---
title: "Rippit vs. Glean for Conversation Analysis and Agents (2026): Differences, Pricing, and When to Use Both"
description: "Rippit vs. Glean: Glean searches company knowledge; Rippit analyzes every customer conversation at scale. Differences, pricing and when to use both."
canonical: https://www.rippit.com/comparisons/rippit-vs-glean
type: Comparison
datePublished: 2026-09-28
dateModified: 2026-11-13
---

# 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]](#src-1)  
  
 Rippit is a Growth Data Platform: it reads every customer conversation in the sources you connect, turns what it finds into structured data you can count and trend,[[3]](#src-3) and keeps that data current alongside your CRM, product and market data, so your teams can set up agents on it.[[2]](#src-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 and free to start, with $10 in credits.[[4]](#src-4) Glean pricing is by demo.[[5]](#src-5) They overlap a little: both can power agents for go-to-market and customer experience work.

By Lauren Alexander · Updated November 13, 2026

## Rippit vs. Glean at a glance

| CRITERIA | Glean | Rippit |
| --- | --- | --- |
| What is it? | Enterprise AI platform with search, an assistant and agents[[1]](#src-1) | Growth Data Platform for your conversations and business data, with agents your teams set up[[2]](#src-2) |
| Core job | Search and answer across work knowledge: documents, chat, tickets, calls and people[[6]](#src-6) | Turn every conversation you connect, and the business data around it, into structured, countable data[[3]](#src-3) |
| Starting point | Your company's knowledge across hundreds of connected tools[[1]](#src-1) | The conversations you already have: tickets, calls and chats,[[7]](#src-7) plus the survey, CRM, product and market data around them |
| Best fit | Every employee who needs a document, answer or expert, and IT teams rolling out AI across the company | Large-scale analysis and agents across every conversation you connect, with one dataset serving product feedback, revenue, CX and support operations, customer success and churn risk, and AI agent monitoring[[8]](#src-8)[[9]](#src-9) |
| Question type | "Where's our refund policy?" "How did we resolve a case like this?" | "How many customers hit this issue, why, and is it getting worse?" |
| Output | Answers, summaries and drafted content, with citations to source documents[[10]](#src-10) | Structured fields on every conversation, plus the counts and trends built from them[[11]](#src-11) |
| Conversation analysis | Indexes Zendesk[[12]](#src-12) and Intercom tickets[[13]](#src-13) and Gong call transcripts[[14]](#src-14) so employees can search them and ask questions | The foundation. AI analyzes every conversation you connect, with nothing sampled, and adds new ones as they sync[[2]](#src-2) |
| How teams use the data | Surface the right experts, runbooks, product updates and past resolutions so frontline teams resolve more; uncover common customer frustrations and root causes[[15]](#src-15) | Describe the insight you want, and Rippit adds it as a field across every conversation (root cause, churn risk, resolution).[[11]](#src-11) Reuse it in dashboards, reports and agents |
| Customization | Agent builder for reasoning-based agents and workflows grounded in company context[[16]](#src-16) | AI tags every conversation in scope with the fields you define, such as intent, root cause, churn signal and resolution[[11]](#src-11), and each team can add its own fields and sources |
| What gets scanned, analyzed and acted on | Retrieves the most relevant results for each question.[[17]](#src-17) A Company Search step returns about 10 results by default[[17]](#src-17) | 100% of conversations in scope, with nothing sampled[[4]](#src-4)[[2]](#src-2) |
| How it handles large datasets | Knowledge graph of people, content and activity;[[6]](#src-6) uploaded spreadsheets analyzed in a sandbox, indexed files via retrieval[[18]](#src-18) | Stores every conversation you connect as structured, queryable data[[3]](#src-3) that agents and people can enrich and aggregate across the full dataset |
| Traceability | Answers cite the source documents they draw on[[10]](#src-10) | Every result is a field on a specific conversation,[[3]](#src-3) and each analysis step is documented, so any answer can be audited |
| Repeatability | Agents can run in the background on a schedule, such as daily or weekly, once admins enable it,[[19]](#src-19) or be triggered from key events[[16]](#src-16) | Agents you set up run hourly, daily, weekly or monthly on new data and send findings, with the evidence, to Slack or email[[28]](#src-28) |
| Setup | Admins connect each data source.[[12]](#src-12) Permissions from each source apply at query time[[12]](#src-12) | Self-serve. Connect Zendesk, Intercom, Granola, Gong and more with pre-built integrations[[7]](#src-7), without needing an engineer to start |
| Works with Claude | Remote MCP server connects Claude and other hosts to Glean's company knowledge and admin-enabled tools[[20]](#src-20) | Rippit's MCP server lets Claude, Cursor or ChatGPT (on Business, Enterprise or Edu plans) query your Rippit data and open the conversations behind an answer[[21]](#src-21) |
| Using both | Keep Glean for finding knowledge and answers across the company | Add Rippit for the metrics: how often each issue happens, why, the trend and the accounts it affects |
| Public pricing | Pricing by demo[[5]](#src-5) | $10 in free credits to start, no card needed; then pay as you go: $1/GB a month for storage with compute included, AI models at list price, people and agents free; Enterprise by quote[[4]](#src-4) |
| Best starting question | "What do we already know about this, and where is it?" | "Why are refund contacts rising this month, and which accounts are at risk?" |

