Scope
Our client offers AI-driven call center assistance software, empowering agents with real-time objection handling, conversational AI capabilities, and compliance monitoring to enhance customer interactions, boost sales, and ensure regulatory adherence. The application’s user interface lacked intuitiveness initially. The AI assistant was experiencing response latency and accuracy issues initially.
Engagement
An initial study of current Information Architecture was conducted to understand their existing ecosystem, which included studying of their products management system, and the correlation with regional needs of the subsidiary websites & their environments.
Based on the findings, the following changes were implemented.
- Architectural Transformation: The tightly coupled monolith architecture was transformed into a robust cloud-based containerized microservices architecture to enhance scalability, flexibility, and fault tolerance.
- User-Centric Design: Intuitive UI/UX design practices were introduced, and user testing was conducted for continuous improvement, ensuring easy accessibility for customers across different platforms.
- AI Assistant Optimization: The AI assistant was optimized by fine-tuning algorithms, improving data pipelines, and enhancing infrastructure for better response latency and accuracy, further enhancing customer experience.
- Automated Build and Release Pipelines: Build and release pipelines were automated using CI/CD tools to eliminate manual interventions and accelerate release cycles, facilitating rapid deployment of updates.
- Structured Software Delivery: Structured software delivery processes, including documentation, code reviews, and quality checks, were established to ensure consistency, maintainability, and regulatory compliance.
Outcome
- Increased scalability by 300% with cloud-based containerized microservices.
- Enhanced CSAT through intuitive UI/UX and a 25% improvement in AI assistant accuracy.
- Accelerated release cycles by 40%, reducing overhead with automated build and release pipelines for faster feature delivery.
