Bridgehorn
Introduction
Customer interactions are a critical source of business intelligence for modern enterprises. Contact centers handle thousands of customer conversations daily across sales, technical support, billing, complaints, renewals, and service requests.
Bridgehorn manages large-scale inbound and outbound customer interactions through contact center operations. Although customer calls were recorded and stored, only a small percentage of conversations could be manually reviewed by supervisors due to time and resource limitations.
This limited visibility made it difficult to understand customer sentiment, identify compliance risks, detect churn indicators, and evaluate agent performance consistently.
To address these challenges, Bridgehorn implemented a GenAI-Powered Conversation Intelligence Platform on AWS that automatically analyzes 100% of customer interactions using Artificial Intelligence, Natural Language Processing (NLP), and Generative AI.
The platform transcribes calls, analyzes customer sentiment, generates call summaries, identifies customer intent. detects compliance violations, predicts churn risks, and provides actionable insights through executive dashboards.
Testimony
"Anjana Cloud Services migration strategy was in line with our quality and costing expectations enabling our solutions perform better on AWS cloud which resulted into improved service reliability and increased customer base" — Director, Bridgehorn
Problem Statement
Business Challenges
Bridgehorn faced several operational and customer experience challenges:
• Supervisors could review only 1–2% of recorded calls manually.
• Limited visibility into customer sentiment and satisfaction.
• Difficulty identifying agent coaching and training requirements.
• Compliance violations were often detected too late.
• Manual call review cycles increased operational costs.
• No structured insights available from thousands of customer conversations.
• Customer complaints and churn risks were identified after escalation.
• Lost opportunities for proactive customer engagement.
Technical Challenges
• Call recordings were stored but not analyzed.
• No automated speech-to-text capability.
• No mechanism to identify customer intent or call outcomes.
• Lack of automated quality monitoring.
• No centralized analytics and reporting platform.
• Inability to search and analyze historical conversations efficiently.
Existing Workflow
1. Customer calls were recorded and stored in telephony systems.
2. Supervisors manually selected calls for review.
3. QA teams listened to recordings and completed scorecards.
4. Compliance checks were performed manually.
5. Agent coaching occurred periodically based on sampled calls.
6. Reports were generated using spreadsheets and CRM data.
7. Business insights depended on manual analysis.
Key Challenges
• Only a small percentage of calls were reviewed.
• High manual effort for QA teams.
• Inconsistent quality evaluations.
• Delayed identification of customer dissatisfaction.
• Limited visibility into compliance violations.
• No automated call summaries.
• Lack of churn prediction capabilities.
• No real-time customer intelligence.
AI Solution Overview
Bridgehorn implemented a cloud-native GenAI-Powered Conversation Intelligence Platform on AWS to automatically analyze customer interactions and generate actionable insights.
Data Sources
• Customer call recordings
• Amazon Connect metadata
• Customer interaction history
• Knowledge base documents
• Compliance guidelines
Project Details
USA
Microsoft Workloads Migration Azure to AWS
Software Development Services
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AI Capabilities
Automated Call Transcription
• Speech-to-text conversion
• Speaker identification
• Multi-language support
Sentiment Analysis
• Positive, Neutral, Negative sentiment detection
• Escalation identification
Call Summarization
• Customer issue summary
• Resolution details
• Follow-up actions
Agent Quality Scoring
• Greeting compliance
• Communication effectiveness
• Resolution quality
• Process adherence
Compliance Monitoring
• Script violation detection
• Missing disclosures
• Regulatory compliance checks
Intent & Topic Detection
• Sales inquiries
• Technical support
• Billing issues
• Complaints
• Cancellation requests
Churn Risk Detection
• Frustrated customer identification
• Repeat complaint analysis
• Cancellation risk prediction
Executive Analytics
• Agent performance trends
• Customer sentiment insights
• Compliance scorecards
• Business dashboards
Solution Architecture Overview
The solution uses a cloud-native AWS architecture to automate call analysis, AI-driven insights, and reporting.
Core Components
• Amazon Connect – Manages inbound/outbound calls, IVR, routing, and call recording.
• Amazon S3 – Stores call recordings, transcripts, and analytics data.
• Amazon Transcribe – Converts customer conversations into searchable text.
• Amazon Comprehend – Analyzes customer sentiment and conversation tone.
• Amazon Bedrock (Claude, Llama, Nova, Mistral) – Generates call summaries, detects intent, scores agents, monitors compliance, and predicts churn risks.
• Amazon Bedrock Knowledge Bases – Provides contextual insights using SOPs, product manuals, and compliance documents.
• Amazon OpenSearch Service – Enables transcript search and trend analysis.
• AWS Lambda – Triggers automated AI workflows and integrations.
• AWS Step Functions – Orchestrates transcription, AI analysis, and reporting processes.
• Amazon Athena & QuickSight – Deliver analytics, dashboards, and business insights.
• AWS VPN, Amazon VPC & Amazon CloudWatch – Ensure secure connectivity, network isolation, monitoring, logging, and alerts.
This architecture enables automated conversation analysis, real-time insights, compliance monitoring, and executive reporting across the contact center.
Architecture Diagram
Business Impact & Outcomes
Quantifiable Results
• Call Review Coverage increased from 1–2% to 100% through automated analysis.
• QA Review Time reduced from several days to a few minutes.
• Compliance Monitoring shifted from manual reviews to automated detection.
• Customer Sentiment Tracking evolved from not available to real-time visibility.
• Agent Coaching changed from reactive feedback to proactive recommendations.
• Call Summary Creation moved from manual documentation to automated generation.
• Churn Risk Detection improved from no visibility to AI-driven prediction.
• Operational Costs were significantly reduced through automation and streamlined processes.
Business Benefits
• 100% call analysis coverage.
• Significant reduction in manual QA effort.
• Faster identification of customer dissatisfaction.
• Improved agent performance through proactive coaching.
• Automated compliance monitoring.
• Reduced operational costs.
• Faster decision-making using real-time analytics.
• Better customer retention through churn prediction.
Conclusion
The GenAI-Powered Conversation Intelligence Platform enabled Bridgehorn to transform its contact center operations through intelligent automation and AI-driven insights.
By leveraging Amazon Connect, Amazon Transcribe, Amazon Comprehend, Amazon Bedrock, OpenSearch, Athena, and QuickSight, Bridgehorn achieved complete visibility into customer conversations, automated quality monitoring, improved compliance governance, and proactive customer engagement.
The platform also establishes a strong foundation for future enhancements such as:
• Real-time agent assistance
• Multilingual conversational AI
• Predictive customer behavior analytics
• Voice biometrics
• AI-driven customer journey optimization
• Automated next-best-action recommendations
