Enterprise Conversational AI Platform

Overview

A large enterprise needed to modernize their customer support infrastructure to handle increasing support volumes while maintaining service quality. Their existing chatbot solution couldn't scale beyond a few hundred users and lacked the intelligence to handle complex queries. The business needed a platform that could handle 10,000+ concurrent conversations while providing personalized, context-aware responses.

We architected and built a distributed conversational AI platform with intelligent routing, context management, and seamless human handoff. The system uses state-of-the-art LLMs combined with custom business logic to handle routine queries while escalating complex issues to human agents with full conversation context.

The platform now handles over 70% of customer inquiries automatically, significantly reducing support costs while improving response times and customer satisfaction scores.

Objectives

Support 10,000+ concurrent conversations without degradation
Maintain conversation context across multiple interactions
Integrate with existing CRM and ticketing systems
Achieve <3 second response time for 95% of queries
Implement intelligent routing to human agents when needed

Challenges & Approach

Challenge

Scaling WebSocket connections to support massive concurrency

Solution

Implemented distributed architecture with load balancing across multiple Node.js instances and Redis for session management

Challenge

Managing conversation state and context across sessions

Solution

Built custom context management system with Redis caching and PostgreSQL persistence for long-term history

Challenge

Reducing latency for LLM responses under high load

Solution

Implemented request queuing, response streaming, and intelligent caching of common query patterns

Challenge

Seamless handoff to human agents with full conversation context

Solution

Developed real-time synchronization system that transfers complete conversation history and user intent analysis

Outcomes & Impact

Successfully handling 10,000+ concurrent conversations

70% reduction in human support tickets

Average response time of 2.1 seconds

85% customer satisfaction score

99.9% platform uptime over 6 months

Key Learnings

Building truly scalable conversational AI requires careful architecture planning from day one. We learned that conversation state management is one of the hardest challenges—naive approaches break down quickly under load. Using Redis for hot data and PostgreSQL for cold storage, with careful cache invalidation strategies, proved essential for performance.

LLM response streaming significantly improved perceived performance, even when actual processing time remained constant. Users perceive the system as faster when they see responses appearing incrementally. We also learned that intelligent human handoff is critical—knowing when to escalate and providing agents with rich context makes the difference between frustration and excellent service.