How TeamWhale Scaled QA Efficiency 6x, Moving from Playvox to AI-First Quality with Oversai

The Challenge

As TeamWhale continued to scale high-touch support for a global client base, their QA program hit practical limits:

• •Low QA coverage: Only a small percentage (1-5%) of interactions were sampled, creating blind spots in service quality for fast-growing programs.
• •Previous platform constraints: Their previous QA vendor lacked AI-native features to automatically surface improvement opportunities and risk signals across large volumes.
• •Scale pressure: Multiple clients onboarding in parallel meant QA needed to scale predictably, without adding headcount or changing agents' day-to-day tools.

Why Oversai

TeamWhale chose Oversai to upgrade from a legacy QA tool to an AI-first quality operating system:

• •AutoQA of conversations to expand coverage instantly and surface patterns by topic, intent, and compliance.
• •Manual QA superpowers via a Chrome extension so evaluators stay in their native systems (billing, Slack, ticketing, CRM) and complete reviews faster, without tab fatigue.
• •Out-of-the-box integrations with Intercom and LiveChat, plus open APIs to connect AppFollow (app store reviews) and Statusbrew (social) for unified auditing across social and app marketplaces.
• •Partner-level support from Oversai: rapid iteration on feedback, proactive check-ins, and roadmap alignment.

The Solution

Oversai and TeamWhale deployed a combined AutoQA + Manual QA program to make the difference:

• •AI-First Coverage: AutoQA removed the trade-off between scale and visibility—every conversation is now auditable.
• •In-Context Manual QA: Evaluators stay in their natural workspace (billing, Slack, ticketing), reducing evaluation time and context switching.
• •"Needles in the Haystack" Discovery: AI clusters and outlier detection uncovered rare but costly failures that manual sampling missed.
• •Partnership, Not Just Software: Oversai's team acted as an extension of QA, responsive support, fast iteration on feedback, and shared success metrics.

Impact & Results

The implementation delivered significant improvements across multiple areas:

• •QA coverage increased 6x! From 50 to 300 evaluations per agent, driven by AutoQA eliminating blind spots.
• •Time to complete a manual evaluation decreased by 10% thanks to the Chrome extension and in-context reviews.
• •Risk detection improved with AI surfacing of low-frequency, high-impact cases (refund risk, compliance missteps, churn language).
• •Business visibility: Ops leaders gained unified reporting across chat, social, and app reviews, tying QA outcomes to CSAT, resolution, and retention.

Looking Ahead

With the foundation in place, TeamWhale is expanding QA automation to new lines of business, enriching rubrics with client-specific intents, and tying QA insights to better reporting, more blind-spots detection, sentiment analysis, and Oversai will continue to partner on roadmap items, advanced sentiment modeling, and generative coaching tips, to keep TeamWhale's service quality a competitive advantage.