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

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

Connect With Team Today

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-

The project began with building a computer-vision spine capable of reading the body accurately and responding 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

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