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AI Voice Agents
Missed calls are the quietest way to lose money. Someone rings while you're on a job or after hours, gets voicemail, and calls the next name on the list. A voice agent picks up every time. It sounds like a person, works out what the caller needs, answers the usual questions, and books them straight into the calendar. Your team gets a clean summary afterwards.
Works the same for a dental front desk hearing a toothache and finding a same-day slot, or a property agent qualifying a buyer and booking the viewing.
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Dial: the AI front desk for one-person trades
The problem: one-person trades can't answer the phone from a roof or a crawlspace. Missed calls become lost jobs, and evenings go to quotes and follow-ups.
I built Dial and set it up for a demo HVAC shop, Dry Heat Air Co. in Mesa, AZ.
For customers
• An AI voice front desk that answers every call, asks the right questions and flags emergencies
• Online booking in plain words. Dial suggests the likely fault and offers real arrival windows from the owner's schedule. Booked in about a minute.
• A live job page, plus a My Visits account with every visit, quote and receipt
• One tap to confirm "I'll be home" or accept a quote
For the owner
• The whole day on one screen: what needs you, what the front desk handled, the route and time saved
• Hold to talk after a job. The voice note becomes the receipt, the quote and follow-ups on day 2, 7 and 14.
• Both sides update live, so there's no phone tag
Built with Lovable and Lovable Cloud, ElevenLabs for the voice agent and transcription, and Gemini to write up each job.
Try it: dial-frontdesk.lovable.app
The customer side has a shared demo account, so you can tap around and watch the owner's side update live.
Receipts processed without a spreadsheet this month.
Every one logged, categorized, and filed the same day it came in.
→ Want this running for your expenses? Let's talk.
Getting receipts logged and filed the same day wipes out the spreadsheet grind.
How 40 resumes become 5 shortlisted, automatically:
1. Resumes screened against the actual role criteria
2. Ranked by fit, not just keyword count
3. Top matches surfaced for a human to review, same day
→ Want your hiring pipeline running like this? Let's talk.
Good structure. The part I would add is a review queue for low-confidence parsing and duplicate records. Ranking is only useful if the recruiter can see why someone matched and correct the data before it becomes the source of truth.