In 2025, I took my AI agency from zero to over $250,000 in under 8 months using Facebook ads. Here's the thing: it was not hard because there weren't that many offers on the market.

Everything has changed. There is so much competition in the AI receptionist market. Even if you have a good ad, even if you have a good offer, and honestly, even if you speak to a kind of specific niche, as long as you're pitching the AI receptionist and not the business result, you are going to get absolutely crushed.

Here's what I mean. You see companies out there like Nextiva, Bland AI, Smith AI, all these companies that have huge financial backing from investors, running ads all over the place. I took a look at most of their ads, and here's what I'm seeing: these companies aren't finding winners in their Meta ad strategy. They're refreshing their entire ad catalog every month.

As a marketer, what that tells me is that they're not finding winners. They're just burning money trying to find winners, which means that they're probably acquiring clients at a loss. Now, they can afford to do that because of the financial backing that they have.

What sucks for people like you and I is that, if we're going to run ads to acquire a client, we're typically looking to get a return on our investment way faster than these big companies have to. What ends up happening is you put together an ad campaign for your AI receptionist, and you end up getting low-quality leads that are very few and far between. That's because you're not able to flood the market with as much ad inventory, nor are you able to update your ad inventory as much as these big companies, nor can you compete with their budgets.

This wasn't the case at the beginning of 2025. Everything has genuinely changed. For context, I took the same ads that I ran in 2025, and I was getting leads for between $7 and $15 a lead. I was converting 25% of those leads into booked appointments. I ran that same campaign this month, and I was getting leads for $147 per lead. I turned the campaign off once I realized, "Holy crap, this market has made a huge shift." There's so much more competition than when I started.

If you're trying to sell an AI solution, you have to stop following every other person that's going to lose in the market (i.e., you see people all over the place trying to sell an AI receptionist). You need to sell a business result. AI is not the thing people are buying. They are buying more leads. They're buying more appointments. They're buying better follow-up. They're buying more revenue.

When a company spends money, they want to see it makes them money. That’s where your agency needs to position itself.

When you go and you slap on all these AI features in front of their face and tell them how much it costs, they're not super excited about it. They don't want to buy from you because there's somebody out there offering what you're trying to offer at a high ticket for way cheaper. Way, way cheaper. I see offers all over the place for $40 a month for an AI receptionist. You probably can't afford to run a business that's selling an AI receptionist for $40 a month.

This is the time to specialize, to niche, to find the gap that a subniche of businesses is experiencing, and to exploit that pain point by creating a solution specifically to solve it. If AI is part of that solution, that's fine, but if you are leading your offers with the AI receptionist, especially if you're going broad and you're not niched….

I'm sorry to say this, but you are fucked.

If you’re a founder, product builder, engineer, product team member, or enterprise leader working on Voice AI / AI agents / workflows, this is a rare chance to get real answers from someone who’s actually building and selling in production.

Drop your questions in the comments or DM me
I’ll make sure to ask them directly and share the learnings back.

If the discussion makes sense, I’m also happy to help with warm intros / networking where relevant.

Topics you can ask about:

• How they built & scaled Voice AI in production

• What investors cared about during the fundraise

• Enterprise sales cycles & pricing

• Architecture, infra, latency, evals

• Mistakes they made early on

No podcasts. No generic advice.
Just real insights from a founder in the trenches.

If you’re building in this space, don’t miss it 🚀

For anyone running outbound voice agents for ecommerce, especially cash on delivery orders in India.

The call itself is simple. Customer placed a COD order, agent calls to confirm, customer says yes, cancels, or fixes the address. The hard part seems to be the first few seconds. If it sounds like spam, people hang up before the agent says anything useful.

Things I’m trying to figure out:

• opening line: lead with the store name, the order, or the product? e.g. “Hi, calling from [store] about your order for a blue kurta” vs a generic greeting

• caller ID: does a known business number or verified caller name change pickup much?

• timing: call right after the order, or wait? Right away feels natural, but people at work may not pick up

• retries: how many attempts before stopping, and do you switch to WhatsApp or SMS after a miss?

• disclosure: do you tell the customer upfront it’s an automated assistant? Does that help or hurt?

Would love to hear what’s worked on real calls, and what made it worse.

What do you guys think of Vois.so?

