Rippit does a great job of giving folks a headstart on various aspects of their interactions with customers. We use it both for QA and for general classification of customer sentiment and reaction. It's been fantastic at helping us understand what's really going on. I believe it is very much worth the price.

One of the biggest upsides of Rippit is that it helps us move away from a classic, checklist-style QA tool and toward real conversation analysis. Rather than simply scoring agents against a fixed rubric on a small sample of tickets, Rippit lets us dig into the substance of every conversation at scale, pulling out root cause, contact reason, resolution quality, and product signal. That shift from “grading agents” to “understanding what’s actually happening in tickets” brings forward insights like avoidable volume, at-risk accounts, and recurring product issues things a traditional QA scorecard would never catch.

What I like best is the depth of Rippit's conversation analytics — and how easily I can get to it through the Claude integration. Instead of hand-scoring a handful of tickets, I ask a question in plain English and Rippit reads the full transcript of every conversation, chat and phone alike, and makes a real judgment on each one. It isn't keyword matching or skimming subject lines; it understands what actually happened in the conversation. The example that sold me: I needed to catch escalations brewing across thousands of support conversations. Rippit tagged every single one for severity and whether the issue got resolved, then surfaced the critical, unresolved cases — including a phone call whose subject line gave nothing away but whose transcript was clearly urgent. A tag- or subject-based system would have missed it, and finding it by hand at that volume just isn't possible.