--- title: "Rippit vs. Decagon for Conversation Analysis and Agents (2026): Differences, Pricing, and When to Use Both" description: "Rippit vs. Decagon: Decagon's AI agents resolve conversations; Rippit analyzes every one, bot and human. Differences, pricing and when to use both." canonical: https://www.rippit.com/comparisons/rippit-vs-decagon type: Comparison datePublished: 2026-09-28 dateModified: 2026-11-13 --- # Rippit vs. Decagon for Conversation Analysis and Agents (2026): Differences, Pricing, and When to Use Both **Short answer:** Decagon builds AI agents that resolve customer conversations.[[1]](#src-1) Rippit is a Growth Data Platform that reads every Decagon conversation and human-agent ticket you connect, turns each into structured data[[3]](#src-3) and joins it to your account, billing and product data,[[2]](#src-2) so you can see whether issues were resolved, why, and what they cost. Rippit's agents work in the background on that data, never live with your customers. Teams keep Decagon to run the AI agent and add Rippit as an independent layer that reads 100% of the conversations they connect.[[4]](#src-4) They overlap only in analytics (Decagon's Watchtower and Insights). Rippit publishes its pricing.[[5]](#src-5) Decagon prices per conversation or per resolution.[[6]](#src-6) By Lauren Alexander · Updated November 13, 2026 ## Rippit vs. Decagon at a glance | CRITERIA | Decagon | Rippit | | --- | --- | --- | | What is it? | AI agent platform ("the AI concierge for every customer") across chat, voice and email[[1]](#src-1) | Growth Data Platform for your conversations and business data, with agents your teams set up.[[2]](#src-2) Rippit's agents analyze conversations; they don't talk to customers | | Starting point | Your workflows, knowledge and tools, written as Agent Operating Procedures (AOPs) in natural language[[7]](#src-7) | The conversations you already have: tickets, calls and chats (those contained by the bot as well as those handed over to human agents),[[8]](#src-8) plus the account, billing and product usage data around them | | Best fit | Teams automating customer conversations with AI agents | Large-scale analysis and agents across every conversation you connect, AI and human. One independent view, so CX, CS, product and leadership can see what's resolved, what's not, why and which accounts it affects | | Conversation analysis | Insights & Reporting (topics, CSAT, deflection, Ask AI)[[9]](#src-9) and Watchtower,[[10]](#src-10) built into the Decagon platform | The foundation of the platform. AI analyzes every conversation you connect, with nothing sampled[[2]](#src-2) | | AI agent monitoring | Watchtower flags conversations against criteria you define.[[10]](#src-10) QA Hub organizes AI-agent review batches with custom rubrics[[11]](#src-11) | An independent read on bot performance. Reads 100% of bot conversations you connect to surface silent failures, unresolved issues and the intents the bot struggles with[[12]](#src-12) | | Human + AI conversations in one view | Watchtower reviews AI and human agent interactions;[[10]](#src-10) coverage of conversations handled outside Decagon is a question to ask Decagon | Bot and human-agent conversations from your helpdesk sit in one dataset, so you can see where people succeed where the bot doesn't[[12]](#src-12) | | How teams use the data | Improve AOPs, knowledge and agent behavior. Duet helps teams build and refine AOPs[[7]](#src-7) | Describe the insight you want, and Rippit adds it as a field across every conversation you connect (root cause, churn risk, resolution).[[13]](#src-13) Reuse it in dashboards and follow-up questions, join it to account and billing data, and set up agents that send findings to the right owner | | Customization | Natural-language flag criteria, custom categories and rubric scoring in Watchtower[[10]](#src-10) | AI tags every conversation in scope with the fields you define, such as intent, root cause, churn signal and resolution[[13]](#src-13) | | What gets scanned, analyzed and acted on | Watchtower reviews every conversation against your criteria[[14]](#src-14) | 100% of conversations in scope, with nothing sampled[[5]](#src-5)[[2]](#src-2) | | How it handles large datasets | Watchtower organizes findings into clusters and tracks trends across flags and categories;[[10]](#src-10) Insights & Reporting clusters conversations into topics and tracks CSAT and deflection[[9]](#src-9) | Stores every conversation you connect as structured, queryable data[[3]](#src-3) that Rippit's agents and your team can enrich and aggregate across the full dataset | | Traceability | Watchtower lets you move from dashboard trends to individual transcripts, with