How a Consumer Brand Designed a Multilingual Call Analytics Platform on Azure
Discover how a high-volume consumer brand built an AI-powered call quality monitoring system to analyze sentiment, intent, customer satisfaction, and agent performance across 20,000+ monthly customer conversations.
20,000+
Customer Calls Analyzed Per Month
100%
Centralized Visibility Across Call Channels
Real-Time
Sentiment, Intent & Quality Insights
WebQnA answers safety-related questions very well from the documents shared. The WebQnA team is really quick at implementing any customizations we needed to integrate with our systems.
Omer Miranda
Head of Technology, JSW Steel
THE CHALLENGE
A growing consumer-facing business handling more than 20,000 monthly customer interactions faced increasing difficulty maintaining quality oversight across sales and support operations.
As call volumes grew, manual quality assurance processes became increasingly unsustainable. Teams could only review a small percentage of conversations, leaving significant blind spots around customer sentiment, service quality, agent coaching opportunities, and conversion trends.
The organization faced several operational challenges:
Fragmented Call Data
Customer conversations were distributed across multiple telephony systems, making centralized analysis difficult.
Limited Sentiment Visibility
Leadership lacked a structured way to identify customer satisfaction trends, recurring complaints, and service issues.
Manual QA Bottlenecks
Traditional sampling methods reviewed only a small fraction of total calls, reducing overall QA coverage.
Multilingual Complexity
Customer conversations occurred in Indian English and multiple regional languages, creating additional challenges for consistent quality monitoring.
Lack of Actionable Insights
Raw recordings and transcripts provided little value without structured analysis, scoring, and reporting.
The company needed a scalable solution capable of transforming thousands of customer conversations into actionable operational intelligence.
THE solution
An AI-Powered Call Analytics Platform Built for Modern Customer Operations
To help leadership gain visibility into customer interactions at scale, Punctuations designed a cloud-native call quality monitoring platform built entirely within the client's Azure ecosystem.
The solution unified recordings, transcripts, metadata, sentiment analysis, quality scoring, and dashboard reporting into a single operational platform.
The architecture combined Azure-native services with advanced AI models to create an end-to-end customer conversation intelligence platform.
Multi-Channel Call Ingestion
The platform ingests call recordings, transcripts, and metadata from multiple telephony providers into a centralized analytics pipeline.
AI-Powered Speech Transcription
Azure AI Speech converts customer conversations into searchable text while supporting Indian English and regional language interactions.
Sentiment Analysis
Each conversation is analyzed at both utterance and call level to identify positive, neutral, and negative customer sentiment patterns.
Intent Detection
The system automatically classifies conversations into categories such as:
- Sales enquiries
- Product questions
- Customer complaints
- Delivery updates
- Service requests
Quality Scoring
Calls are evaluated against configurable quality metrics including:
- Agent tone
- Communication clarity
- Product knowledge
- Customer enthusiasm
- Satisfaction indicators
Conversion Likelihood Prediction
AI models identify signals associated with successful sales outcomes and high-conversion conversations.
Unified Analytics Dashboard
Supervisors gain access to:
- Sentiment distribution trends
- Agent performance analytics
- Language mix reporting
- Channel-based comparisons
- Flagged call reviews
- Historical performance tracking
Azure-Native Deployment
The solution leverages:
- Azure OpenAI Service
- Azure AI Speech
- Azure SQL Database
- Azure Blob Storage
- FastAPI Backend
- Vue.js Frontend
allowing the organization to remain fully aligned with existing cloud governance and security requirements.
The Impact
By implementing an AI-driven approach to call quality monitoring, the organization established a foundation for data-driven customer experience management.
Faster Quality Reviews
Thousands of customer interactions can now be analyzed automatically, significantly increasing QA coverage.
Improved Customer Experience Visibility
Leadership gains continuous visibility into customer sentiment, complaints, and satisfaction trends.
Better Agent Coaching
Supervisors can identify coaching opportunities using objective conversation data rather than limited manual sampling.
Earlier Detection of Service Issues
Recurring customer concerns become visible much sooner, enabling proactive operational improvements.
Stronger Business Intelligence
Structured conversation data becomes available for CRM enrichment, reporting, and executive decision-making.
Scalable QA Operations
The organization reduces reliance on manual call reviews while improving overall monitoring consistency.
Elevated Employee Engagement: By removing the language barrier, usage of safety resources skyrocketed. Frontline workers who previously felt alienated by English documentation now actively query the bot in their native languages.
Enhanced Compliance & Safety On-Site: With 24/7 availability, workers across all shifts can immediately clarify safety protocols before executing high-risk tasks. This immediate access to verified SOPs significantly reduces the risk of operational errors.
Drastic Reduction in Manual Workload: Routine queries regarding safety protocols and manual guidelines are now entirely automated. This frees up JSW’s core safety teams to focus on active field inspections, incident prevention, and strategic safety training rather than administrative troubleshooting.
Data-Driven Safety Insights: Through Pragya’s detailed backend reporting and analytics, management gains deep visibility into employee safety needs. By tracking engagement trends and common queries, JSW can continuously refine its safety manuals and proactively address knowledge gaps across different manufacturing sites.
About Company
The client is a leading consumer-focused retail brand with high-volume sales and support operations across multiple channels.
The organization manages approximately 20,000 customer calls every month, spanning product inquiries, sales consultations, delivery tracking, customer support, and service-related interactions.
INDUSTRY
Retail & Consumer Goods
COMPANY SIZE
Enterprise Organization
CALL VOLUME
20,000+ Monthly Conversations
Relevant proof by workflow problem
We've solved these challenges across highly regulated industries.
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Multilingual Workforce Safety Assistant
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Operations Visibility & Workflow Automation
Unified operational data sources into a real-time AI assistant for faster decision-making and improved logistics visibility.