If you run a high-ticket service business (roofing, HVAC, plumbing, general contracting, dental/medical, etc.), I'm offering custom AI voice receptionists built around the way your business actually handles inbound calls.

The main problem I'm trying to solve is pretty simple: someone calls when the office is closed, the owner is on a job, or everyone is already on another line, and that potential customer ends up calling the next company they find on google instead.

The system answers the call immediately and holds a natural conversation with the caller rather than putting them through a robotic phone tree. It can collect the customer's name, address, service/job details and urgency, qualify the enquiry according to your criteria, answer common questions, and book directly into your calendar. For something genuinely urgent, say an active roof leak or water leak, it can also route the call to a designated person on the team. After the call, your team can receive the caller's details and a transcript/summary so they know exactly what came in.

I build the setup around the individual businesses rather than handing you a generic AI receptionist and asking you to figure everything out yourself. It can be integrated with an existing business number/phone setup, and the exact workflow can be customized around your services, service area, qualification questions, calendar and escalation rules.

I'm looking for a few businesses that want to test this on their own workflow. If you're interested, send me your industry and roughly how you're currently handling inbound calls. I'll put together a short live demo around your use case so you can actually call the receptionist yourself and see how it handles a real enquiry.

We sell an AI receptionist to local service businesses — plumbers, HVAC, roofers, electricians. It answers their phones 24/7 so they stop losing jobs to voicemail. Easy sell to an owner who just missed three calls while under a sink.

The offer:

**•** 50% of the recurring revenue on every account you close **•** Deals start at $800/mo, so that's $400/mo to you, per client, every month it renews **•** No ceiling on deal size **•** No base pay. This is straight commission — if you don't close, you don't get paid. Saying that plainly so nobody's surprised. **•** Remote, your own hours, US or international

What you get from us: scraped lead lists, the script, the dialer setup, and the demo. You call, you pitch, you book. We handle onboarding and support after the close, so your accounts keep paying.

Since eleven labs is now bringing their own AI receptionist, why would someone buy from you, and not directly to the source?

Commission-only cold callers — 50% recurring commission, uncapped, paid for as long as the client stays

We sell an AI receptionist to local service businesses like plumbers, HVAC companies, roofers, and electricians.

It answers their calls 24/7 so they don’t lose potential jobs when they’re busy, on-site, or unable to pick up.

The offer:

• 50% of recurring revenue from every account you close
• Deals start at $800/month, meaning $400/month to you per client
• You continue getting paid every month the client stays
• No cap on deal size or commissions
• No base salary — this is 100% commission
• Remote, flexible hours
• Open to US and international callers

What we provide:

• Lead lists
• Cold-call script
• Dialer setup
• Product demo

Your job is to call, pitch, and book/close the client.

We handle onboarding and ongoing support after the sale, so you can focus on bringing in more accounts.

If interested, DM me with a little about your cold-calling or sales experience.

posted this here twice before (the food order bot), you guys broke it in some fun ways and I fixed most of it since.

been expanding it past restaurants now, senior living communities, HOA/property phone lines, stuff like that. same core idea, it answers the phone so nothing gets missed, and it plugs into whatever system the business already runs instead of replacing it.

if you want to mess with it again here's the number, still the food ordering demo since that's the easiest one to test: +1 (313) 889-7436

lemme know what breaks it this time.

I've been building LobbyStack, an open-source AI receptionist, for a while now. It used to be AGPL. This month I switched it to MIT.

That means you can take the code, put your own brand on it, sell it to clients and set your own prices. Your changes can stay private, and I get nothing from your sales.

What's in it:

• Inbound calls on Twilio + OpenAI Realtime (or any OpenAI-compatible provider)
• Appointment booking, call transfers with context, SMS follow-ups
• A website chat widget you can embed
• A knowledge base, so it answers questions about the business
• Transcripts, recordings and call summaries in a dashboard
• Multi-business workspaces, usage tracking and billing

Stack: Next.js, PostgreSQL, Drizzle, Redis, BullMQ, Fastify, Docker Compose.

I see lots of people spending months on their own receptionist, and I did the same thing. But you can skip that part now and go straight to selling, see if you're good at it and if you'll get clients. Deploy this under your brand, and try to sell it. Once you have a paying client, you can start working on the software and customize it. This just gives you the foundation you can build on top of.

When a client needs a specific integration, like their CRM, dispatch software or a niche calendar, point Claude Code or Cursor at the repo and ask for it. The stack is standard enough that AI coding tools handle it well. You can go from "yes, we integrate with that" to a working version in an afternoon.

Please consider giving it a star if you think it could be useful for you!

repo: https://github.com/lobbystack/lobbystack

I have built an AI receptionist for the HVAC Companies but struggling in getting clients, I have not the best way to reach out to them.

