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Development of AI-Powered Call Data Extraction and Predictive Analytics Platform
  1. case
  2. Development of AI-Powered Call Data Extraction and Predictive Analytics Platform

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Development of AI-Powered Call Data Extraction and Predictive Analytics Platform

thinktoshare.com
Business services
Information technology

Challenges in BPO and Customer Care Services

Manual data entry processes in BPO and call center operations lead to high error rates, excessive time consumption, and reduced executive productivity. The industry lacks automated solutions for real-time data extraction, sentiment analysis, and predictive client targeting during customer calls.

About the Client

IT solutions provider specializing in AI and automation for customer service operations

Key Objectives for the AI-Powered Solution

  • Automate call data extraction with 90%+ accuracy
  • Enable real-time sentiment analysis during calls
  • Implement predictive analytics for client shortlisting
  • Generate customizable performance reports
  • Reduce average call duration by 50%+

Core System Functionalities

  • Real-time speech-to-text transcription with dialect/accent adaptation
  • Automated customer data extraction during live calls
  • Sentiment analysis dashboard for call monitoring
  • Predictive analytics engine for client targeting
  • Customizable performance reporting with demographic segmentation
  • API integration for existing BPO systems

Technology Stack Requirements

Machine Learning (ML) frameworks
Natural Language Processing (NLP)
Cloud infrastructure (AWS/Azure)
Real-time data processing engines
Customizable ML models for accuracy improvement

System Integration Needs

  • CRM systems (Salesforce, HubSpot)
  • Telephony platforms (Twilio, RingCentral)
  • Enterprise call center software
  • Data visualization tools

Performance and Scalability Requirements

  • 99.9% system uptime SLA
  • Sub-2 minute transcription latency
  • Horizontal scalability for 10M+ monthly calls
  • Data encryption and GDPR compliance
  • Multi-tenancy architecture for enterprise clients

Expected Business Impact of the AI-Powered Solution

Projected 53% reduction in average call duration, 213% improvement in client shortlisting accuracy, and 91%+ data extraction accuracy. Enables BPOs to increase call volume capacity while reducing operational costs associated with manual data entry and inefficient targeting.

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