Using AI to Automate Customer Service Processes

Project 

The primary objective was to introduce AI-powered capabilities into customer service processes. 

The platform enables: 

• automated summaries of calls, emails, and chats,
• generation of notes supporting customer service agents,
• real-time transcript processing,
• integration of organizational data with an Agentic GenAI platform,
• persistence of user session context and history,
• integration of additional AI services and models.

By automating manual and repetitive activities, the solution improved customer service quality while reducing operational costs. 

Scope of Work 

The j‑labs team was responsible for: 

• building from scratch a service integrating the client’s systems with the GenAI platform,
• designing a scalable and modular architecture,
• implementing both synchronous and event-driven communication,
• integrating transcription services and external AI models,
• setting up Kubernetes infrastructure and CI/CD processes,
• implementing monitoring, metrics, and alerting,
• troubleshooting issues across components maintained by other teams,
• preparing the solution for deployment and migration across additional organizational environments,
• gradually taking ownership of services previously developed by an external vendor.

Delivery 

The engagement began with developing a strong understanding of the business context and assessing the organization’s technology requirements. Based on these insights, we built a new engineering team, defined its role within the organization, and established ways of working with Business Owners and key stakeholders. 

As priorities evolved, continuous roadmap planning and refinement of the scope for subsequent delivery stages became an important part of the engagement. 

The first production use case focused on generating on-demand summaries from call transcripts, emails, and chat conversations. 

In the next phase, the solution was extended with an event-driven architecture, enabling real-time conversation processing and note generation. The team took end-to-end responsibility for delivery; from analysis, architecture, and implementation through testing, Kubernetes infrastructure preparation, and deployment support. 

A strong emphasis was placed on system stability and observability. The team introduced Prometheus metrics, Grafana dashboards and alerts, distributed tracing, and load testing. These capabilities enabled faster issue diagnosis and provided greater visibility into service performance and reliability. 

As a result, the organization gained a scalable technology foundation that can support additional AI-powered customer service use cases as they emerge. 

Team 

1x Tech Lead 
1x Project Manager 
2x Expert Java Developers 
2x Regular Java Developers 

Greater Delivery Predictability with the Agile Delivery Framework 

Built on 16 years of j‑labs experience, the Agile Delivery Framework (ADF) is our approach to delivering IT projects with greater transparency, adaptability, and predictability. 

Regular monitoring of client satisfaction is embedded in the Delivery stage, allowing us to identify areas for improvement early. Through recurring CSAT surveys, the team was able to quickly identify opportunities to improve the delivery model. 

As the project evolved, the need for greater transparency and a more structured delivery process became apparent. Based on regular client feedback, we jointly adapted the way of working, transitioning from Kanban to Scrum. 

This smooth transition allowed the delivery model to evolve alongside the project without incurring the costs and inefficiencies associated with maintaining an approach that no longer matched the client’s needs. 

The change: 

• increased delivery predictability,
• improved planning,
• enabled more transparent communication of project status.

As a result, current progress, priorities, and planned next steps became clearly visible to both the client and other stakeholders across the organization. 

Client-Validated Value 

The value delivered by the team was confirmed through recurring client satisfaction (CSAT) surveys. 

The client awarded a score of 5/5 for support, communication, and responsiveness. 

The engineering team received a score of 4/5 for engagement, technical expertise, teamwork, and software quality.