We analyzed the client’s customer support operations and call management workflow, where high call volumes were handled without any system to analyze conversations or detect recurring issues. Based on this gap, we designed an AI-powered solution to automatically process call data and generate searchable insights.
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Smart Call Analysis & Search System
Industry
E-Commerce
Region
Australia
Project Size
Non-Disclosable
Overview
Customer support teams generate huge volumes of call data every day, but most businesses store recordings without structured insights. Datumquest built an AI-powered system that automatically analyzes customer calls, extracts key insights, and enables teams to search conversations using natural language queries.
Core Feature
AI-powered automation that analyzes customer calls, insights, and enables intelligent search across conversations in real time.
- Automated Call Recording Processing And Transcription
- AI-Based Sentiment Analysis For Customer Experience Insights
- Smart Call Categorization By Issue Type And Intent
- Product And Brand Mention Detection From Conversations
- Semantic Search For Finding Calls Using Natural Language
- Centralized Call Data Storage For Analytics And Reporting
Technologies
Built with Industry-Leading Tools
Azure Blog
n8n
Vector DB
Webhooks
Google Sheets
AI/NLP
Outcomes
Significantly reduced manual call review time for support teams.
Enabled managers to instantly locate specific call types and issues.
Clear visibility into delivery issues and compatibility queries.
Enhanced customer service quality through better insights.
Supported more effective training and coaching for support teams.
Provided valuable product feedback and issue analysis.
The Challenges
01
Unsearchable Call Data
Customer call recordings were stored but lacked search and analytical capabilities, limiting their usefulness for business insights.
Limited Visibility into Customer Issues
Managers lacked clear visibility into recurring customer issues and escalation patterns across recorded calls.
02
Manual Call Review Process
Support teams spent significant time manually listening to call recordings for audits and training purposes.
03
Key Customer Inquiry Types
Queries related to product compatibility, order tracking, delivery delays, and warranty requests
04
Solution by Datumquest
Developed an automated workflow to process and analyze customer call recordings.
Used AI/NLP models to extract meaningful insights from conversations.
Stored processed data in a vector database for intelligent search.
Enabled semantic search to find calls based on meaning, not just keywords.
Project Timeline
A structured 18-week journey from concept to deployment
Discovery
Research & Planning
2 weeks
Development
Core System Build
8 weeks
Integration
AI Model Training
4 weeks
Testing
QA & Optimization
3 weeks
Launch
Deployment & Rollout
1 week
Requirement Analysis
Key Activities
- Reviewed existing customer support workflows.
- Detected issues in manual call review and tracking.
- Analyzed call recording storage and data accessibility
- Defined goals for AI-driven call analysis and insights.
Data Collection & Processing
In this phase, we set up infrastructure to capture and securely store customer call recordings and transcripts in the cloud. This centralized the data and prepared it for automated AI processing and analysis.
Key Activities
- Stored call recordings and transcripts in cloud storage
- Prepared raw call data for analysis
- Set up pipelines to capture new call data
- Ensured secure storage and data access
AI/NLP Development
During this phase, we built an AI system to analyze customer conversations and extract key insights. Using NLP models, it detects sentiment, issue categories, and product mentions from call transcripts.
Key Activities
- Built an AI/NLP pipeline for call transcript analysis
- Implemented sentiment detection for customer calls
- Developed issue classification for call types
- Extracted key insights like summaries and product mentions
Semantic Search Integration
We implemented a vector database to enable semantic search, allowing teams to find call insights using natural language queries. The system connects processed call data with internal tools and analytics platforms for easier access and analysis.
Key Activities
- Implemented vector database for semantic search
- Indexed call insights for faster retrieval
- Enabled natural language search across calls
Testing & Deployment
In the final stage, the system was tested with real customer call data to ensure accuracy, performance, and reliability. After optimization, it was deployed to automatically process new calls and generate searchable insights for support teams.
Key Activities
- Tested the system with real call recordings
- Optimized AI models and processing pipelines
- Implemented automated workflow monitoring
- Deployed the system for stable operation
How Datumquest is Making an Impact
Michael Reynolds
Head of Customer Experience
Datumquest helped us transform thousands of call recordings into searchable business intelligence. Their AI-powered call analysis system made it incredibly easy to identify recurring customer issues, improve support quality, and reduce response times.”
Robert Kim
Director of Customer Success
The Smart Call Analysis & Search System developed by Datumquest significantly improved our operational visibility. Instead of manually listening to hours of call recordings, we can now instantly search conversations and uncover key insights within seconds.
James Carter
Product Operations Manager
What impressed us most was how seamlessly the system integrates AI, semantic search, and automation. Our managers can quickly detect patterns in customer complaints and proactively improve the service experience.
Datumquest specializes in AI, automation, data science, and analytics, helping businesses unlock data-driven insights and streamline operations.
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