How AI can scale customer experience — online and IRL

The front-end layer

Managing real-time communication across web, mobile, and voice channels requires lightweight microservices that handle session management, channel integration, and API gateway services.

Cloud Run is the ideal platform for this workload. As a fully managed, serverless solution, it automatically scales from zero to thousands of instances during traffic spikes, then scales back down, so LiveX AI only pays for the computation they actually use.

The orchestration and AI engine

The platform’s core, AgentFlow, manages the conversational state, interprets customer intent, and coordinates responses. When issues require human expertise, it routes them to agents with complete context. The system processes natural language input to determine customer intent, breaks down requests into multi-step plans, and connects to databases (like Cloud SQL) and external platforms (Stripe, Zendesk, Intercom, Salesforce, Shopify) so both AI and human agents have complete customer context.

Cloud Run for orchestration automatically scales based on request traffic, perfectly handling fluctuating conversational loads with pay-per-use billing.

GKE for AI inference provides the specialized capabilities needed for real-time AI:

• GPU management: GKE’s cluster autoscaler dynamically provisions GPU node pools only when needed, preventing costly idle time. Spot VMs significantly reduce training costs.

• Hardware acceleration: Seamless integration with NVIDIA GPUs and Google TPUs, with Multi-Instance GPU (MIG) support to maximize utilization of expensive accelerators.

• Low latency: Fine-grained control over specialized hardware and the Inference Gateway enable intelligent load balancing for real-time responses.

With this foundation, LiveX AI can serve millions of concurrent users during peak demand while maintaining sub-second response times.

The knowledge and integration layer

From public FAQs to secure account details, the knowledge layer provides all the information the system needs to deliver helpful responses.

The Doc Processor (on Cloud Run) builds and maintains the knowledge base in the vector database for the Retrieval-Augmented Generation (RAG) system, while the API Gateway manages configuration and authentication. For long-term storage, LiveX AI relies on Cloud SQL as the management database, while short-term context is kept in Google Cloud Memorystore.

Putting it all together

Three key advantages emerge from this design: elastic scaling that matches actual demand, cost efficiency through serverless and managed GKE services, and the performance needed for real-time conversational AI at scale.

Looking ahead: Empowering customer experience teams at scale

The future of customer service centers on intelligent systems that amplify what human agents do best: empathy, judgment, and creative problem-solving. Businesses that adopt this approach empower their teams to deliver the personalized attention that builds lasting customer relationships, freed from the burden of repetitive queries.

For teams evaluating AI-powered customer experience systems, this architecture offers a proven blueprint: start with Cloud Run for elastic front-end scaling, leverage GKE for AI inference workloads, and ensure seamless integration with existing platforms.

The LiveX AI and Google Cloud partnership demonstrates how the right platform and infrastructure can transform customer service operations. By combining intelligent automation with elastic, cost-effective infrastructure, businesses can handle exponential inquiry growth while enabling their teams to focus on building lasting customer relationships.