Case Studies

Case Studies

Ather Energy Saves 100K+ Agent Hours Annually with AI-Powered Call Summarization

Editor’s Note: Originally featured by BootLabs on Google Cloud, this customer success story has been republished by Quality Kiosk with additional business context, implementation insights, and outcomes.

Industry & Segment

Automotive & Manufacturing

Objective

To transform Ather Energy’s contact center operations through automated call summarization and sentiment analysis for reduced manual effort, improved data consistency, and actionable customer insights.

Engagement Overview

Ather Energy, India’s leading smart and connected electric scooter manufacturer, partnered with Bootlabs, part of QualityKiosk Technologies, to implement AI-driven contact center transformation. Founded in 2013, Ather faced scalability challenges as their customer base grew with the Ather 450 line. QualityKiosk 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

High-volume manual call disposition workload

Ather'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.

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QualityKiosk Technologies 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 metrics

Analytics Foundation for Customer Insights:

Enabled pattern detection across customer conversations to identify trends
Structured conversational data for root cause analysis and product feedback

QK's Strategy

API contract & integration validation

All snap‑ins were validated against defined API contracts, authentication mechanisms, and rate‑limiting policies to ensure consistent and predictable behavior across diverse third‑party systems, including engineering, CRM, collaboration, and communication platforms.

Test‑driven development & coverage validation

Each connector followed a test‑driven development approach with a minimum 80% unit test coverage, ensuring functional correctness, regression safety, and production stability across monthly release cycles.

Enterprise‑scale performance validation

The marketplace was validated using large‑scale synthetic and real‑world data volumes, including 10M+ Slack records, to confirm throughput, latency, and stability under enterprise workloads without performance degradation.

Phase‑gated delivery validation

A strict phase‑gated delivery model was enforced, covering API exploration, TDD sign‑off, object mapping, coding with unit tests, automated regression, and release validation to ensure speed never compromised quality or reliability.

AI‑augmented quality controls

AI‑assisted workflows validated code quality through automated reviews, documentation checks, root‑cause analysis, and defect resolution, reducing manual errors while maintaining engineering rigor and consistency.

Operational SLA & reliability validation

All connectors were validated against sub‑24‑hour SLA requirements for high‑volume synchronization jobs, ensuring reliability for enterprise customers with stringent performance expectations.

Regression & release stability validation

An intelligent automation layer continuously updated SDKs, generated pull requests, and executed regression suites to validate backward compatibility and prevent connector drift across ongoing releases.

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, enabling instant insights

Zero

manual entry errors in disposition categorization, ensuring data integrity

QualityKiosk'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 Ather to scale GenAI capabilities across customer service operations

Delivered measurable business value and structured intelligence from every conversation

TESTIMONIALS

Testimonial

With Bootlabs and Google’s innovative GenAI solutions, we were able to explore the true potential of GenAI beyond mere hype. Through our use case, we’ve successfully tapped into conversational data, enabling us to gain invaluable customer insights. I am confident that this success will propel us towards even greater heights of customer satisfaction and operational excellence.