AI-Powered Virtual Agent Platform for a Leading Contact Center Provider

Operationalized AI handling of core customer experience interactions

This engagement demonstrates how BETSOL helped enterprises operationalize conversational AI platforms through the right cloud architecture, automation, and governance. By combining platform deployment, infrastructure automation, and user enablement, the solution established a production-ready foundation for AI-augmented call center workflows and scalable conversational experiences.

The challenge

The client required a scalable and well-governed environment to support virtual agent development and operations. The platform needed to run across multiple customers, regions, support ongoing releases, and provide visibility, security controls, and non-technical user enablement of AI flow management.

The solution

The AI-based flow platform was deployed into the client’s cloud environment and operated as a managed service. The engagement focused on infrastructure automation, custom studio experience, monitoring, and operational readiness.

Process

• Deployed multi-region AI flow platform: Implemented an AI-based solution across a multi-region GKE environment with managed MongoDB and Redis.
• Customized virtual agent studio UI: Built a tailored front-end GUI aligned to the client’s requirements.
• Automated infrastructure and deployments: Used Terraform for cluster provisioning and Google Cloud Build CI/CD for workload deployment and theming.
• Established monitoring and alerting: Implemented uptime checks and regional dashboards for operational visibility.
• Enabled security and compliance readiness: Implemented ClamAV controls and completed evidence collection activities supporting multiple compliance requirements.
• Delivered user training and enablement: Conducted knowledge transfer sessions covering flows, intents/NLP configuration, troubleshooting, and operations.

Results

A production-supported virtual agent platform with automated multi-region deployment, enhanced operational visibility, and a customized studio experience for managing conversational AI flows.

Key features

• Multi-region AI-enabled deployment on GKE
• Customized virtual agent studio UI
• Terraform-based infrastructure automation
• Google Cloud Build CI/CD pipelines
• Regional monitoring dashboards and uptime checks
• User training for flows and NLP configuration

Technical implementation details

• AI platform: Enterprise conversational AI platform
• Databases: Managed MongoDB and Redis
• Infrastructure automation: Terraform; Google Cloud Build CI/CD
• Security: ClamAV implementation; evidence collection for compliance requirements

Business impact

• Established a production-capable environment for AI-driven contact center interactions
• Enabled repeatable multi-region deployments through infrastructure automation
• Improved operational visibility with regional dashboards and monitoring
• Prepared client teams to manage AI flows through targeted training and enablement