Pricing checked November 13, 2026.

**01 · THE DIFFERENCE**

## What's the difference between Rippit and Glean?

Glean retrieves the right information and is optimized for search. Rippit measures what's happening across every customer conversation you connect.

Glean is enterprise AI search with an assistant and agents on top.[[1]](#src-1) It builds a knowledge graph of your company's people, content and activity, so each employee gets permission-aware, personalized results[[6]](#src-6) and cited answers from hundreds of tools.[[10]](#src-10) It works well when someone needs a document, a policy, a past decision or an expert.

Rippit is a Growth Data Platform that starts with your customer conversations.[[2]](#src-2) It reads every sales, support, success and chatbot conversation you connect[[2]](#src-2) and records what it finds (intent, root cause, sentiment, churn signal, resolution) as fields you can filter, combine, trend and build on.[[11]](#src-11) It analyzes your CRM, product usage and market and web research too, so a pattern in the conversations sits next to the accounts and revenue it affects. Product, CX, CS and leadership work from the same data, and each team can set up agents on it that send what changed to the person who acts.

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.

**IN GLEAN · A SUPPORT AGENT ASKS**

An answer

**"How do we handle partial refunds?"**

Partial refunds are allowed within 30 days for damaged or missing items. Issue them from the order page and note the reason on the ticket.

Refund policy · Resolved ticket · Team thread · Help center article

**IN RIPPIT · A SUPPORT LEADER ASKS**

Every conversation, counted

"Why are refund contacts rising this month?"

R. **Rippit** analyzed 1,284 refund conversations across tickets, chats and calls.

- Refund contacts **1,284** this month

- Trend **+38%** vs. last month

- Top driver **Policy change** partial-refund rules, 41% of contacts

- Customers at risk **62** with a churn signal

**Evidence from the conversations**

Ticket #48321

“The new partial-refund rule doesn't match what I was told at checkout.”

**Root cause: policy change**

Chat #20917

“If this isn't fixed I'm moving our account.”