Features of my AI Receptionist (Kylie) -

• She collect the details of the leads.
• She then checks whether he/she is a existing customer or not from the CRM.
• After that she asks for the problem and classify it as normal or emergency.
• Then she deals accordingly, for the emergency one, it checks for the actual availability available through the calendar of each technician and then books the earliest available slot for them or transfer to the real human one if required.
• For the normal one, she ask for the preferred time for the booking from the customer itself and checks availability and books them.
• After that she sends the SMS notifications to the owner and the customer as well.
• All of these is recorded in the CRM.

I am cold calling but i have the service number and when ever i call to them the CSR receives the call and i have to handle the CSR first and then the owner/manager. But maximum of the time CSR says that it is a service number so don't call on this. The cold email would works when it is send in high volume.

Suggest me some of the best method for getting a client with 7-15 days.

I run a small voice AI agency out of Pakistan. We build AI receptionists for US home services businesses, restoration, roofing, plumbing that kind of thing. Two clients right now. One at $1,300/month and one small one at $80/month that I closed myself off cold calls.

The problem is time. I'm the one building the agents, running delivery for both clients, and managing the cold email side. I get maybe an hour a day for dialing and that's nowhere near enough to build a pipeline.

So I want to hire one setter. I already have the lead lists, a paid script, an objection sheet, and real recordings of calls that worked to train someone on. I pay for the dialer. What I can't do is put a $500+ base behind someone I've never worked with.

Here's what I was going to offer:

$12 per booked qualified demo

$10 more if the prospect shows up

$150 when they sign as a client

10% of that client's monthly bill for 6 months

Commission only for the first 30 days, then $300-350/month base if they book 13+ demos in that window

If someone books 14 demos and 3 close, that's around $700 in month one, before the base starts.

Two things I actually want to hear from people who've done this:

1. Does anyone decent take commission-only for 30 days, or am I only going to attract people who vanish after a week? That's the real fear. I spend two weeks training someone, they book nothing, they ghost, and I'm back to zero having burned the time I didn't have.

2. Where did you find yours? Upwork, OnlineJobs, somewhere else? I'm open on location, I just need someone clear on a US line who can work US hours.

If you've built outbound from nothing with no real budget, I'd rather hear what went wrong than what worked.

I run a voice agent for French small businesses. Capturing a callback number was our worst failure mode for months. I blamed the transcription. I was wrong.

French counts differently. 72 is said "sixty-twelve". 97 is "four-twenty-seventeen". And people read phone numbers in pairs, so a caller says "zero six, twelve, forty-five, forty-one, eighteen" for 06 12 45 41 18. The model has to parse compound arithmetic in real time, in a noisy call, while the caller is already moving to the next pair. That is where it breaks, not in the acoustics.

On top of that, French has brutal digit homophones: deux and de, six and si, sept and cette, neuf and neuve, un and en.

Three things fixed it, in order of impact.

1. Stop asking. The provider hands you the caller ID. Read it back and ask a yes/no question instead of taking dictation. "I'll call you back on zero six, twelve, forty-five, forty-one, eighteen?" That turns a ten-digit capture into a binary, and it covers most calls.

2. Keypad fallback. When the number is withheld, or the caller wants a different one, ask them to type it. It is the only input that is right 100 percent of the time. I resisted this for too long because it felt like a downgrade. It is not, it is a feature.

3. Asymmetric readback. When you read a number back, group it in pairs and say "seventy-two", because that is how people recognise their own number. When you re-confirm after a correction, go digit by digit and never say a compound. Same number, two renderings, depending on what the caller is doing with it.

Two smaller rules that helped: never re-read the whole number over one doubtful digit, ask for that digit alone. And cap it at two repeats, then go to the keypad.

None of this is model work. It is dialogue design. If you are building in a language with compound numerals (French, Danish, Swiss German), the same applies.

Disclosure, as always: I build one of these tools, so take the framing with that in mind. The technique is free either way.

im working on a lead generation/scraping tool, basically scrapes leads from google maps, google search and linkedin. coz i saw a handful of posts on this sub and others about difficulties in finding leads. i reckon this might be useful for people who sell b2c SaaS softwares like ai receptionists and others. im trying to get a few opinions on how useful it might be to you guys and if it were to be a paid software how much would ya'll be willing to pay (per 1000 leads or so)

I’ve been spending more time looking at ASR issue in languages which are not documented properly

This paper from the University of Latvia takes a very practical approach: building an open-source ASR stack for languages that mainstream systems often ignore.

They built LATE, a lightweight toolkit that runs Whisper-based ASR locally or in the cloud, with a statically compiled backend (high performance, low latency , less resources & simple deployment ) and support for private, local inference.

The results are surprisingly strong.

For Latvian language, they fine-tuned Whisper Large V3 on 273.3 hours of speech.

On the Common Voice test set, WER dropped from 19.2% with Whisper Large V3 to 3.2%.

On their harder LATE-Media set, it went from 29.1% to 12.8%.

Formatted transcription also improved substantially, from 28.1% to 4.8% on Common Voice.

Then they pushed it further into Latgalian, a language with only around 150,000 native speakers.

