Trades · Northern Ireland
Feeney Flooring and Blinds
An AI product specialist that also manages two separate booking calendars
The Context
Trades businesses selling a physical product, flooring and blinds among them, get a specific kind of enquiry before a job is ever booked: genuine product questions. What materials are available, roughly what something costs, how long fitting takes. Answering those well takes real product knowledge, and a generic contact form or a slow reply doesn't give a browsing customer much reason to wait around for one.
Scheduling adds a second layer of complexity on top of that. A business handling more than one type of appointment, for instance needing two genuinely separate diaries rather than one shared calendar, is exactly the kind of thing that's easy to get wrong by hand: a customer booked into the wrong calendar, or two people accidentally double-booking the same slot from different channels.
Customers also increasingly expect to reach a business wherever they already are, which for a lot of trades customers is WhatsApp rather than email or a contact form. A business only offering the channels that suit it, rather than the ones customers actually use, is quietly turning some enquiries away before they even start.
Put together, that's three separate problems, product questions, calendar logic, and channel coverage, that don't have much in common on the surface but all sit in the same gap between a customer's first question and a job actually getting booked.
The challenge
Feeney Flooring and Blinds needed to answer real product questions quickly, manage two genuinely separate booking calendars without double-booking across them, and meet customers on WhatsApp as well as more traditional channels, all without it becoming a full-time job on top of the fitting work itself.
What we built
An AI flooring and blinds specialist
Trained on the business's actual product range and general trade knowledge, so a website visitor gets a specific, useful answer about materials, options, and rough pricing immediately, rather than a generic contact form and a wait.
WhatsApp as a real channel
Customers who'd rather message on WhatsApp than email or call get the same coverage as any other channel, folded into the same inbox as everything else rather than being a separate, easily-missed thing to check.
Two AI-managed booking calendars
The AI books directly into whichever calendar a given enquiry actually needs, so the two stay genuinely separate without anyone having to manually check both before confirming a slot.
A CRM tying it together
Every enquiry, WhatsApp conversation, and booking is tracked against one contact record, so nothing about a customer's history gets lost between channels.
This system is launching in 2026. Results will be published here once live.
What This Means
For a trades business selling a physical product, the gap between a customer's first question and a booked job is often wider than it looks: it's not just about answering fast, it's about answering with real product knowledge, on the channel the customer actually prefers, and getting the booking into the right calendar without anyone double-checking by hand. Closing all three at once is what actually removes the admin, not just one piece of it.
What this case study teaches, more broadly
- Product-specific AI knowledge matters more in trades than a generic chatbot script. Customers ask specific product questions before they'll seriously consider booking.
- WhatsApp is often where trades customers already are. Meeting them there, rather than only offering a contact form, removes real friction.
- Two calendars handled by one AI system avoid the classic problem of two people juggling a shared diary and double-booking each other.
- A CRM underneath ties every channel, chat, WhatsApp, and calendar bookings, back to one contact record, so a customer's history doesn't fragment across separate tools.
Related services
Questions about this project
Why isn't Feeney Flooring and Blinds' site live yet?
It's built and working; the domain setup just isn't finished. This case study will be updated with real results once it's had time to run live.
How does the AI know about specific flooring and blinds products?
It's trained on Feeney Flooring and Blinds' actual product range and general trade knowledge, not a generic chatbot script, so answers about materials, options, and rough pricing are specific to what the business actually offers.
What happens if a customer wants to speak to a real person?
Anything the AI can't handle, or that a customer specifically asks for, is routed through to the team the same way any other enquiry would be.
Does the AI handle both calendars the same way?
It knows which calendar a given type of enquiry needs to go into, so bookings land in the right diary without a customer needing to know the difference themselves.
How We Publish Results
This is a real engagement, reported honestly
Everything above reflects an actual project we scoped and built. The challenge is the real problem the client came to us with, and the approach is what we actually built, not a simplified or idealised version of it. Where a result is quoted, it's a genuine outcome the client has confirmed, not a projection or an industry average presented as if it were specific to them.
We follow the same process for every client, documented in full on how it works: a discovery call to understand the real problem, a bespoke proposal built around what we heard, a build that integrates with what the business already has, and ongoing support once it's live. Nothing above happened by accident or as an off-the-shelf configuration. It was scoped specifically for this business, the same way we'd scope a system for yours.
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