full conversational context[[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 | Watchtower runs always-on against the criteria you set, surfacing issues as they occur[[10]](#src-10) | Agents you set up run hourly, daily, weekly or monthly on new conversations and send findings, with the evidence, to Slack or email[[25]](#src-25) | | Setup | Sales-led. Buyers book a demo, and Decagon's setup guide lays out a 6-week implementation from discovery to go-live[[18]](#src-18) | Self-serve. Connect Zendesk, Intercom, Granola, Gong and more with pre-built integrations,[[8]](#src-8) without needing an engineer to start | | Works with Decagon | Integrates with Zendesk, Salesforce and Intercom[[15]](#src-15) | Pre-built Decagon connector. Analyzes Decagon conversations, plus the human-agent tickets they escalate to, through your helpdesk connector | | Works with Claude | Uses MCP so its agents can call your tools;[[16]](#src-16) we found no Decagon-published connector for querying Decagon data from Claude (checked Nov 13, 2026) | Rippit's MCP server lets Claude, ChatGPT or Cursor query your Rippit data and open the conversations behind an answer[[17]](#src-17) | | Public pricing | Per-conversation or per-resolution pricing; no public price list[[6]](#src-6) | $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[[5]](#src-5) | | Best starting question | "How do we resolve more customer conversations with AI agents?" | "Did our AI agent actually resolve the issue, and where did it fail?" | Pricing checked November 13, 2026. **01 · THE DIFFERENCE** ## What's the difference between Rippit and Decagon? Decagon resolves customer conversations. Rippit analyzes them. Decagon is an AI agent platform it calls "the AI concierge for every customer."[[1]](#src-1) Teams write Agent Operating Procedures (AOPs) in natural language, connect the agent to their tools, and deploy it across chat, email and voice.[[7]](#src-7) Rippit turns those conversations into data your teams can use.[[2]](#src-2) It connects to your helpdesk and to Decagon, reads every conversation there, whether a bot or a person handled it,[[2]](#src-2) and turns what it finds (intent, root cause, sentiment, churn signal, resolution) into fields you can filter, trend and act on.[[13]](#src-13) It brings in the data around those conversations too, such as accounts, billing and product usage, so an unresolved refund request sits next to the customer's plan and renewal date. Teams set up agents on that foundation to watch for what changed and send the evidence to the right owner. They work behind the scenes on your data; Decagon's agents are the ones talking to customers. Take a customer who asks an AI agent about a refund, gets a policy answer and closes the chat. On a dashboard, that conversation counts as handled. Two days later the customer writes in again, and a human agent issues the refund. Rippit reads both conversations in full, tags the first as unresolved, and shows how often that intent ends up with a human agent. With billing data connected, it also shows which accounts those repeat contacts come from and what they're worth. Rippit can also take this further: Rippit's Bot Outcome Check agent groups the "resolved" chats that came back by topic and drafts the missing help articles, for your team to review. **ONE REFUND REQUEST, TWO CONVERSATIONS** **DAY 1** **Customer asks the AI agent about a refund, gets a policy answer and closes the chat** Dashboard: counts as handled **DAY 3** **Customer writes in again, and a human agent issues the refund** **RIPPIT** **Reads both conversations in full** First chat: unresolved · How often this intent ends with a human **NEXT** Agent drafts missing help articles for review **02 · SCALE** ## How does Rippit analyze every conversation? 1,000 conversations or 1,000,000, bot and human alike: Rippit reads every one you connect.[[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 Rippit's agents can work across all of it. Reading every conversation, including every escalation, 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.[[24]](#src-24) 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.[[24]](#src-24) For open-ended questions no existing field answers yet, you can run a worksheet analysis[[3]](#src-3) on up to 10,000 conversations at a time, and what it finds can feed new analysis and agents. That changes three things: **Counts are complete** "What share of refund requests the AI agent handled were actually resolved?" is computed across every one of those conversations, not a sample.[[5]](#src-5) **New questions reach the past** Describe a new field in plain English, and Rippit applies it to past conversations in a single run.