How it Works

How Rippit Analyzes 100% of Your Customer Conversations

Rippit connects to tools like Zendesk, Intercom and Gong, reads every conversation with AI, and turns it into structured data you can query in plain language, from churn risk and QA scores to contact drivers and compliance flags. No sampling, no engineers needed.
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Crunch conversations like numbers in excel

Conversation data is scattered across your business — buried in CRM notes, customer calls, support tickets, and AI chats.

For the first time, AI can calculate conversations like numbers and unlock a new class of business insight.

Rippit turns unstructured conversations into structured, queryable data — instantly.

It’s 10× easier to use, 10× more powerful, and dramatically more cost-effective than legacy platforms like Qualtrics and Medallia.

Unlike DIY stacks built on Snowflake, Databricks, BigQuery, or Redshift, Rippit gets teams up and running fast — no engineering lift required.

You can try to analyze conversations in Google Sheets, ChatGPT, Gemini, NotebookLM, or Claude. But those tools weren’t built to operationalize conversation data at scale. Rippit was.

How Rippit works

Step 01

Ingest

Step 02

Model

Step 03

Analyze

Step 04

Democratize

Ingest

Every chef I know, if you asked them, 'If you had to spend the rest of your life in one country, eating one country's food for the rest of your life, where would that be?' They’re all gonna say the same thing: Japan. Tokyo. Period. For me, that’s an argument ender.

Out-of-the-box Sources

35+ One Click Integrations

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Data Warehouses

Sync existing data from Snowflake, Databricks, BigQuery, or Redshift.

Documentation

API

Push data directly from any source with our developer SDKs.

Documentation

Model

There are three primary ways to model conversation data to generate higher-quality insights

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Analyze

Fast analysis on small conversation datasets

Large scale analysis on 100% of conversation data

I’m in the empire business.

Democratize

A UI every employee understands

Individually, we are one drop. Together, we are an ocean.

The case for centralized data

Collecting feedback on a new product launch is nearly impossible when your data is fragmented. When support logs, reviews, and sales calls live in silos, you’re forced to reconcile different AI interpretations and incomplete datasets. By bringing everything into one place, you move from "guessing" to a truly holistic understanding.