Advancing Call Center Quality Assurance with AI
AQUA is an AI-fueled quality control tool for contact centers that transcribes, evaluates, scores, and generates coaching insights from call recordings.
Industry Challenge: Subjective and Low-Volume Manual Call Reviews
Customer support specialists face the daily need to manually review, analyze, and document all calls and inquiries to evaluate the quality of services provided. This process is highly time-consuming and tedious, requiring extra effort from human agents that could be redirected to more important tasks if assisted by an AI call center QA system that ensures automated call quality monitoring and support call analysis.
So, in cooperation with TEAM's Managed and Professional Services Studio, which directly runs a customer support call center, our artificial intelligence team decided to transform manual QA processes into a digitalized, AI-assisted workflow driven by speech analytics software for automated call transcription.
The Technical Approach: From PoC to MVP
Originating from a concrete operational need for robust customer service QA tools within the Managed Services Department, this project accelerated during TEAM International's Hackathon, which focused on AI-assisted development.
Rapid prototyping and iterative development
Productization progressed in stages. After the Hackathon, the AI Studio continued working on the product, enhancing its back end, front end, and infrastructure while retaining the same AI call-scoring core. The first PoC covered automated call transcription, QA scoring, AI conversational analytics for issue detection, and structured outputs for call quality assessment.
- 1Creating the AI call scoring core first, with the full product built around it
- 2Enabling the system to handle larger and more complex datasets
- 3Expanding from single-call to multi-call analysis, summarization, and historical analytics
- 4Aligning business expectations (KPIs, performance, cost) with technical feasibility
Applying a structured AI quality framework
Leveraging expertise in automation, the Studio implemented a mature AI quality framework including: choosing the right models for optimization, supporting prompt optimization, enhancing the QA framework started for SAMI, training an AI model, structuring data for AI chatbot searchability, introducing a self-service AI chatbot for automated agent coaching, and translating subjective human evaluation into structured, repeatable AI call scoring.
Quality as a Practice
We managed quality deliberately. F1-score was used to evaluate output accuracy, and testing compared different product versions (not just underlying models). Continuous user feedback flowed directly into the backlog, and ongoing requirement elicitation ensured development remained grounded in real usage, not assumptions.
Navigating architectural trade-offs
TEAM's AI Studio demonstrated mature judgment in balancing cost, performance, consistency, and scalability. There was a clear understanding that early decisions — such as where configuration ownership sits — shape future iteration speed and flexibility.
The Solution: AI-Assisted Transcription, Scoring, and Analysis
To eliminate legacy processes that limited scalability due to the need to manually listen to and evaluate customer support calls, we delivered AQUA — an AI-powered quality control product for contact centers that leverages speech analytics software.
Evaluating calls against QA criteria
AQUA transcribes call recordings, evaluates them against predefined QA criteria, and assigns unbiased scores. The product not just reduces the manual effort of customer support specialists but also accelerates review processes, while still keeping human judgment in control and consistently improving over time.
Identifying issues and generating insights
Our AI call center QA system flags compliance and behavioral issues and generates structured outputs, including call summaries and automated agent coaching insights.
Process Flow
The Impact: Preparing for Scalable QA Operations
Supervisor time freed from manual QA reviews
Rapid productization journey
Scoring, summarization & coaching insights
Automated call quality monitoring frees up to 30–50% of customer support supervisors' time. This gain shifts effort away from merely transactional QA work toward coaching, real-time support, and more efficient performance management.
The overall impact is not only time saved, but a structural change in how quality management operates — moving from manual review to continuous, insight-driven oversight.
Key Benefits
- Building internal capability in applying AI to operational quality control
- Enabling AI-assisted call summarization, scoring, and issue detection in one workflow
- Applying AI call scoring to improve consistency and quality of evaluation vs. manual scoring
- Reducing manual effort and time spent reviewing calls and documenting evaluations
- Accelerating insight generation and feedback cycles
- Reducing subjectivity and increasing consistency across teams, lowering calibration overhead
Technologies Used
Explore More from TEAM International's AI Studio
Case study: Automating resume parsing and ranking with AI (CiviSynch)
Discover how automated resume data extraction and AI candidate ranking reduced the time to manually process one CV from 15–30 minutes to under 5 minutes, allowing specialists to process 150+ CVs in an hour.