E-CommerceBusinessAutomatesTier-1CustomerSupportwithLangGraph.js
How an e-commerce retailer built a LangGraph.js AI agent to handle routine customer support queries — order status, returns, product questions — automatically, with intelligent routing of complex cases to human agents.
Key Benefits
How an e-commerce retailer built a LangGraph.js AI agent to handle routine customer support queries — order status, returns, product questions — automatically, with intelligent routing of complex cases to human agents.
- Tier-1: Queries Automated
- Smart: Escalation
- 8 Wks: Delivery
The Challenge
A growing e-commerce retailer found their customer support team spending the majority of time on routine, repetitive queries: order status checks, return eligibility questions, product specification queries, and shipping timeline estimates. These queries followed predictable patterns but required the agent to look up real-time order data, check policy documents, and compose accurate responses. Scaling the support team proportionally with order volume was not sustainable.
The Solution
Techseria built a LangGraph.js support agent that handles tier-1 queries end-to-end. The agent receives a customer query, classifies the intent, retrieves relevant information from the appropriate source — order management API for order status, Qdrant knowledge base for product information and policy queries — drafts a response, and either sends it automatically for low-complexity queries or routes to a human agent with full context pre-populated for complex or sensitive situations.\n\nThe routing logic is defined in the LangGraph.js state graph: the agent classifies complexity and sentiment, determines whether automated response is appropriate, and escalates with a full conversation summary when human handling is required. Built on Node.js, integrated with the retailer's existing order management and support platform via webhooks. Delivered in 8 weeks.
Impact by the numbers
- 8Weeks
- Full DeliveryDiscovery Workshop to production deployment in 8 weeks, including integration, testing, and agent quality review.
Results
How an e-commerce retailer built a LangGraph.js AI agent to handle routine customer support queries — order status, returns, product questions — automatically, with intelligent routing of complex cases to human agents.
- Routine Query Resolution: Automated - Tier-1 queries — order status, returns, product info — resolved automatically without human agent involvement.
- Customer Response Time: Instant - Automated responses delivered immediately on query receipt rather than queued for human agent availability.
- Human Agent Time: Freed - Support team time freed from routine query volume to focus on complex customer situations requiring judgment.
- Intelligent Human Escalation: ✓ - Complex or sensitive queries routed to human agents with full conversation context pre-populated — no context lost.
- Order Management Integration: Live - Agent retrieves real-time order data via API at query time — responses reflect current order status accurately.
- LangGraph.js
- LangChain.js
- Qdrant Vector Database
- Azure OpenAI Service
- Node.js / Webhook Integrations
Technologies Used
Client Voice
"Most of our support volume was routine queries that followed the same pattern every time. Techseria's agent handles those automatically — it looks up the order, checks the relevant policy, and sends an accurate response. Our human agents now focus on the situations that genuinely need a person involved. The escalation logic was important to us — when the agent routes to a human, the full context is already there so the agent doesn't have to start over."