AI Customer Support Bot for E-commerce

We export 60-90 days of historical tickets and cluster them by intent. This shows exactly which question types dominate the queue, which are safely automatable, and which must always route to a human. The audit becomes the scoping contract: the bot launches only on intents where the source of truth is unambiguous.

We turn the store’s product catalog, FAQs, shipping matrix, and returns policy into a structured, retrieval-ready knowledge base. Every answer the bot gives must trace back to a document the store controls — no free-form model guessing about prices, stock, or policy. Catalog syncs keep product data current as inventory changes.

We build the bot on a retrieval-augmented LLM pipeline and wire it into the channels customers already use: the website widget and WhatsApp Business. Shopify integration lets it answer “where is my order” with live order status rather than a canned deflection, and start a return flow inside the conversation itself.

Anything outside approved intents — refund disputes, damaged items, upset customers — escalates to a human inbox with the full transcript, detected intent, and order context attached, so agents never restart the conversation. Confidence thresholds, PII handling, and a hard never-invent-policy rule are tested before launch.

We launch to a slice of traffic first and measure auto-resolution rate, handoff rate, and customer feedback per intent. Weak intents get better source documents or are pulled back to humans. Only after the numbers hold across a full week does the bot take the entire queue.