[[13]](#src-13) **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) **03 · USE BOTH** ## Can you use Rippit and Decagon together? Yes. Keep Decagon to run your AI agent, and add Rippit as an independent layer that reads every conversation, bot and human, from your helpdesk. Decagon integrates with Zendesk and Intercom[[15]](#src-15) and hands conversations to human agents through those helpdesk integrations.[[18]](#src-18) Rippit connects to Zendesk, Intercom, Granola, Gong and more with pre-built integrations and analyzes the conversations recorded there.[[8]](#src-8) In Rippit's review of 2,345 of its own sales conversations from 2025–2026, 38 prospects were running or evaluating Decagon. The recurring reason they talked to Rippit wasn't to replace it. They wanted an independent check on AI-agent conversations, in the same place they review human agents and escalations.[[4]](#src-4) Using both fixes two common gaps: - **Bot reporting lives inside the bot platform.** Decagon's reporting centers on its own AI agent's performance.[[9]](#src-9) Rippit gives CX leaders an independent read that doesn't depend on any one AI agent vendor, so the same definitions hold if you add channels, add vendors or change your stack.[[2]](#src-2) - **Bot and human outcomes sit in different places.** Rippit puts both in one dataset. At Klaviyo, Rippit runs across 100% of bot conversations to identify "where humans were succeeding where the bot wasn't."[[12]](#src-12) Decagon's agents keep talking to your customers, while Rippit works behind them. Here's how that plays out for one question a CX or CS leader might ask: *which Decagon escalations trace back to a policy or product gap, and which accounts and how much revenue do they touch?* - **Decagon handles the conversation.** Its agent follows your AOPs and, when it can't resolve the issue, hands the customer to a human agent through its helpdesk integrations.[[7]](#src-7)[[18]](#src-18) - **Rippit has already read both sides.** The Decagon conversation and the human agent's ticket sit in one dataset, each tagged with fields like intent, root cause and resolution,[[13]](#src-13) so escalations can be grouped by cause. With your billing or CRM data connected, each one carries the account's plan, ARR and renewal date. - **A worksheet analysis names the gap.** "Policy gap or product gap" isn't a field yet, so a worksheet analysis reads up to 10,000 of those escalations at a time and answers the question now.[[3]](#src-3) To reuse the answer, add it as a field, and Rippit applies it to past conversations in one run.[[13]](#src-13) - **The work keeps running.** An agent you set up checks each week's new escalations and sends support operations the top causes, the accounts behind them and example conversations, in Slack or email.[[25]](#src-25) Your team decides whether the fix goes into Decagon's AOPs or onto the product backlog. **CUSTOMERS** **DECAGON AI AGENT** **Talks to customers across chat, email and voice[[1]](#src-1)** **HUMAN AGENTS** **Take escalations handed over through the helpdesk[[18]](#src-18)** **YOUR HELPDESK** **Bot and human conversations recorded** Zendesk Intercom **RIPPIT · INDEPENDENT LAYER** **Reads every conversation, bot and human, in one dataset** Resolved? · Silent failures · Where humans succeed **38** prospects running or evaluating Decagon, in Rippit's review of 2,345 sales conversations. They wanted an independent check, not a replacement.[[4]](#src-4) **04 · RESOLUTION** ## Is containment the same as resolution? No. Containment means the conversation stayed with the AI agent. Resolution means the customer's problem was solved. A customer can leave mid-conversation or accept an answer that doesn't fix the issue, and the conversation can still look contained. Decagon's own explainer on resolution-based pricing names both cases.[[19]](#src-19) Rippit measures resolution by reading the conversation itself. Resolution is one of the dimensions AI tags on every conversation in scope in one pass, alongside intent, root cause, sentiment, urgency and churn signal.[[13]](#src-13) Klaviyo used this to change how its bot escalates. Rippit surfaces silent failures in its bot conversations and feeds those patterns back into training. When a conversation signals an intent the bot consistently struggles with, it now triggers an immediate human handoff, "prioritizing speed to resolution over bot containment." Klaviyo reports a lower escalation rate and a higher true containment rate.[[12]](#src-12) **CONTAINED** Stayed with the AI agent **Customer leaves mid-conversation** Not resolved **Customer accepts an answer that doesn't fix the issue** Not resolved **Customer's problem is solved** Resolved All three can look contained. Rippit tags resolution by reading the conversation.