The client wanted to modernize their e-commerce customer support system, which was heavily dependent on manual processes and rigid decision trees. They required a solution that could understand messy or incomplete customer queries, identify the customer securely, and provide instant resolutions across common scenarios like order lookups, delivery tracking, and returns. A critical requirement was minimizing unnecessary handovers to human agents while still ensuring that sensitive, complex, or high-risk queries were escalated with full context.
CLIENT’S REQUIREMENT
The client wanted to modernize their e-commerce customer support system, which was heavily dependent on manual processes and rigid decision trees. They required a solution that could understand messy or incomplete customer queries, identify the customer securely, and provide instant resolutions across common scenarios like order lookups, delivery tracking, and returns. A critical requirement was minimizing unnecessary handovers to human agents while still ensuring that sensitive, complex, or high-risk queries were escalated with full context.
Solution
We built an AI-powered multi-agent customer support assistant that coordinates specialized agents to deliver faster, more reliable service. It starts by sanitizing customer input so even short or error-prone queries are understood correctly, then verifies identity securely via OTP tied to email or order ID for accurate, context-aware responses.
From there, domain-specific agents handle tasks: order lookup and logistics tracking fetch purchases and deliveries, while policy validation and return logistics apply the right product-specific rules. All paths converge into a response formulation agent that crafts clear replies. Meanwhile, a fraud detection agent flags high-value or suspicious cases for review, and a sentiment analysis agent monitors frustration to escalate to humans when needed. Escalations include full history so customers never repeat themselves.
The assistant resolves most queries end-to-end like tracking packages, processing returns, and answering order questions, while involving human agents only when necessary. This hybrid model scales support operations, improves customer satisfaction, and safeguards compliance and fraud risks through intelligent automation and oversight.
Technology Stack
frontend: Steamlit, Chat Widget , LLM: Google Gemini (via langchain-google-genai), Ollama (local model support via langchain-ollama), backend : Flask, FastAPI, Docker, Agentic FW: Langchain, Lang graph, Lang smith, Additional Libraries: BeautifulSoup4 (web scraping), Pydantic (data validation)
