Conversational AI Platform
A B2B SaaS company with 2M+ users was scaling faster than their 120-person support team could handle. We built a conversational AI platform that understands context, resolves 80% of inquiries autonomously, and seamlessly hands off complex cases to human agents with full conversation context.
The support team was handling 45,000+ tickets per month across email, chat, and phone. Average first-response time had degraded to 4.2 hours, CSAT scores had dropped to 3.8/5, and the team was burning out. Hiring more agents was not sustainable — the company's user base was growing 40% year-over-year, but support costs were growing faster than revenue.
Previous chatbot attempts had failed because they relied on rigid decision trees that couldn't handle the variety of real customer inquiries. The product was complex (enterprise workflow automation), and customers asked nuanced questions that required understanding their specific account configuration, integration setup, and usage patterns.
We built a multi-agent conversational system using LangGraph for orchestration. The architecture separates concerns into specialized agents: an intent classifier that routes inquiries, a knowledge agent that retrieves from product documentation and knowledge base, an account agent that accesses customer-specific configuration data, and a resolution agent that executes actions like password resets, plan changes, and integration troubleshooting.
Each agent has access to different tools and data sources, and the orchestrator manages conversation state, decides when to escalate to humans, and ensures that handoffs include the full reasoning trace so agents don't ask customers to repeat themselves. The system supports Slack, web chat, email, and WhatsApp through a unified message processing pipeline.
We fine-tuned the intent classifier on 50,000 historical support conversations, achieving 96% routing accuracy. The knowledge retrieval uses a RAG pipeline over 3,000+ help articles with semantic chunking and a domain-adapted embedding model. Response quality is continuously evaluated against human agent responses on a weekly basis.
The platform autonomously resolves 80% of incoming support inquiries — up from 0% with the previous rule-based chatbot. Average response time dropped from 4.2 hours to 12 seconds. CSAT scores improved from 3.8 to 4.7/5, with customers frequently unable to distinguish AI responses from human agents.
Support costs decreased by 60%, allowing the company to redeploy 40 agents to proactive customer success roles. The remaining human agents handle only the most complex cases, with full AI-prepared context that reduced their average handle time by 35%. The system has served 2M+ users across 180,000+ conversations since launch.