85%
Speech-to-text accuracy
25%
Accuracy improvement
100k
Monthly interactions
85%
Call-driver identification
The challenge
Tackling data complexity and improving customer experience
A leading Australian bank struggled to effectively analyze thousands of monthly customer interactions across their contact centers. Their existing systems couldn't effectively process and analyze the vast amount of customer conversation data, leading to missed opportunities for service improvement.
Key challenges
Poor transcription accuracy hampered data quality
Manual tagging processes were resource intensive
Natural language processing capabilities were limited
Ability to generate actionable insights from data was limited
The solution
Advanced customer interaction insights platform
Intelligent data processing
Advanced speech-to-text transcription
Automated pattern recognition
AI-powered intent extraction
Enterprise integration
Comprehensive AWS ecosystem integration
Automated governance controls
Scalable cloud architecture
Implementation approach
1
Foundation
AWS infrastructure setup
Integration with existing systems
Amazon EKS deployment
2
Development
Amazon Bedrock implementation
OpenSearch Service configuration
Dashboard development
3
Deployment
Continuous refinement
Systematic platform rollout
Continuous performance monitoring
The impact
Unlocking a comprehensive, data-driven transformation
Speech-to-text
85%
Accuracy rate
Enhanced transcription quality
Improved data quality
Real-time analytics capability
Operational efficiency
100%
Reduction in manual tagging
Automated intent extraction
Reduced manual effort
Streamlined analysis
Call analysis
85%
Call-driver identification
Identification of interaction causes
Enhanced customer issue resolution
Pattern recognition
Looking ahead
Enhanced AI models and systems
Expanding predictive capabilities
Platform evolution continues
Integration of new data sources
Market expansion opportunities
Available on AWS Marketplace
