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 across chat, email and voice.[1] Rippit is built for one job: analyzing and acting on every customer conversation at scale.[2] It reads those conversations, plus your human-agent tickets, turns what it finds into structured data,[3] and shows whether issues were actually resolved and what to change. Rippit also runs AI agents of its own that monitor, analyze and act on that data;[2] they work in the background, not real-time with your customers.

Teams keep Decagon to run the AI agent and add Rippit as an independent layer that reads 100% of conversations.[4] Rippit publishes its pricing.[5] Decagon prices per conversation or per resolution.[6]

Where Rippit and Decagon overlap is Decagon’s conversation analytics capabilities.

What's the difference between Rippit and Decagon?

Decagon resolves customer conversations. Rippit analyzes them.

Decagon is an AI agent platform for customer service.[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]

Rippit is AI conversation analytics and agents, built for scale.[2] It connects to all of your conversation sources, analyzes every conversation, whether a bot or a person handled it,[2] and turns what it finds (intent, root cause, sentiment, churn signal, resolution) into fields you can filter, trend and action on.[13] Rippit's agents then monitor those fields, analyze them and act on what they find.[2] They work behind the scenes on your conversation 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. Rippit can also take this further: Rippit agents can draft SOPs based on the results, for your team to use in improving Decagon.

How does Rippit analyze every conversation?

1,000 conversations or 1,000,000, bot and human alike: Rippit reads every one.[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 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] 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] 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:

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] and hands escalations to human agents with the complete chat history.[18] Rippit connects to Zendesk, Intercom, Gong and Granola in one click and analyzes the conversations recorded there.[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]

Using both fixes two common gaps:

• Bot reporting lives inside the bot platform. Decagon's reporting measures performance within Decagon.[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]
• 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]

Decagon's agents keep talking to your customers, while Rippit does the mass-scale enrichment behind them: it analyzes every conversation in scope,[2] stores each judgment as a field on that conversation,[13] and runs ad hoc deep dives on up to 10,000 conversations at a time for questions no existing field answers yet.[3]

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]

Rippit measures resolution by reading the conversation itself. Resolution is one of the dimensions AI tags on every conversation in one pass, alongside intent, root cause, sentiment, urgency and churn signal.[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]

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] Insights & Reporting tracks CSAT and deflection, clusters conversations into topics, and answers questions like "Why are customers requesting refunds?"[9] QA Hub gives teams a workspace to review AI-agent quality with custom rubrics.[11]

Rippit sits across tools - from your helpdesk,[2] so Decagon conversations and human-agent tickets get the same classification: intent, root cause, sentiment, churn signal and resolution.[13] It is also built for complex analytics and background Agents to support a wider variety of use cases.

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]

How much do Rippit and Decagon cost?

Rippit publishes its pricing, and paid plans bill AI credits at cost.[5] Decagon doesn't publish a price list, but it describes two pricing models.[6]

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]
• 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]

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