AI Auto Responder for Appliance Repair

No other trade gets leads this specific. Customers write "Samsung fridge not cooling but freezer works," "LG washer stops mid-cycle with a UE code," "Whirlpool dryer spins but no heat," "GE oven won't preheat past 150." The machine, the brand, and the symptom are the lead — and the first reply that engages with those details ("does the ice maker still work?", "is the compressor humming or silent?") instantly sounds like a technician, while a generic "we'd love to help!" sounds like a call center.

Three answers before the visit change the economics of every job: brand, model number, symptom. The model number decides which control board, pump, or igniter rides on the truck — and customers never know where to find it, so the AI tells them: sticker inside the fridge door frame, around the washer door rim, in the oven drawer. A first conversation that captures the model number converts a two-trip repair into a one-trip repair, which is the difference between profit and break-even on smaller tickets.

The repair-or-replace question hangs over every conversation. A nine-year-old refrigerator with a failing compressor, a dryer needing its third heating element — customers ask straight out whether it's worth fixing. Shops that give an honest rule of thumb ("past ten years on a mid-range unit, we'll tell you at diagnosis if replacement makes more sense") win the booking precisely because they didn't oversell the repair.

Timing in this trade has a signature: the evening discovery. The fridge is found warm at dinnertime, the washer dies during the after-work load, the oven fails while preheating — so leads stack up between 7 and 11pm, after most shops stop answering. The refrigerator ones carry a food-spoilage countdown that makes them the most urgent messages in home services. An instant overnight reply plus a first-slot morning booking is how those jobs are actually won; the rush before Thanksgiving, when every oven in town gets tested at once, runs on the same clock.