Retail AI inside an existing furniture commerce stack
Afurnix · Systems integration · mid-size retail
A growing furniture retailer was answering the same room, stock, and delivery questions across the shop, chat, and email, while catalogue and warehouse data already existed in other tools.
We connected the storefront, catalogue, and warehouse data they already held, then added an assistant for the shop floor and the inbox. It covered stock, lead times, and room suggestions, with staff confirming the customer-facing reply.
Sales and support remain in their existing tools. AI connects the information those tools already hold.
22% fewer “let me check and come back to you” loops.
Context
Mid-size retailers often already have a storefront, a catalogue, a warehouse view, and a chat inbox. Those systems do not answer together when a customer asks, for example, whether a sofa is available next week in grey. Staff place the customer on hold and search.
A full platform replacement would have paused trading. The work was to add AI and integration to the stack they already sold from.
What was broken
Stock truth lived in warehouse software. Product copy and options lived on the site. Conversations lived in chat and email. Every “let me check” added a handoff, and a lost sale when the customer did not wait.
The goal was not a new e-commerce engine. It was one operational answer, drafted for the channel the customer was already using.
Intervention
We joined catalogue SKUs, warehouse availability, and lead-time rules in a thin operational layer, then placed an assistant in the inbox and shop-floor tools staff already used. It proposed stock, option, and delivery answers with a source the person could see.
Room suggestions used existing product metadata rather than a new configurator. Anything involving payment or custom manufacture stayed on the manual path.
Outcomes
“Let me check and come back to you” loops on the targeted question types fell by about 22% after rollout. Staff reported that they could stay in the same chat instead of switching systems. Conversion on assisted conversations improved because answers arrived in the same session.
The retailer kept its storefront. AI connected tools the business already paid for.
Patterns you can reuse
Integrate the systems you have before you build new ones. Place the assistant in the inbox you already use. Show the person the source of a stock or lead-time claim. Measure handoff loops, not chatbot activity that does not change the sale.
“We did not need a new commerce platform. We needed the catalogue and the warehouse to finally talk — and a reply we could send in the same chat.”
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