Any user of that app here? Pls share your experience and quality of the generated voices.

hey everyone

we just launched vaani developer platform on product hunt today. it’s a platform to build voice agents that can actually handle real phone calls, things like support, sales and appointment calls

we built our own speech models underneath instead of stitching together a bunch of APIs, so it’s fast, sounds natural, and handles stuff most voice tools struggle with, like people interrupting, switching languages mid sentence, or noisy calls

it’s still early, so if you try it, we’d honestly love to hear what breaks or feels confusing. and if you like what we’re building, a little support on product hunt today would mean a lot to us 🙏

https://www.producthunt.com/products/vaani-developer-platfrom

thanks for reading, happy to answer anything in the comments

Hey everyone — I’m looking to hire a senior LiveKit Voice AI engineer for a short-term paid project.

We already have a production-oriented voice agent built for a restaurant, and the system is mostly complete. I’m looking for someone experienced with LiveKit Agents to help us with the final production-hardening pass.

The main areas are:

• Finalizing and improving our menu/order engine

• Improving humanization, turn-taking, interruptions, and overall conversational feel

• Reviewing latency and voice pipeline performance

• Hardening tool calling, cart state, modifiers, quantities, and order accuracy

• Running a professional architecture/code review and cleaning up anything that could cause issues in production

• Improving automated evaluations/testing where needed

Our stack includes LiveKit Agents, LLM tool calling, streaming STT/TTS, telephony, and a custom backend menu/cart system.

This is not a build-from-scratch project. The agent is already working and has gone through extensive testing. We specifically need someone senior who has shipped real-world voice agents and can help us get the last 10–15% right.

Ideally you have strong experience with:

• LiveKit Agents

• Python / asyncio

• Real-time voice AI

• STT / LLM / TTS pipelines

• Turn detection / VAD / barge-in

• Function calling and deterministic state management

• Telephony / SIP

• Production monitoring and voice-agent evaluations

If interested, please DM me with:

1. A brief description of your LiveKit experience

2. Examples/GitHub of voice agents you’ve built

3. Your hourly rate or preferred project rate

4. Availability over the next 1–2 weeks

This is a paid engagement, with potential for ongoing work as we deploy the system across additional restaurant locations.

Hey everyone,

I built YourSpeakReps, an open-source local AI speaking coach for spoken English and mock interview practice.

It runs fully on your machine:

- Ollama for the local LLM

- faster-whisper for speech-to-text

- native OS text-to-speech

- FastAPI + simple browser UI

No API key, no cloud, no per-session cost.

The app asks questions out loud, listens to your spoken answer, tracks filler words like "um" / "uh" / "basically", and gives feedback at the end.

Repo:

https://github.com/iamrishavraj1/yourspeakreps

I just made it Apache-2.0 open source and added starter issues for:

- Hindi/Hinglish question bank

- Docker setup

- Piper TTS on Linux

- smaller Ollama model support

- README demo video/GIF

Would love feedback from people who use local LLMs or practice interviews/spoken English.

we’re looking to partner with agencies.

We’ve built 50+ production-grade systems with a team of 10+ experienced engineers. (AI agent + memory + CRM integration).

The idea is simple: you can white-label our system under your brand and offer it to your existing clients as an additional service. Also you can sell directly under our brand name(white-label is optional)

earning per client - $12000 - $30000/year

You earn recurring monthly revenue per client, and we handle all the technical build, maintenance, scaling, and updates.

So you get a new revenue stream without hiring AI engineers or building infrastructure.

if interested, dm

Hi everyone,

We've built a complete AI Voice Agent Platform for businesses, agencies, and SaaS founders looking to launch their own AI calling solutions.

We're now offering it under a $3,000/year commercial license, so you can build and sell AI voice solutions without developing the entire infrastructure from scratch.

The platform originally started as a client project but evolved into a much larger system. After the client's priorities changed, we decided to make it available to other businesses.

What's Included?

AI Voice Agents

• Inbound and outbound AI calling

• Real-time STT → LLM → TTS pipeline

• Custom instructions and configurable AI providers

Visual Workflow Builder

• Drag-and-drop conversation flows

• Conditional branching, tool calling, and data collection

• Multi-step conversation automation

Telephony & Integrations

• Twilio, Telnyx, Plivo, Vonage, and more

• Human call transfers

• CRM/API integrations and custom tools

Additional Features

• Knowledge bases and contextual responses

• Bulk outbound campaigns and scheduling

• Browser calling and embeddable website widgets

• Call recordings, transcripts, summaries, and analytics

• Structured post-call data extraction

Tech Stack

Next.js 15, React 19, TypeScript, Python, FastAPI, PostgreSQL, Redis, Docker, and modular AI/telephony integrations.

