HomeSuccess StoriesSoftware developmentAI-powered processes accelerate release cycles by 40% for a leading call center platform

AI-powered processes accelerate release cycles by 40% for a leading call center platform

Success Highlights

30% scalability improvement with a cloud-native microservices architecture

25% increase in customer satisfaction through AI optimization and better UX

40% faster release cycles powered by full CI/CD automation

Key Details

Industry: Call Center Technology Geographies: United States

Platform: AI powered support, Cloud-based containerized architecture, integrated with web, desktop, and external systems

Business Challenge

Our client, a leading AI-driven call center platform, needed to modernize their system to keep up with rising customer demand. Slow updates, high maintenance effort, and heavy cross-team coordination made releases unreliable and hard to scale, creating four key blockers.

Slow and Inconsistent AI Performance. The AI assistant responded slowly, produced inaccurate suggestions, and struggled with natural conversations—hurt­ing agent productivity and customer experience.
Outdated, Hard-to-Use Interface. A non-intuitive UI forced agents to work around the system, slowing workflows and limiting adoption of AI features.
Data Limitations Affecting AI Accuracy. The platform couldn’t process real-time transcripts or past recordings effectively, weakening sentiment analysis and recommendations.
Tightly Coupled Architecture Slowing Releases. A monolithic system meant even small changes caused regressions, costly delays, and slower feature delivery.
Unpredictable Release Quality and Long Testing Cycles. Teams spent too much time fixing pipeline issues and managing manual tests, making releases slow, inconsistent, and high-risk.

Our Solution Approach

We adopted a phased modernization strategy focused on scalability, stable AI performance, and a faster delivery pipeline. Each phase removed a key bottleneck, enabling smoother releases, stronger AI accuracy, and a better agent experience.

1 · Optimize

AI Algorithms for Speed and Accuracy

Fine-tuned models to improve response time and precision.
Aligned AI suggestions with real-time agent workflows.

2. Enhance

AI Capabilities for Better Agent Support

Added sentiment analysis and Next Best Action features.
Trained models on past calls to improve intent detection.

3 · Boost

Data Pipelines for Real-Time Insights

Built real-time ingestion for transcripts and call history.
Enabled continuous learning to boost AI accuracy.

 

4 · Improve

Performance Across the Platform

Decoupled AI from the monolith for independent scaling.
Reduced latency and improved system stability

5 · Redesign

UI/UX for Better Agent Experience

Introduced an intuitive dashboard with integrated AI.
Reduced steps, improving speed and agent adoption.

6.Modernize

Architecture and Accelerate Release Delivery

Migrated to microservices with automated CI/CD.
Enabled faster, reliable, and test-driven releases.

Technical Highlights

 Containerized microservices architecture using cloud-native services
 CI/CD pipelines for automated build, testing, and deployment
 Infrastructure as Code (IaC) for consistent environment provisioning
 Improved AI accuracy and reduced latency through model tuning and dedicated inference services
System monitoring and alerting for early detection of failures


// Pseudocode
{
function
handleCustomerQuery(query):
intent = detectIntent(query)aiResponse = getAIResponse(intent)if isComplex(aiResponse):
logIssue(query, aiResponse)
notifySupervisor()return aiResponse
}

Business Outcomes

The modernized platform delivered measurable improvements across performance, reliability, and AI quality.

30%

Improvement in Scalability
The move to microservices reduced platform complexity and improved throughput during high call volume.

 

25%

Boost in Customer Satisfaction
Better AI accuracy and a cleaner agent UI shortened resolution time and reduced frustration.

 

40%

Faster Release Cycles
Automated pipelines removed manual handoffs and enabled consistently faster deployments.

 

 Higher maintainability  Better cross-team stability  Full compliance with structured delivery practices

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