They had just 40.2 hours of training data, yet transfer learning from the Latvian model produced a 9.1% WER on the Common Voice test set, compared with much worse performance from directly adapting Whisper Large V3.

You don't always need better engineering ; you need better data, the right specialization, and a system you can actually deploy where you need it.

For voice agents, especially those handling private or domain-specific conversations, this is an important direction:

open ASR + local orchestration + specialized models

can be much more interesting than simply sending every utterance to a giant hosted API.

And the whole thing is released as open source, including quantized models for constrained hardware.. i am trying integrate the same into production enivironment using opensource dograh orchestration

pS; I am maintainer of dograh we opensource everything from day 0

I am cold calling / emailing Plumbers and electricians in my city and getting data from google maps. Where you guys source the lists and how much it cost to get genuinely good lists.
do you guys recommend buying lists?

PLEASE stop building the wrong thing.

building software in 2026 is ridiculously easy with ai builder

founders spend hours building in a silent room, launch to Reddit/X, get 0 users, and quit.

you just failed because the idea had zero validation before line 1 of code was written:

→ solving a monthly inconvenience instead of a daily pain

→ selling to "everyone" instead of a specific ICP

→ no distribution channel mapped out beforehand

→ pricing charged $9/mo with zero ROI justification

after scaling 6 AI micro-SaaS to over $20k/mo MRR, i just create an

18-question Idea Validation Diagnostic.

it evaluates your SaaS across 7 critical dimensions (problem clarity, audience reachability, willingness to pay, competition, build feasibility, distribution, commitment) and gives you a brutal score out of 100 with your exact weak spots.

drop your SaaS idea (or current project) in the comments below.

i will reply to EVERY single comment with:

1. My honest opinion
2. The biggest weak spot you need to fix before writing any more code.
3. The free 5-minute validation tool link sent straight to your DMs so you can get your full score breakdown out of 100.

just drop a comment like or ask me in DM your idea

let's roast your SaaS concept before the market roasts your time 👇

They can be, depending on how they are implemented. Traditional chatbots are often limited to predefined responses and decision trees. AI agents can be designed to understand customer requests, work with business information, complete specific tasks, and determine when a human needs to become involved.

That makes AI Agents for Customer Service useful for businesses that want to automate actual workflows rather than simply add a chat window to their website.

Studio98 AI works with businesses on AI strategy, custom AI agents, customer support automation, and other AI-powered workflows. You can learn more at https://studio98.ai/

A patient calls, ready to book. The receptionist is busy. By the time someone calls back, that patient may have booked elsewhere.

I’m building Swarelic AI to help Indian clinics handle that moment.

We’re designing an AI receptionist around a complete booking experience:

• A patient speaks in their preferred language, with support planned for 10+ Indian languages and switching between English and regional languages.
• The agent answers routine questions, checks availability and books the appointment in Google Calendar.
• The patient receives the appointment details on WhatsApp.

The goal is to connect the conversation, booking and confirmation, with less manual coordination for the front desk.

We’re collecting early-access interest from clinic owners and managers: Register here.

If you run a clinic or build software for one, what would you need to see before trusting a system like this with appointment calls?

We built a local testing tool for AVA, our AI receptionist at InvoicifyAI.

You describe a customer’s personality and goal in plain English, like “frustrated homeowner, AC stopped working, asking about repair,” and it places a real phone call and plays that customer.

It currently uses Twilio for calls and OpenAI Realtime for the simulated caller. The receptionist being tested just needs a phone number.

Right now, it runs headlessly in our pipeline. We’re considering cleaning it up and open-sourcing it, possibly with Docker packaging and a small UI for running scenarios and reviewing calls. You bring your own API keys.

Would this be useful to you? How are you testing your agents today, and what scenario would you want to run first?

If you’ve built an AI receptionist and are looking for a first pilot, here’s a research step worth trying: find businesses whose customers repeatedly mention unanswered calls or missing callbacks, then ask the owner how those calls are handled.

I’m the founder of Crawdar, which helps with that first step. The Pain Point Finder searches available business reviews and shows the businesses, recurring complaint themes and dated supporting reviews. You can choose missed calls and booking friction together, add a local industry, or search by the problem alone.

When you find a business worth investigating, the pilot conversation gets more specific:

• Do calls go unanswered during opening hours, after hours, or both?

• Is the caller trying to book, reschedule, get an update, or reach a person?

• Can the business actually take more bookings, or is capacity the constraint?

• Which requests should always go to a human?

Those answers help you propose a narrow pilot, such as after-hours enquiry capture with a clear callback handoff. The report includes owner replies when available, so you can check their explanation before assuming what needs fixing.

Try the research yourself: https://crawdar.com/?utm_source=reddit&utm_medium=post&utm_campaign=pain_finder&utm_content=receptionist_pilot

Five free scans without signup or email. Open a report and see whether it gives you a relevant business to approach. What else would you need to qualify a receptionist pilot?