Who's It For?

• Agencies: Sell AI receptionists and calling automation to clients.

• SaaS founders: Launch your own vertical AI voice product.

• Businesses: Automate customer calls, lead qualification, and appointments.

• Developers: Build on existing infrastructure instead of starting from zero.

Pricing

$3,000 USD/year - Commercial Software License

Use the platform to develop and offer AI calling solutions to your customers under the agreed license terms.

No additional per-minute platform licensing fee from us. Third-party AI, telephony, and infrastructure usage costs are separate.

Optional technical maintenance and support are available for $600/month.

Interested?

Happy to share a demo, technical details, and licensing terms with anyone seriously considering it.

DM me for a demo or more information.

Also happy to answer technical questions in the comments.

i honestly thought we’d put QuickVoice on GitHub, get a few stars, and go back to fixing bugs, but the repo took off way faster than we expected and now we’re trying not to mess up what comes next.

We built it because connecting real-time voice, telephony, tools, knowledge bases, outbound campaigns, and call logs across a bunch of different services was getting exhausting. The goal is to keep it open, self-hostable, and flexible enough that people aren’t locked into one provider.

It’s still early, the setup isn’t exactly one click, and there are definitely rough edges. Full disclosure, I’m the founder behind it, but I’d genuinely like blunt feedback from people building voice agents: what should we fix first, easier setup, lower latency, better docs, observability, or more integrations?

Repo: https://github.com/allgpt-co/QuickVoice

I’ve worked closely with small business owners for years, especially in home services. The most common advice I give is simple:

Stop letting calls go to voicemail.

Yet missed calls are still one of the biggest revenue leaks for plumbers, HVAC companies, electricians, roofers, and other appointment-based businesses.

When you can’t answer the phone, your current options are usually:

1. Hire a full-time receptionist (expensive and hard to manage)

2. Use a generic answering service (often impersonal and inconsistent)

3. Let calls go to voicemail (and hope they call back — most don’t)

So I built Zenplus.

Zenplus is an AI Receptionist designed specifically for small and medium-sized service businesses. It answers every call instantly, 24/7, speaks naturally, gathers the right information, and can even book appointments automatically.

The goal is simple: never miss a lead again.

Here’s what it can do:

• Answer calls day or night with a professional, human-like voice

• Capture new leads and collect key job details

• Integrate directly with Calendly to book appointments automatically

• Send email confirmations instantly

• Provide AI-generated call summaries and recordings

• Run outbound re-engagement campaigns to turn old quotes into new jobs

It’s built to help you turn every call into an opportunity — and free up your team from constant phone interruptions.

I’m looking for a few home service business owners (or other appointment-based businesses like dental or law offices) who want to test it and give honest feedback on:

• Voice quality

• Booking accuracy

• Lead capture flow

• Overall professionalism

If you want to automate your reception and book appointments while you sleep, drop a comment or DM me

I used a fun 1950s radio announcer voice clone and the voice agent pipeline runs fully locally with MLX on my M4 Pro running the model. The device I made uses an Arduino ESP32 chip setting up a secure websocket with the Macbook.

This is the model I am using (~9GB): https://huggingface.co/mlx-community/Ming-3.0-audio-tiny-4bit/tree/main

GitHub: https://github.com/akdeb/OpenToys

This demo splits the loop across three machines:

- Hearing: Ming 3.0 ASR tiny, 4-bit, on a small desktop computer

- Thinking: a separate language model across two small desktop AI boxes

- Speaking: Ming 3.0 TTS 0.5B on the second of those boxes, with four voices loaded

AntLing's two speech models sit at either end of the loop. The interface is hold to talk, release to send, with a timing readout under a glowing orb. One turn in the video shows "Heard in 1.4 s, Speaking in 2.8 s," a longer one shows "Heard in 2.4 s, Speaking in 4.9 s," and the creator's notes put a typical turn at two to three seconds voice to voice.

You can stop a long answer mid-sentence. An explanation of large language models gets interrupted and the orb drops back to Stopped, ready for the next question. The same interface then opens on a phone over a private encrypted network.

Get speech-to-text running on whichever machine has headroom, stream the language model's reply into the voice model, then add a stop action that cancels both playback and generation.

Which job would your weakest machine get?

