AI-Powered Automated Call Quality Assessment

Real-time scoring, compliance checks and coaching for contact centers

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About

Industry

Customer Support, BPO, Sales, Contact Centers

Application Type

Intelligent call monitoring and coaching platform

Core Functionality

Automated call scoring, sentiment analysis, compliance tracking, coaching recommendations

The client operates large-scale contact centers where thousands of conversations take place every day. Traditional QA teams could manually review only a small portion of calls, typically 2–5 percent. This created inconsistent scoring, delayed feedback, and frequent compliance misses.

They needed an automated system capable of reviewing every call, understanding tone and sentiment, detecting violations, and giving agents real-time guidance. The solution required advanced speech recognition, LLM-based evaluation, acoustic analysis, and a QA-scoring engine aligned with their internal frameworks.

Results

These improvements highlight how automated QA streamlines operations and lift overall call quality.

Reduced Work

98% reduction in manual QA workload

Complete Coverage

100 percent call coverage, up from 5 percent sampled manually

Precision

92% scoring accuracy compared to human QA

Faster Feedback

3x faster feedback cycles for agents

Better Quality

40% improvement in agent performance due to timely coaching

Fewer Errors

60% reduction in compliance violations thanks to real-time alerts

The automated call quality system transformed our QA operations and gave us visibility we never had before.

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Challenges

Why It Mattered

Call quality directly influences customer satisfaction, retention, and brand perception. Automating QA ensures consistent scoring at scale while reducing costs and giving agents the coaching they need to improve quickly.

Our Approach-

We built an end-to-end AI-driven QA platform that evaluates every call for quality, compliance, and coaching insights in real time.

Built a complete processing flow combining ASR, acoustic analysis, and LLM-based evaluation.

Designed structured scorecards covering communication, compliance, empathy, and accuracy.

Implemented models to detect silence blocks, interruptions, dead air, and escalations.

Created dashboards with trends, comparisons, QA summaries, and compliance alerts.

Developed an automated coaching system that generates insights after every call.

Added scalable microservices to handle multiple languages and regional variations.

Our Tools:

Speech-to-Text

Whisper
DeepSpeech
Google STT

NLP & LLM

GPT-4
LLaMA
BERT models
sentiment classifiers

Acoustic Analysis

Praat
PyDub
audio feature extraction tools

Backend

Python
FastAPI
Node.js

Frontend

React.js
Next.js

Databases

PostgreSQL
ElasticSearch
Redis

DevOps

Docker
AWS Lambda
ECS
CloudWatch

Integrations

CRM platforms
dialers
call recording APIs

Before & After

This comparison shows how AI-driven QA delivers consistent accuracy, faster feedback, and stronger agent performance across every campaign.

Feature / Metric	Before — Manual QA	After — AI-Powered QA
Call Coverage	5% sampled	100% analyzed
QA Workload	High, repetitive	98% automated
Feedback Speed	3–7 days	Instant recommendations
Scoring Accuracy	Inconsistent	92% consistent scoring
Compliance Detection	Often missed	60% fewer violations
Agent Improvement	Slow due to delayed coaching	40% improvement via real-time coaching

Testimonial

A Partner That Took Our QA to the Next Level

The automated call quality system transformed our QA operations and gave us visibility we never had before.” Their technical expertise and understanding of contact-center workflows made the transition seamless. We now deliver faster feedback, maintain consistent scoring, and catch compliance issues before they escalate.

— Director of Quality and Training