**Churn signal**

**02 · USE BOTH**

## Can you use Rippit and Glean together?

Yes. They do different jobs. Keep Glean for search, answers and agents across company knowledge.[[1]](#src-1) Add Rippit to turn customer conversations into metrics you can track.[[3]](#src-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]](#src-22) They came to Rippit for what search doesn't give them: counts and trends across every ticket.[[22]](#src-22)

Using both closes three common gaps:

- ** Search finds tickets. Leaders need patterns.** A search returns the most relevant tickets for a question.[[17]](#src-17) Rippit does the mass-scale enrichment: it analyzes every conversation in scope[[2]](#src-2) and stores each judgment as a field on that conversation,[[11]](#src-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]](#src-11) so the trend line starts with your full history rather than the day you thought to ask. Rippit also runs ad hoc worksheet analysis on up to 10,000 conversations at a time, for questions no existing field answers yet.[[3]](#src-3)
- ** Answers can draw on both.** Ask why refund contacts are rising, and a Rippit agent returns the count, trend, driver and at-risk accounts from every conversation you connect. It can also call Glean's MCP server for the current refund policy and the thread where it changed, so the support lead gets what customers hit and what the policy says.

**03 · METHOD**

## 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]](#src-12) Intercom conversations and tickets,[[13]](#src-13) and Gong calls and transcripts.[[14]](#src-14) Glean's support solution also positions it to "uncover common customer frustrations and the root causes behind recurring questions."[[15]](#src-15)

The difference is method. Glean's search and agents retrieve the most relevant results for each question, then summarize them.[[17]](#src-17) A Company Search step retrieves about 10 results by default, and teams can raise that number.[[17]](#src-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]](#src-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.

**04 · SCALE**

## How does Rippit analyze every conversation?

Search returns the most relevant results. Rippit reads every one you connect: 1,000 conversations or 1,000,000.[[2]](#src-2)

Rippit runs an AI engine built to label conversations in bulk: AI enriches every conversation you connect[[2]](#src-2) and stores the results as structured, queryable data,[[3]](#src-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]](#src-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]](#src-27) For open-ended questions no existing field answers, Rippit also runs ad hoc worksheet analysis[[3]](#src-3) on up to 10,000 conversations at a time, and what they find can feed new analysis and agents.

That changes three things:

**1,000 conversations or 1,000,000. Rippit reads every one you connect.**

**Counts are complete**

"What share of refund contacts went unresolved?" is computed across every refund contact in scope, not a sample.[[4]](#src-4)

**New questions reach the past**

Describe a new field in plain English, and Rippit applies it to past conversations in a single run.[[11]](#src-11)

**Every number is checkable**

Each result is a field on a specific conversation, so you can open the conversations behind any count.[[3]](#src-3)

**05 · AI ASSISTANTS**

## 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]](#src-20) Rippit supports MCP, so Claude can query your Rippit data and open the conversations behind any number.[[21]](#src-21)

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

What Rippit adds is analysis that's already done and kept. An assistant works within its context window,[[24]](#src-24) so across thousands of tickets it retrieves or summarizes rather than classifying each one the same way. Rippit has already classified every conversation you connect and saved the results, so Claude starts from fields it can count, and every number traces back to specific conversations.

**06 · PRICING**

## How much do Rippit and Glean cost?

Rippit publishes its pricing. AI models and data tools are billed at each provider's list price, with no markup.[[4]](#src-4) Glean doesn't publish prices on its website; its site directs buyers to request a demo.[[5]](#src-5)

**RIPPIT**

**Published plans**

- **Free to start:** $10 in credits, no card needed, plus $90 more when you add a card[[4]](#src-4)
- **Storage:** $1 per GB a month, with the compute to sync, run jobs and query included[[4]](#src-4)
- **AI models and data tools:** each provider's list price, no markup[[4]](#src-4)
- **People and agents:** free[[4]](#src-4)
- **Enterprise:** a personalized quote, with SSO, SCIM, audit logs, data region choice and custom terms[[4]](#src-4)

**SIZE RIPPIT BY**

**Conversation volume you want analyzed**

**GLEAN**

**Pricing by demo**

Glean doesn't publish prices on its website; its site directs buyers to request a demo.[[5]](#src-5) Because the products do different jobs, compare each against its own use case: Glean against the number of employees who need search and AI assistance, Rippit against the conversation volume you want analyzed.