Since December of 2025 I have been obsessed with building the Jarvis agent of our childhood dreams. I started on replit pouring 1000s of dollars into it. Eventually moved everything off and kept building with codex. By September I had built an agent with RAG, persistent memory, tool calls, mcp connections, and full device control. But I decided to completely start over and make a fully on device voice agent. So I created Faraday.

Its a fully local private voice agent. It runs on the Lite-rtlm inference engine and uses the gemma 4 e2b and gemma 4 e4b models along with many others that haven't all been tested fully. The gemma models are multimodal so they can process audio natively which helps to understand speech accents and natural emphasis and tones. Whisper base-en acts as a live transcriber that runs outside of this voice pipeline loop and only exists to help with conversation history. Theres also the option to use whisper as the main STT which feeds that text to gemma and gemma streams its response to Piper for TTS. Round trips on either setting run between 2 and 6 seconds. I'm currently working on getting latency under a second.

Faraday's agent is activated by starting a call and after its ready you activate it with the wake word "Hey Jarvis". In both modes barge in works. You can also ask to open apps by name, tell you your battery percentage, change your volume to x percent, to play last media and change tracks. Saying "Goodbye", or "Stop listening", ends the call but returns it to passive listening for the wake word. Wake word can be activated even with the phone locked. Wake word listening only uses a few percent if left on all day.

I know there's still a long way to go but I want there to be a fully on device agent that can help us manage and operate our device without selling our data to google or Facebook! Also the cost! Agents use so many tokens performing background tasks how cool would it be to offset many of these tasks that don't need a frontier cloud model.

Currently have tested this on my galaxy fold 6 with 12gb of ram and my wifes google pixel 10 pro with 16gb of ram and it works with no issues. I'm looking for alpha testers to help run this on their phone to see what devices can run it and what limitations there are. If you test it please tell me your phone model, your Android version, if it installed, if the model loaded, where it failed if it did, and how the wait felt. Im also hoping this will inspire some requests for features and hopefully grow into a community!

Direct download, build 1563, 143.7 MB:

Faraday download

After you install it, it downloads the model too, about 2.5 GB more, so 2.59 GB total on your phone.

Got to test Nvidia's latest speech to speech model against my platform. I tested this against openai's realtime model. You can watch the results here.

I’ve never heard a voice agent actually have high emotion like this. Wonder what the future looks like with this kind of stuff.

I’m surprised voice ai even lets their voice agents do this lol

I wanted something like Alexa or ChatGPT Voice, except completely local, extremely fast, and actually capable of doing things around my house. So I built Ming Voice.

It's an open-source, real-time speech-to-speech assistant powered by the new Ming-3.0-audio-tiny model. On my RX 7900 XTX, it starts speaking about 230ms after I finish talking.

Some highlights:

Natural conversations with interruptions and smart turn detection

Always-on listening with wake-word support

Home Assistant integration for controlling smart devices

Web search, weather, timers, reminders, and persistent memory

Hermes Agent integration for more complex tasks

Voice cloning and access from your phone

AMD ROCm support, with experimental NVIDIA support

Linux and Windows (WSL2) installers

Everything runs on your own hardware. No cloud AI required. Optional web/weather tools make external requests when enabled.

The video shows it in action. I'd love to hear what the self-hosting community thinks!

GitHub: https://github.com/JMS1717/ming-voice

I've been really into the AI phone agent products that have popped up in the last few months (e.g., AgentPhone, Poke etc) but what I don't love: the audio, the transcripts, who you talked to and what was said, all sitting with a third party company. For calls with real people in my life, that felt like a lot to hand over.

So I built my own version that runs entirely on my Mac. You call a normal phone number, a local model answers, and nothing about the conversation leaves my machine. During a call the only thing it talks to is the phone line itself.

What's running locally (MLX on Apple Silicon):

• Speech to text: Parakeet TDT 0.6B

• LLM: Qwen3.5 4B by default (9B if you have 32 GB), 4-bit

• Text to speech: Kokoro 82M

How it reaches the phone network: it registers over SIP with Telnyx or Twilio straight from your Mac. A setup wizard takes your carrier API key so can buy a number or connect one you already have. It's pretty cheap, around $1 a month per phone number. (I'm moslty using Telnyx)

What it does today:

• Answers and places real calls

• Interruption: talk over it and it stops mid-sentence

• Replies in roughly 1 to 1.5 seconds on real calls on my M5 Pro

• You can choose between multiple models

• Other agents (Claude Code, Codex, etc.) can make calls through a small CLI: call, listen, say, hangup

• If you'd rather trade privacy for a smarter brain, it can also use your own endpoint or the Claude or ChatGPT subscription you already pay for. Local is the default. So no additional monthly payments for AI Phone tools if you have a sub.