[[13]](#src-13)[[19]](#src-19) **KLAVIYO'S ESCALATION RULE[[12]](#src-12)** **Intent the bot consistently struggles with** **Immediate human handoff** Lower escalation rate · Higher true containment rate **05 · ANALYTICS** ## How is Rippit different from Decagon Watchtower and Insights? Decagon's analytics are built into the platform you use to run your agent. Watchtower reviews every conversation, with an AI or human agent, against criteria you write in natural language, such as "mentions of frustration."[[10]](#src-10) Insights & Reporting tracks CSAT and deflection, clusters conversations into topics, and answers questions like "Why are customers requesting refunds?"[[9]](#src-9) QA Hub gives teams a workspace to review AI-agent quality with custom rubrics.[[11]](#src-11) Rippit sits across tools, starting from your helpdesk,[[2]](#src-2) so Decagon conversations and human-agent tickets get the same classification: intent, root cause, sentiment, churn signal and resolution.[[13]](#src-13) Because it also reads account, billing and product data, those fields can answer questions that reach past the support queue, such as which accounts a recurring failure touches and what they're worth. Use Watchtower for real-time QA of your Decagon deployment. Add Rippit for one independent view of every support conversation, and for questions beyond the bot: churn risk, product gaps and coaching.[[20]](#src-20) **INSIDE THE DECAGON PLATFORM** Real-time QA of your deployment **Watchtower[[10]](#src-10)** Flags conversations against natural-language criteria **Insights & Reporting[[9]](#src-9)** CSAT, deflection and topic clusters **QA Hub[[11]](#src-11)** AI-agent review with custom rubrics **RIPPIT · ACROSS TOOLS** From your helpdesk[[2]](#src-2) Decagon conversations Human-agent tickets The same classification on both[[13]](#src-13) Intent · Root cause · Sentiment · Churn signal · Resolution **06 · PRICING** ## How much do Rippit and Decagon cost? Rippit publishes its pricing. AI models and data tools are billed at each provider's list price, with no markup.[[5]](#src-5) Decagon doesn't publish a price list, but it describes two pricing models.[[6]](#src-6) **RIPPIT** **Published pricing** - **Free to start:** $10 in credits, no card needed, plus $90 more when you add a card[[5]](#src-5) - **Storage:** $1 per GB a month, with the compute to sync, run jobs and query included[[5]](#src-5) - **AI models and data tools:** each provider's list price, no markup[[5]](#src-5) - **People and agents:** free[[5]](#src-5) - **Enterprise:** a personalized quote, with SSO, SCIM, audit logs, data region choice and custom terms[[5]](#src-5) **DECAGON** **No public price list** Decagon doesn't publish a price list. Its December 2024 pricing post describes two models: a fixed rate for every incoming conversation, or a higher fixed rate for each fully resolved conversation, with no charge for escalations.[[6]](#src-6) Decagon says the majority of its customers choose per-conversation pricing.[[6]](#src-6) **PER CONVERSATION** **Fixed rate** Every incoming conversation. Most customers choose this **PER RESOLUTION** **Higher fixed rate** Each fully resolved conversation; no charge for escalations **07 · CUSTOMERS** ## What do teams use Rippit for? - **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 runs Rippit across 100% of its bot conversations to catch silent failures and route hard intents to humans faster, and it analyzed a full month of product-launch conversations in two hours.[[12]](#src-12) - **Checkr** built predictive CSAT in Rippit that covers 100% of conversations, including chatbot conversations. Because customers communicate differently with a chatbot than with a human agent, Checkr built separate models for each source and combined them into one signal.[[23]](#src-23) ## When should you choose Decagon, Rippit, or both? **CHOOSE** ### Decagon You want AI agents to resolve customer conversations across chat, email and voice, with QA and reporting built into the same platform. **CHOOSE** ### Rippit You want to know what's driving support conversations, bot and human, and what each issue costs in accounts and renewals. You also want CX, CS and product to ask new questions and set up agents without writing keyword rules. **CHOOSE** ### Both You run Decagon and want an independent read on whether the AI agent actually resolves issues, how it compares with your human team, and what to change next. ## Frequently asked questions ### Is Rippit a Decagon alternative? No. Decagon runs AI agents that talk to your customers.