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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 Used
Azure Blog
NLP Processing
n8n
Vector DB
Webhooks
Google Sheets
Automation Workflows
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
Queries related to product compatibility, order tracking, delivery delays, and warranty requests
02
Call recordings were stored but not searchable or analyzable
03
Hundreds of customer support calls received daily related to orders, delivery, and product compatibility.
04
Managers could not easily identify recurring issues or escalations
Solution by Datumquest
01
Developed an automated workflow to process and analyze customer call recordings.
02
Used AI/NLP models to extract meaningful insights from conversations.
03
Stored processed data in a vector database for intelligent search.
04
Enabled semantic search to find calls based on meaning, not just keywords.
Project Timeline
1/5
Requirement Analysis
At the beginning of the project, we analyzed the client’s customer support operations and call management workflow. The business handled a high volume of calls but lacked a system to analyze conversations and identify recurring issues. This led us to design an AI-powered solution to automatically process call data and generate searchable insights.
Key Activities:
- Studied existing customer support and call management workflows
- Identified challenges in manual call review and issue tracking
- Analyzed call recording storage and data accessibility
- Defined automation goals for AI-driven call analysis and insights
2/5
Data Collection & Processing Setup
In this phase, we established the infrastructure required to capture, store, and process customer call recordings. The system was designed to automatically collect call transcripts and store them securely in cloud storage so they could be analyzed by AI models. This step ensured that all call data could be centralized and prepared for automated processing.
Key Activities:
- Integrated call recordings and transcripts into cloud storage
- Structured raw call data for processing and analysis
- Configured automated pipelines to capture new call data
- Ensured secure storage and data accessibility for processing
3/5
AI/NLP Call Intelligence Development
During this phase, we developed the AI-powered processing system that analyzes customer conversations and extracts meaningful insights. Using Natural Language Processing models, the system identifies sentiment, detects issue categories, and extracts product mentions from call transcripts
Key Activities:
- Built AI/NLP pipeline for call transcript analysis
- Implemented sentiment detection for customer interactions
- Developed issue classification for different call types
- Extracted key conversation insights such as summaries and product mentions
4/5
Semantic Search & Data Integration
To make call insights easily accessible, we implemented a vector database that enables semantic search capabilities. This allows managers and support teams to search conversations using natural language queries rather than keywords. The system connects processed call data with internal tools and analytics platforms.
Key Activities:
- Implemented vector database for semantic search
- Indexed processed call insights for fast retrieval
- Integrated searchable call data with internal systems
- Enabled natural language search across customer calls
5 /5
Testing, Deployment & Optimization
In the final stage, the system was tested using real customer call data to ensure accuracy, performance, and reliability. The workflow was optimized to automatically process new calls, generate insights, and provide searchable results for support teams. After testing, the system was deployed into production
Key Activities:
- Conducted system testing with real call recordings
- Optimized AI models and processing pipelines
- Implemented automated workflow monitoring
- Deployed the solution and ensured stable operation
How Datumquest is Making an Impact
Datumquest specializes in AI, automation, data science, and analytics, helping businesses unlock data-driven insights and streamline operations.
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