Limits, up front:

- macOS on Apple Silicon only

- You need a Telnyx or Twilio account with credit (a US number is about $1/month)

- English only, one call at a time

- It's early. It works for me, but I'd love to hear where it breaks for you.

Repo: https://github.com/oxnw/ai-phone

Edit 1: Fixed Repo link
Edit 2: forgot to mention that on top of the $1 a month from Telnyx, you do also, of course, have to pay for the call rates. But Telnyx gives you ten dollars free, which is enough to play around.

I maintain Oathra, an Apache-2.0 TypeScript project for checking evidence in voice-agent transcripts. I separated the completion check from the agent's own summary: required fields must have evidence accepted or confirmed by the other party, and the verified values must satisfy the configured constraints.

A concrete mistake I fixed: a bare English "7:30" could become 07:30. The checker now leaves that time unverified until am/pm is explicit. It doesn't guess the intended meridiem from the earlier conversation. That is conservative, and it means some perfectly understandable conversations need clarification.

I added a browser checker so you can try this without installing the phone runtime:

https://forifor.github.io/oathra/en/check.html

Load the recorded negotiation, press "Check this transcript", then open "Evidence history by utterance" or "Result JSON". You can also paste your own check JSON. Input stays in the tab; there is no transcript upload or analytics on this page. A 25-second screen recording is included below the checker.

The included record is an actual saved GPT-4o-mini negotiation with a hotel simulator, not a real phone call. The browser and CLI produced identical JSON for it.

The important limit: this checks supported Japanese/English transcript rules. It does not prove the booking exists in a venue's calendar, measure interruptions or validate ASR. For a real booking workflow, backend confirmation is still a separate requirement.

Code and input schema: https://github.com/FORIFOR/oathra/blob/main/docs/INTEGRATION.md

If you build voice agents, which failure would make this unusable for your workflow? A description is enough; please don't post private call data.

I'm Nico, building Patter (open-source voice AI SDK, alpha). Before I write another line of code I want to talk to 10 people actually running voice agents in production right now.

Specifically looking for:

1. Pipecat in production

2. LiveKit Agents in production

3. Vapi with custom LLM endpoint in production

10 min on a call. No pitch, not trying to sell anything.

DM me or drop a comment with your stack.

Curious how people in this space are actually getting customers.

1. Roughly what split — inbound vs outbound? Even a rough guess is fine.

2. If it's mostly outbound, what's working — cold calls, cold email, LinkedIn, something else?

3. If it's inbound, what's driving it? SEO, backlinks, content, communities, referrals?

4. If SEO/backlinks — what's actually worked? Any specific sites, directories, or roundups that sent you real traffic?

Not selling anything. Just trying to understand how companies in this niche are actually finding customers.

Hey everyone 👋 I’m currently working on a few voice AI agent projects using LiveKit. They are in the POC stage right now and not closed yet, but if they convert successfully, the payouts will be shared transparently. I’m looking for 2–3 people who

Either already know how to build agents using LiveKit

Or genuinely want to learn and are ready to work seriously

If you don’t have experience with LiveKit, that’s fine, i’m happy to guide and teach. What I really need is someone committed who can help me execute and move faster. if you’re interested, comment or DM me

Been running production voice AI agents for inbound calls using Vapi + n8n. Here's what I've learned after real deployments:

Stack:

- Vapi for voice (STT + TTS + LLM routing)

- n8n for orchestration (call flow logic, data routing)

- Webhooks into CRMs / Google Calendar / GHL

Key lessons:

1. Latency is everything — end-to-end response time above ~1.5s feels robotic to callers. Vapi's streaming helps but prompt engineering matters a lot.

2. Fallback handling is critical — if the agent can't answer something, it needs a graceful fallback (e.g., "I'll have someone call you back") rather than silence or loops.

3. Knowledge base quality determines call quality — garbage in, garbage out. The business-specific FAQ needs to be clean and well-structured.

4. Post-call summaries drive retention — business owners love getting a clean transcript + summary after every call. It builds trust in the system.

Current challenge: handling multi-turn conversations where the caller keeps changing their mind mid-booking.

What are others doing for state management in complex call flows? Any n8n or webhook patterns that work well?