[[1]](#src-1) Rippit doesn't answer customers; it analyzes the conversations your AI agents and human team have, next to your account and product data.[[2]](#src-2) ### Does Rippit integrate with Decagon? Yes. Decagon is a pre-built Rippit connector. You add a Decagon API key, and Rippit syncs every AI agent conversation on a schedule, along with its summary, resolution, CSAT, tags and Watchtower reviews.[[8]](#src-8) The human-agent tickets that follow an escalation come in through your helpdesk connector. ### Doesn't Decagon already have QA? Yes. Watchtower reviews conversations against criteria you define.[[10]](#src-10) Rippit adds an independent view across every helpdesk conversation you connect, bot and human.[[2]](#src-2) ### Can Rippit tell whether an AI agent actually resolved the issue? Yes. Resolution is one of the dimensions AI tags on every conversation in scope,[[13]](#src-13) so you can separate conversations that stayed with the bot from ones that were actually solved.[[12]](#src-12) ### Is Rippit cheaper than Decagon? They do different jobs, so most teams budget for both. 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.[[5]](#src-5) Decagon prices per conversation or per resolution.[[6]](#src-6) ### Does Rippit work with Claude? Yes. Rippit supports MCP,[[17]](#src-17) so Claude, ChatGPT or Cursor can query your Rippit data directly.[[21]](#src-21) ### 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.[[22]](#src-22) 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).[[22]](#src-22) 1. Decagon, homepage: [https://decagon.ai/](https://decagon.ai/) 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 first-party research: AI analysis of 2,345 Rippit sales conversations (2025–2026), run on Rippit's own conversation analytics platform. Decagon came up in 64 of 2,345 conversations; 38 prospects were current or prospective Decagon customers. 5. Rippit, Pricing: [https://www.rippit.com/pricing](https://www.rippit.com/pricing) 6. Decagon, "Pricing the AI Agent Economy" (Dec. 2024): [https://decagon.ai/blog/pricing-ai-agents](https://decagon.ai/blog/pricing-ai-agents) 7. Decagon, Product overview: [https://decagon.ai/product/overview](https://decagon.ai/product/overview) 8. Rippit, Integrations: [https://www.rippit.com/integrations](https://www.rippit.com/integrations) 9. Decagon, Insights and Reporting: [https://decagon.ai/product/insights-and-reporting](https://decagon.ai/product/insights-and-reporting) 10. Decagon, Watchtower: [https://decagon.ai/product/watchtower](https://decagon.ai/product/watchtower) 11. Decagon, "QA Hub: Agent quality is a team sport" (May 2026): [https://decagon.ai/blog/qa-hub](https://decagon.ai/blog/qa-hub) 12. Rippit, Klaviyo customer story: [https://www.rippit.com/customer-story/klaviyo](https://www.rippit.com/customer-story/klaviyo) 13. Rippit, Conversation Taxonomy playbook: [https://www.rippit.com/playbooks/conversation-taxonomy](https://www.rippit.com/playbooks/conversation-taxonomy) 14. Decagon, "Watchtower: Always-on QA for every conversation" (June 2025): [https://decagon.ai/resources/decagon-watchtower](https://decagon.ai/resources/decagon-watchtower) 15. Decagon, Integrations: [https://decagon.ai/product/integrations](https://decagon.ai/product/integrations) 16. Decagon, "Why MCP alone isn't enough for reliable agent tool use" (Apr. 2026): [https://decagon.ai/blog/getting-the-most-out-of-mcp](https://decagon.ai/blog/getting-the-most-out-of-mcp) 17. 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) 18. Decagon, "Complete AI customer support setup: Tools, integrations, and launch strategy": [https://decagon.ai/blog/ai-customer-support-setup](https://decagon.ai/blog/ai-customer-support-setup) 19. Decagon, "What is resolution-based pricing?": [https://decagon.ai/glossary/what-is-resolution-based-pricing](https://decagon.ai/glossary/what-is-resolution-based-pricing) 20. Rippit, Brex customer story: [https://www.rippit.com/customer-story/brex](https://www.rippit.com/customer-story/brex) 21. Claude Help Center, Get started with custom connectors using remote MCP: [https://support.claude.com/en/articles/11175166-get-started-with-custom-connectors-using-remote-mcp](https://support.claude.com/en/articles/11175166-get-started-with-custom-connectors-using-remote-mcp) 22. Rippit, "MaestroQA is now Rippit": [https://www.rippit.com/news/maestroqa-is-now-rippit](https://www.rippit.com/news/maestroqa-is-now-rippit) 23. Rippit, Checkr customer story: [https://www.rippit.com/customer-story/checkr](https://www.rippit.com/customer-story/checkr) 24. Rippit, The Coverage Benchmark, Part 8: Rippit: [https://www.rippit.com/research/coverage-benchmark-rippit](https://www.rippit.com/research/coverage-benchmark-rippit) 25. 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