Rippit Reviews 

We use Rippit as the backbone of our support analytics at MSI (Meevo), and it's changed what our ticket data actually does for us.

Before Rippit, every support conversation was a closed loop — a ticket got resolved and the insight died with it. Now we run structured worksheets and AI enrichment across the full conversation history to answer questions our helpdesk was never built to answer: which accounts are showing early churn signals, where our chatbot hands off to humans and why, what's actually driving contact volume versus what we assume is driving it, and which support interactions are quietly building or eroding trust with a customer.

The enrichment layer is the differentiator. Instead of scoring agents on a canned rubric, we tag every ticket for root cause, avoidability, resolution quality, and risk — then roll that up into programs that feed our churn prediction model and escalation monitoring. That's a fundamentally different posture: from reactive QA to proactive account intelligence.

It also plugs cleanly into the rest of our stack. We pull signal from Rippit alongside Salesforce, Gainsight, and Pendo data to build composite risk scores, and Rippit's own connector layer means we're not stuck exporting CSVs to get there.

For a SaaS business focused on efficient growth, this is exactly the kind of tool that lets a lean CX team punch above its headcount — we're getting insight depth that used to require a dedicated analytics hire.

We’ve been using Rippit for several years and have been happy with the platform for our traditional manual QA and coaching workflows. More recently, we’ve been exploring how to take our AI-powered QA approach into production at scale. We initially developed and tested our QA framework using Claude, and Rippit has been working closely with us to operationalize that approach within their platform.

What has impressed us most is the team’s commitment to adapting the product to our specific needs. They’ve taken the time to understand what we’ve built, listen to our feedback, and work with us to operationalize our existing QA logic and setup rather than asking us to completely change our approach to fit the product. Their level of engagement and willingness to iterate has been a real strength.

We see a lot of potential in combining Rippit’s production and analytics capabilities with our AI QA framework, with the ultimate goal of monitoring a much larger volume of interactions, consistently identifying CX issues and trends, and turning those insights into improvements that help us maintain a high level of QoS.

We use Rippit's Claude Connected App to turn our support conversation data into workflows we actually run, not just dashboards we glance at. On top of it we've built a weighted AutoQA scoring model, a bot-vs-human gap analysis that tells us where our chatbot needs work, an escalation-risk scanner, and a weekly digest that turns tickets into a bug list and feature-request list for our product team. Being able to query and enrich our own ticket data directly from Claude, without exporting anything first, has turned QA and analytics from a sampled, manual process into something we can run on our entire ticket population. Recommend it to any CX team that wants more than a static reporting layer.