.

How does Rippit analyze every conversation?

Search returns the most relevant results. Rippit reads every one: 1,000 conversations or 1,000,000.[2]

Rippit runs an AI engine built to label conversations in bulk: AI enriches every conversation[2] and stores the results as structured, queryable data,[3] so analysis and agents can work across all of it. Reading everything, not just the top search results, doesn't have to be expensive. In Rippit's own coverage benchmark, it read all 1,000 transcripts in full for about six cents, the same full read that cost about $61 per question with a map-reduce approach.[27] Questions then run on those stored fields across the full dataset, instead of rereading transcripts each time, so asking the same question again returns the same answer.[27] For open-ended questions no existing field answers, Rippit also runs ad hoc deep dives[3] on up to 10,000 conversations at a time, and what they find can feed new analysis and agents.

That changes three things:

Do Glean and Rippit have an MCP server?

Yes, both do. Glean's remote MCP server connects Claude and other hosts to your company's Glean knowledge, with each person's own permissions.[20] Rippit supports MCP, so Claude can query your conversation data directly.[21]

In Claude, you can turn on several connectors for the same conversation.[23] A team can pull the refund policy from Glean and the refund-complaint trend from Rippit without switching tools.

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,[24] 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.

How much do Rippit and Glean cost?

Rippit publishes its pricing, and paid plans bill AI credits at cost.[4] Glean doesn't publish its prices; buyers request a demo.[5]

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.[25]
• 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]

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