A growing SaaS platform was experiencing rapid growth in customer support ticket volume. Their support team was overwhelmed, leading to long response times, inconsistent answer quality, and increased customer churn. They needed a scalable solution that could handle routine inquiries automatically while escalating complex issues to human agents with enough context for review.
Our Approach
Designed and implemented an agentic AI system using OpenAI's function calling and LangChain framework for orchestration
Integrated with Dify platform for prompt management and workflow configuration, enabling non-technical staff to refine responses
Built custom integrations with Chatwoot for traceable handoff between AI and human agents
Developed evaluation framework to measure response accuracy, customer satisfaction, and resolution rates
Implemented Kubernetes-based microservices architecture for scalability and reliability
Created Python-based service layer handling intent classification, context retrieval, and response generation
Designed monitoring and analytics dashboard to track performance metrics and identify improvement opportunities
Results
Routed routine customer inquiries through an AI-assisted support flow
Escalated complex tickets to human agents with conversation context preserved
Made answer quality easier to review through evaluation and monitoring workflows
Gave the support team a path for handling higher ticket volume without losing handoff visibility
Kept support operations observable through dashboards and service-level monitoring
QRUV helps teams with practical AI systems, retrieval, evaluation, observability, cost-aware architecture, and backend automation. If this project resembles a problem on your roadmap, send a short note about the workflow and current constraints.