A Leading Electric Mobility Company Saves 100K+ Agent Hours Annually with AI-Powered Call Summarization
Industry & Segment
Automotive & Manufacturing
Objective
To transform the company’s contact center operations through automated call summarization and sentiment analysis for reduced manual effort, improved data consistency, and actionable customer insights.
Engagement Overview
A leading electric mobility company partnered with QKTech to implement AI-driven contact center transformation. As its customer base scaled rapidly across a growing product portfolio, the company faced scalability challenges in its contact center operations. QKTech deployed Google’s Generative AI and Text-to-Speech models integrated directly with Salesforce CRM to automate conversation summarization, extract customer sentiment, and enable systematic customer interaction analytics.
Business Challenges
Growth exposed three problems in how the contact center recorded and used its calls.
High-volume manual call disposition workload
The company’s contact center agents handled significant incoming support call volumes, with each call requiring manual summary creation and disposition tagging. This process extended average handle time, reduced agent productivity, and created workflow bottlenecks during peak periods.
Inconsistent summary quality and data loss
Manual summarization resulted in variable quality across agents, with critical customer insights, sentiment indicators, and actionable discussion points often missing or inadequately captured.
Operational inefficiencies and reporting gaps
The lack of structured, analyzable conversational data prevented systematic pattern detection across customer interactions. Manual errors and inconsistencies created analytics challenges, limiting the ability to uncover customer behavior trends, predict service needs, or measure agent performance objectively.
QKTech Approach
We deployed an end-to-end AI-driven contact center automation solution combining Google Cloud’s GenAI capabilities with seamless CRM integration in phases for operational readiness and business impact validation.
Discovery and process assessment
- Analyzed current call handling workflows and manual disposition processes
- Identified disposition categories, agent effort patterns, error rates, and reporting gaps
- Validated integration points with telephony platforms and Salesforce CRM
AI model design and configuration
- Automated conversation summarization using Google Generative AI models
- Configured Natural Language Understanding for intent detection and outcome classification
- Real-time extraction of customer sentiment and discussion highlights
Salesforce CRM integration
- Direct integration to automatically update Salesforce records with structured summaries
- Enabled real-time data availability for supervisor dashboards and quality monitoring
Pilot validation and model tuning
- Ran AI-driven disposition in parallel with manual tagging to validate accuracy
- Measured false positive rates, agent override frequency, and summary quality
- Refined models based on agent feedback and operational performance metric
Analytics foundation for customer insights
- Enabled pattern detection across customer conversations to identify trends
- Structured conversational data for root cause analysis and product feedback
Testing Excellence Metrics
90%
reduction in call disposition time, freeing agents to focus on customer problem resolution
80%
decrease in mis-tagged calls, improving reporting accuracy and analytics quality
100,000+
agent hours saved annually, redirecting effort from manual tasks to value-added service
100%
standardized call summary format, eliminating quality variation across agents
Real-time
Salesforce updates with structured conversation data, giving teams instant insights
Zero
manual entry errors in disposition categorization, ensuring data integrity
QKTech’s Approach and Solution
- Reduced operational costs, improved agent productivity, and redirected efforts toward complex customer issues requiring human expertise
- Customer insights informed product development priorities, service improvement initiatives, and customer experience strategy with data-driven evidence
- Correctly tagged calls improved analytics accuracy, enabled leadership to identify emerging issues faster, and allocated resources more effectively
- Established an AI-driven automation foundation that positioned the company to scale GenAI capabilities across customer service operations
- Delivered measurable business value and structured intelligence from every conversation
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