**SIZE GLEAN BY**

**Employees who need search and AI assistance**

**07 · CUSTOMERS**

## 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]](#src-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]](#src-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]](#src-8)

## When should you choose Glean, Rippit, or both?

**CHOOSE**

### Glean

You want every employee to find documents, answers and experts across company tools, and to build agents on that knowledge.

**CHOOSE**

### Rippit

You need counts, rates and trends from every conversation you connect, not search results, read alongside the account, product and market data that shows what they're worth. You also want agents your teams set up to send each change to the person who acts.

**CHOOSE**

### Both

Glean already answers your team's questions, and your leaders still can't say how many customers hit a problem, why, or whether it's getting worse.

## Frequently asked questions

### Is Rippit a Glean alternative?

No. Glean is company-wide search and AI assistance.[[1]](#src-1) Rippit is a Growth Data Platform that turns your conversations and business data into structured data your teams build agents on.[[2]](#src-2) Most teams that need both use both.

### Does Glean connect to Zendesk and Intercom?

Yes. Glean indexes Zendesk tickets, help-center articles and comments,[[12]](#src-12) and Intercom conversations, tickets and Help Center articles.[[13]](#src-13)

### Can Glean tell me what percentage of tickets mention an issue?

Glean retrieves the most relevant results for a question and summarizes them.[[17]](#src-17) Rippit classifies every conversation in scope, so percentages and trends come from the full dataset.[[2]](#src-2)

### Does Rippit analyze 100% of conversations?

Yes, every conversation in the scope you choose, with nothing sampled.[[2]](#src-2)

### Is Rippit cheaper than Glean?

Rippit is free to start, with $10 in credits and no card needed, then pay as you go: $1 per GB a month for storage, and AI models at list price.[[4]](#src-4) Glean pricing is by demo,[[5]](#src-5) so ask for a quote on the scope you need.

### Do I need keyword rules to use Rippit?

No. AI classifies conversations with the fields you define, and new categories apply to past conversations in one run.[[11]](#src-11)

### Can I use Rippit and Glean in Claude at the same time?

Yes. Both support MCP,[[20]](#src-20)[[21]](#src-21) and Claude lets you turn on several connectors in one conversation.[[23]](#src-23)

### Can I trace a number back to the conversations behind it?

Yes. In Rippit, every result is a field on a specific conversation,[[3]](#src-3) so you can audit any answer by opening the conversations and fields behind it.

### Is Rippit the same company as MaestroQA?

Yes. MaestroQA is now Rippit: same founders, with a rebuilt AI-first platform. The company moved from maestroqa.com to rippit.com, and existing customer data, rubrics and integrations carried over.[[26]](#src-26) Some Rippit help center articles still appear under the MaestroQA name and domain.

## Sources

Rippit was formerly MaestroQA. Some sources below are hosted on the MaestroQA help center (help.maestroqa.com).[[26]](#src-26)

