A fashion brand handles 4x the orders with the same team.

The challenge

A Bangalore D2C fashion brand was breaking operationally as it scaled from 200 to 800 orders a day across Shopify, Amazon, and Meesho. Each order batch took 2.1 hours to process by hand, and the team could not keep three storefronts in step.

Inventory was the worst of it. The brand logged 34 inventory sync errors a month across the three platforms, which meant oversells, cancellations, and refunds. The same 3 people who ran the operation at 200 orders had no path to 800 without a rebuild.

The approach

Webzenia built an order-routing engine on n8n that pulled every order from Shopify, Amazon, and Meesho into one queue. Each order was validated, tagged, and routed to fulfilment without a person touching the spreadsheet.

Real-time inventory sync sat underneath it. When a unit sold on any one channel, stock updated across all three within seconds, which closed the oversell gap that had caused most of the errors.

We added an AI layer on OpenAI to read return requests and classify them, cutting return triage from a manual queue to an automated decision. Razorpay refund events fed straight back into the same workflow.

The stack reached full deployment in 6 weeks. The brand kept its existing Shopify storefront untouched; the automation ran around it.

The results

Order processing dropped from 2.1 hours per batch to 12 minutes, and the same 3-person team now runs 800 orders a day at 4x the previous volume. The operation scaled without a single new hire.

Inventory sync errors went from 34 a month to zero. Stock no longer depends on someone remembering to update three dashboards: one sale updates all three channels instantly.

Return processing fell from 3 days to 4 hours. The AI triage layer turned a manual queue into an automated decision, so refunds clear without a person reading each request.

The stack behind it

• n8n8nAutomation
• ShShopifyCommerce
• AmAmazonCommerce
• MeMeeshoCommerce
• OpOpenAIAI
• RaRazorpayCommerce