How Gabb Scaled QA Coverage to 75% and Raised CSAT to 93%

Overview

Gabb is a US telco company built for families. Because its customers are parents and young kids, the support experience carries a higher bar for quality, trust, empathy, and clarity. Gabb needed a way to understand customer and agent feedback at scale, improve support quality, and surface product insights from every conversation.

The Challenge

Before Oversai, Gabb had a dedicated 14-person QA team, but manual review could only cover a small slice of the operation.

• •Gabb served families with young kids, where support quality and trust had especially high stakes.
• •A 14-person QA team could grade only about 2% of agent interactions across 200 agents.
• •The team had no AI supporting the QA process and little to no voice-of-customer insight.
• •Leaders needed clearer feedback from both customers and agents to improve support, product decisions, and growth.

Why Gabb Chose Oversai

Gabb chose Oversai because the platform had a strong foundation and the team was willing to build with them. Rather than forcing Gabb into a fixed workflow, Oversai listened to what mattered most, showed what was already possible, and incorporated Gabb's ideas into the product.

The Solution

Today, Gabb pairs human grading with Oversai AI to improve customer experience, agent performance, and company growth.

• •Human grading paired with Oversai AI to improve customer experience, agent performance, and company growth.
• •AI processors for bot performance, first contact resolution, churn and retention signals, retention offers, cancellation reasons, and churn-prevention behavior.
• •AI metrics to answer product and customer questions, including child-safety contact drivers and cancellation trends.
• •AI tagging and sentiment tagging to organize support issues into clear reports and reveal the voice of Gabb customers.
• •Coaching workflows that help human graders compile agent interactions into targeted coaching for agents and supervisors.

Impact & Results

Oversai helped Gabb transform QA from limited manual sampling into a broader system for KPI measurement, coaching, and customer sentiment insight.

• •Gabb and Oversai built a QA operating system that allowed the team to reduce QA staffing by 50%.
• •The team moved from grading about 2% of interactions to grading almost 75% of contacts.
• •AI processors, AI metrics, and AI tagging created stronger KPI measurement and deeper visibility into coaching opportunities.
• •Oversai data helped Gabb identify agent empathy as a key lever for raising CSAT and NPS.
• •After empathy coaching and CSAT macro improvements, CSAT increased from 78% to a consistent 93%.
• •Supervisors gained clearer coaching trends while still applying their personal knowledge of each agent.

Looking Forward

Gabb is building a churn and retention processor that will identify retention offers, reveal why customers cancel, and flag whether agents are helping prevent churn. The same system is helping Gabb answer larger product questions, including whether child-safety concerns or cancellation signals are rising across support conversations.