1. Glean, homepage: [https://www.glean.com/](https://www.glean.com/)
2. Rippit, homepage: [https://www.rippit.com/](https://www.rippit.com/)
3. Rippit, How It Works: [https://www.rippit.com/how-it-works](https://www.rippit.com/how-it-works)
4. Rippit, Pricing: [https://www.rippit.com/pricing](https://www.rippit.com/pricing)
5. Glean, Pricing: [https://www.glean.com/pricing](https://www.glean.com/pricing)
6. Glean, Search: [https://www.glean.com/product/search](https://www.glean.com/product/search)
7. Rippit, Integrations: [https://www.rippit.com/integrations](https://www.rippit.com/integrations)
8. Rippit, Klaviyo customer story: [https://www.rippit.com/customer-story/klaviyo](https://www.rippit.com/customer-story/klaviyo)
9. Rippit, Brex customer story: [https://www.rippit.com/customer-story/brex](https://www.rippit.com/customer-story/brex)
10. Glean, Assistant: [https://www.glean.com/product/assistant](https://www.glean.com/product/assistant)
11. Rippit, Conversation Taxonomy playbook: [https://www.rippit.com/playbooks/conversation-taxonomy](https://www.rippit.com/playbooks/conversation-taxonomy)
12. Glean Docs, Zendesk connector: [https://docs.glean.com/connectors/native/zendesk/](https://docs.glean.com/connectors/native/zendesk/)
13. Glean Docs, Intercom connector: [https://docs.glean.com/connectors/native/intercom/](https://docs.glean.com/connectors/native/intercom/)
14. Glean Docs, Gong connector: [https://docs.glean.com/connectors/native/gong/](https://docs.glean.com/connectors/native/gong/)
15. Glean, AI for Customer Service: [https://www.glean.com/solutions/support](https://www.glean.com/solutions/support)
16. Glean, Agents: [https://www.glean.com/product/agents](https://www.glean.com/product/agents)
17. Glean Docs, Company search tool: [https://docs.glean.com/tools/glean/company-search](https://docs.glean.com/tools/glean/company-search)
18. Glean Docs, Data Analysis overview: [https://docs.glean.com/administration/assistant/data-analysis/about-data-analysis](https://docs.glean.com/administration/assistant/data-analysis/about-data-analysis)
19. Glean Docs, Schedule triggers: [https://docs.glean.com/agents/concepts/schedule-triggers](https://docs.glean.com/agents/concepts/schedule-triggers)
20. Glean Developers, Remote MCP Server: [https://developers.glean.com/guides/mcp/](https://developers.glean.com/guides/mcp/)
21. Rippit, The Complete Guide to Conversation Analytics: [https://www.rippit.com/playbooks/the-complete-guide-to-conversation-analytics](https://www.rippit.com/playbooks/the-complete-guide-to-conversation-analytics)
22. Rippit first-party research: AI analysis of 2,345 Rippit sales conversations (2025–2026), run on Rippit's own conversation analytics platform. 9 prospects used or were evaluating Glean.
23. Claude Help Center, Use connectors to extend Claude's capabilities: [https://support.claude.com/en/articles/11176164-use-connectors-to-extend-claude-s-capabilities](https://support.claude.com/en/articles/11176164-use-connectors-to-extend-claude-s-capabilities)
24. Claude Docs, Context windows: [https://platform.claude.com/docs/en/build-with-claude/context-windows](https://platform.claude.com/docs/en/build-with-claude/context-windows)
25. Rippit, Checkr customer story: [https://www.rippit.com/customer-story/checkr](https://www.rippit.com/customer-story/checkr)
26. Rippit, "MaestroQA is now Rippit": [https://www.rippit.com/news/maestroqa-is-now-rippit](https://www.rippit.com/news/maestroqa-is-now-rippit)
27. Rippit, The Coverage Benchmark, Part 8: Rippit: [https://www.rippit.com/research/coverage-benchmark-rippit](https://www.rippit.com/research/coverage-benchmark-rippit)
28. Rippit docs, Pipelines: [https://docs.rippit.com/pipelines/overview](https://docs.rippit.com/pipelines/overview)

Build your first agent on your customer and business data.

## Getting started from an AI agent

There are two ways to connect. Either works.

- MCP server: `https://mcp.rippit.com/mcp` (Streamable HTTP, OAuth). The person signs in or signs up in the browser that opens. No client ID, secret or API key is needed.
- CLI: install with `curl -fsSL https://rippit.sh | sh` or `brew install adtribute/tap/rippit`, then get an account one of two ways:
  - `rippit login`: the person signs in or signs up in a browser.
  - `rippit start`: creates a temporary account with no email or browser, for a person with no Rippit account. `rippit claim --wait` lets them keep it; an unclaimed account is deleted after 14 days.

Then bring in data: connect a provider such as Zendesk, Intercom, Gong or Salesforce, or upload a CSV.

Full steps: https://www.rippit.com/setup.md