Try the system yourself

This is the actual system live, running on a real backend. Try asking about a return, an order, a product recommendation, or express frustration and watch the escalation agent kick in.

Fashion support teams are buried in repetitive queries

For fashion e-commerce brands, customer support is a constant flood of the same questions "Where is my order?", "Can I return this?", "What size should I get?" answered manually, one by one, all day long.

Human agents spend 80% of their time on these repetitive queries, leaving complex issues unresolved and customers waiting hours for a simple answer.

A supervisor-led multi-agent architecture

We built a system where a central supervisor routes every incoming message to the right specialist agent instantly, based on intent detection and conversation context.

• Supervisor Agent Routes messages to the right agent based on keywords and conversation history. Never lets context get lost between turns.
• Returns Agent Handles refund requests, exchange queries, and return policy questions with accurate date calculations.
• Order Agent Looks up real order data, returns live status, tracking numbers, and estimated delivery dates.
• Recommendation Agent Suggests products based on customer preferences, available sizes, colors, and stock status.
• Escalation Agent Detects frustration and urgency signals, creates support tickets automatically, and prepares a briefing for human agents.

Built with modern Python AI tooling

Every component was chosen for reliability, speed, and ease of maintenance no over-engineering, no unnecessary dependencies.

What this system delivers

What we figured out building this