Error Code. Text Sent. AI Diagnosed. Repair Booked.

Appliance breakdown texts follow a predictable communication pattern: the customer discovers the fault (the washing machine won't drain, the oven won't heat up, the fridge has stopped cooling), and their first instinct is to text because it allows them to describe the problem in specific terms that a phone call can feel too fumbled for. A customer can text "Bosch washing machine, about 5 years old, starts a cycle but stops after 10 minutes and flashes E18 on the display" — a level of detail that's much harder to communicate verbally while simultaneously trying to read an error code off a panel. Customers who found your number on a fridge magnet from a previous visit text because the number is right there on the fridge, and texting it requires no searching for your website or looking up a phone directory. Evening texts are particularly common because that's when people use their appliances (cooking dinner, running the dishwasher, putting a wash on) and discover the fault. The AI responds to these descriptive texts with genuine appliance awareness — recognising the brand, the symptom pattern, and the likely fault — which establishes your credibility before you've even spoken.

When a customer includes an error code in their text — which they do frequently because modern appliances display coded fault indicators — the AI responds with genuine diagnostic intelligence. An E18 code on a Bosch washing machine gets a response mentioning a probable drain pump or filter blockage. An F5 on a Hotpoint dishwasher gets a response about a water inlet issue. An E1 on a Samsung fridge freezer gets a response about a sensor fault. This error code awareness in the initial text reply achieves three things: it demonstrates that you're a specialist engineer who understands the appliance (not a general handyman with a screwdriver), it gives the customer confidence that the problem is diagnosable and repairable, and it allows you to check parts availability for the likely component before visiting — which significantly increases your first-visit fix rate. Customers who receive an error-code-aware text reply from your AI rarely text a second engineer, because the technical competence in the response makes the booking decision easy.

The AI asks every appliance repair customer for the model number — typically found on a sticker inside the door, on the back panel, or on a data plate behind the kick strip — because this single piece of information transforms your repair visit preparation. With the make and model number, you can check the parts catalogue before leaving the house, identify the most common failure points for that specific machine, order or pick up the likely replacement part from your supplier, and arrive at the customer's property with the component ready to fit. A first-visit fix saves the customer the inconvenience of a second visit and saves you the time and fuel cost of a return trip. The AI's text response makes capturing the model number feel natural: "Could you check for a model number sticker inside the door or behind the filter cover? It helps me bring the right part first time." Customers appreciate this efficiency-focused approach because it signals a professional operation.

Text message enquiries reach every appliance repair specialisation, and the customer source determines the text profile. A domestic appliance repair engineer receives texts about washing machines, tumble dryers, dishwashers, ovens, fridge freezers, and cookers from homeowners whose machine has developed a fault. A commercial appliance repair company receives texts from restaurant managers about commercial dishwashers, from launderette owners about industrial washing machines, and from care home facilities teams about commercial kitchen equipment. A white goods installation specialist receives texts about plumbing in new washing machines, connecting cookers, and fitting integrated dishwashers. Whether your customers describe you as an appliance repair engineer, a washing machine repair man, a white goods engineer, an appliance fixer, a domestic appliance technician, a cooker repair engineer, a fridge freezer specialist, an oven repair company, or a commercial appliance service engineer, the AI ensures every text receives a brand-aware, fault-intelligent response.

The AI sends one comprehensive text that captures everything needed to prepare for a productive repair visit: the appliance type and brand, the model number if the customer can find it, the age of the machine, the specific symptoms or error codes, and the customer's location and scheduling preference. This single-reply approach works particularly well for appliance repair because the customer's text typically contains a clear fault description — they know exactly what their machine is doing wrong — and the AI's response needs only to confirm the likely diagnosis, request the model number for parts preparation, and arrange the visit. There's no need for extended text-based diagnostics because the actual fault verification happens physically at the machine. The AI bridges the gap between the customer's initial text and your repair visit with maximum efficiency: one message, one qualified lead, one parts-prepared engineer.

Fridge magnet callbacks represent one of the most valuable lead sources in appliance repair — a customer who used you before, was satisfied with the service, and kept your magnet on their fridge for exactly this moment: another appliance goes wrong. These repeat customers text your number directly from the magnet, often with a message like "you fixed our washing machine a couple of years ago, now the tumble dryer has stopped heating." The AI responds to these returning customers with the same immediate professionalism that earned their loyalty originally, asking about the new appliance fault, capturing the model details, and scheduling the repair visit. The lead notification flags that this is likely a repeat customer (based on the text content mentioning previous service), which allows you to check your records for the address and property details before calling back. Maintaining response quality for repeat customers is commercially critical: a satisfied appliance repair customer refers friends and family, and a single fridge magnet can generate five to ten repair bookings over its lifetime on the customer's fridge.

The SMS add-on is £29 per month on top of the Speed Receptionist Core plan at £89 per month, with unlimited text responses. For an appliance repair engineer, the mathematics of captured text leads are compelling across every appliance category. A washing machine repair is worth £80 to £160. A tumble dryer element replacement is worth £70 to £130. A dishwasher pump repair is worth £80 to £150. An oven element or thermostat replacement is worth £60 to £120. A fridge freezer compressor or thermostat repair is worth £90 to £200. A commercial dishwasher repair is worth £150 to £400. A customer who texts about one faulty appliance and mentions a second issue during the visit is worth double. The AI captures these leads particularly effectively during evening hours — when customers discover faults while cooking or doing laundry — ensuring that the morning callback secures the booking before the customer searches for alternatives.

The "is it worth fixing?" question appears in a significant proportion of appliance repair texts, particularly for machines older than seven years. The AI handles this economically sensitive question with genuine helpfulness: it explains that the repair cost depends on the specific fault (an element replacement on a ten-year-old oven is almost always economical, while a compressor replacement on a twelve-year-old fridge freezer may not be), and that the engineer will give an honest assessment on site. The AI also asks the customer to provide the model number and age, which allows you to assess the repair viability before visiting — because some older machines have discontinued parts or known end-of-life reliability problems. This transparent, advisory approach builds trust: a customer who receives an honest "it might not be worth repairing, but let me check the model first" response from your AI trusts the engineer's subsequent recommendation — whether that's a repair or a replacement.

Yes — the AI captures the appliance details, fault description, and customer contact information at the moment they text, and you follow up during your next available window. Most appliance repair engineers review text leads between jobs: finishing a repair, driving to the next appointment, and phoning the evening's text leads during the journey. The AI's captured details — brand, model, symptoms, error code, age — mean your callback can open with informed confidence: "You texted about your Bosch washer showing E18 — that's usually the drain pump filter. I can come Thursday morning and I'll bring a replacement pump just in case." This parts-prepared, knowledge-led callback is what converts text leads into confirmed bookings.

We configure SMS handling on your existing business number during a brief onboarding conversation — the number on your fridge magnets, your van, your website, and any directory listings. During setup, we learn about your appliance specialisation (domestic, commercial, or both), the brands you're most experienced with, your geographical coverage, your call-out fee structure (which the AI can optionally include in its replies for pricing transparency), and your typical scheduling availability. The AI is configured with appliance-specific terminology covering fault symptoms, error code patterns, common failure modes by brand, and age-related repair viability considerations. We test with simulated texts covering washing machine breakdowns, oven faults, fridge freezer failures, and commercial equipment issues before going live. Most appliance repair businesses are operational within 48 hours.