Conversational AI Support Agent for Automating Retail Customer Queries

A large omnichannel retail brand headquartered in Toronto, Canada, experienced high inbound support volume across order tracking, return requests, and product inquiries. Their human agents struggled to manage peak-hour load across chat, app, and web channels. To improve availability and reduce operational overhead, the company partnered with an AI engineering team to build a 24×7 conversational AI agent capable of resolving common support queries autonomously.

Problem Statement

Order tracking queries accounted for nearly 55% of all conversations.
Human agents manually validated order IDs and shipping details, increasing resolution time.
Refund and replacement workflows required multiple steps, creating delays during peak seasons.
No automated system existed for FAQs or product recommendations.
Support quality varied across agents, impacting user satisfaction.

Solution Approach

Defined 32 high-volume intents (tracking, refunds, returns, product lookup, store hours, offers).
Trained a Rasa NLU model using real historical chat transcripts for improved intent accuracy.
Implemented authentication middleware to validate users before accessing order information.
Integrated with the Shopify Admin API to fetch tracking updates, check refund eligibility, and process return requests.
Built a product-recommendation layer backed by ElasticSearch using keyword + similarity queries.
Developed multi-channel deployment via Twilio Conversations API, providing seamless support across web, app, WhatsApp, and SMS.
Implemented escalation rules to transfer complex cases to human agents along with conversation history.
Added a monitoring dashboard using Kibana for continuous improvement based on conversation analytics.

Benefits

Support ticket volume decreased by 42%, reducing load on human agents.
Enabled instant responses across multiple channels, improving user satisfaction.
Automated refunds, tracking, and FAQ responses reduced average resolution time.
Consistent and accurate assistance improved brand perception.
Human agents focused on high-value support cases instead of repetitive tasks.

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