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 shop, chat, and email — while catalogue and warehouse data already existed in other tools.
We connected storefront, catalogue, and warehouse data they already had, then added an assistant for shop-floor and inbox: stock, lead times, and room suggestions, with staff confirming the customer-facing reply.
Sales and support stay in their existing tools. AI fills the gaps between them.
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 — they just do not talk to each other in the moment a customer asks “can I have this sofa next week in grey?” Staff put customers on hold and hunt.
A full replatform would have paused trading. The work was to insert AI and integration into 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” was a handoff tax — and a lost conversion when the customer did not wait.
The goal was not a new ecommerce engine. It was a single operational answer, drafted for the channel the customer was already in.
Intervention
We joined catalogue SKUs, warehouse availability, and lead-time rules into a thin operational layer, then placed an assistant in the inbox and shop-floor tools staff already opened. It proposed stock, option, and delivery answers with a source the human could see.
Room suggestions used existing product metadata rather than a new configurator product. Anything involving payment or custom manufacture stayed on the manual path.
Outcomes
“Let me check and come back” loops on the targeted question types fell by about 22% after rollout, with staff reporting they could stay in the same chat instead of switching systems. Conversion on assisted conversations improved as answers arrived in-session.
The retailer kept their storefront. AI became the connective tissue between tools they already paid for.
Patterns you can reuse
Integrate before you invent. Put the assistant in the inbox you already have. Show the human the source of a stock or lead-time claim. Measure handoff loops, not chatbot vanity metrics.
“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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