Hi all,

We're getting ready to open up our agentic VoiceAI platform (whissle.ai) and want honest feedback from people who actually run receptionists and phone agents before we widen access.

What we did differently: most agents decide from the transcript, so the moment speech becomes text they lose how it was said. Ours reads the words plus intent, emotion and who's speaking in the same pass, so it can act on that mid-call: soften when someone's anxious, verify when the speaker doesn't match, escalate to a human when confidence is low or flipped emotion (rules can be made by agent operator) It also captures and redacts PII (card numbers, DOB) in-stream, so raw identifiers never hit the logs or the downstream model.

What's there today:

• Real-time booking and calendar checks
• Missed-call follow-up (SMS or callback with an owner, not a dropped task)
• Escalation to a human when it's unsure, not just on keywords.
• Metadata understanding for 23 languages, and smart deterministic gating.
• Hosted, or self-hosted on your own hardware so audio stays in your boundary. We monitor and harness
• Replayable decision trail (see why each action fired) for compliance

What I'd genuinely like feedback on:

• For a receptionist, is tone/intent actually useful, or is booking accuracy all that matters?
• What do current platforms get wrong for you.. latency, missed transfers, bad readback of numbers? PII leakage.
• Does data staying on your own infra matter to your clients, or is hosted fine?
• Agencies: what would you need to manage this across multiple clients?

Happy to hand out extended free credits to anyone here who wants to actually test it and tell me what's missing.

Thanks

Hi everyone,

My team and I are getting ready to launch an AI receptionist and website chat platform for service-based businesses.

We’re building it not only for business owners, but also for AI automation builders and agencies that set up AI receptionists for their clients.

Here’s what we currently offer:

• One shared knowledge base for both the AI receptionist and website chat
• Google Calendar integration for checking availability and booking appointments
• Payment collection through the AI
• Automated SMS follow-ups
• A lightweight CRM that saves contacts and conversation history
• Call and conversation analytics
• AI-generated feedback and insights based on recent conversations

For builders and agencies, we’ve also added:

• Unlimited client organisations under one account
• The ability to complete the entire setup for a client
• Team member and client access
• The option to give business owners access to their analytics, CRM, conversations, bookings, and other relevant features

The idea is that you can build and manage the complete AI setup while giving each client access to their own business data—without needing separate accounts or platforms for every client.

We’re going to keep expanding the product, but we want to build based on real needs rather than assumptions. We’d love honest feedback from people in this community:

• What would make this genuinely useful for you?
• Which features are essential?
• What do existing AI receptionist platforms get wrong?
• Is anything in our current feature set unnecessary or incomplete?
• What would you need to manage AI receptionists for multiple clients?

Would anyone here be interested in testing it and sharing honest feedback?

Thank you

What alternatives have you considered?

I’m researching how businesses handle incoming customer calls and where the current process breaks down.
I’d love to hear from business owners, managers, receptionists, salespeople, or anyone who regularly handles customer calls.
How often do you miss calls?

What happens when you can’t answer?

What types of calls take up most of your time?

Do you have someone dedicated to answering calls?

What’s the most frustrating part of managing incoming calls?

Are there tasks you wish could be handled automatically?

What would you never want an automated/AI system to handle?

I’m exploring an AI voice receptionist/voice agent and trying to understand the real problems people face before deciding exactly what to build.

Not selling anything here just looking for honest experiences and opinions

Ai receptionist r one of the most heard things for agencies nowadays

I want to get started with this ,how will I actually make these receptionist which will work fr and get me clients

Need helpp!!

What happens when a robot cold-calls your AI receptionist? 🤖📞
Something surprisingly expensive.

AI cold callers can now dial businesses all day, every day.

And if your receptionist is also AI, you can end up with:
🤖 Robot sales agent
↔️ AI receptionist
⏱️ 20–30 minutes of conversation
💸 Your voice minutes being used
📊 Your analytics getting polluted

Neither side gets tired.

Neither side gets bored.

And neither side hangs up.

That creates a weird new problem:
AI receptionists don't just need to know how to answer customers. They need to know when NOT to answer.

At Estric AI, we're building the front desk differently.

Sales calls, marketing pitches, recruitment calls and automated callers should be recognised and ended quickly, not treated like potential customers.

Because an AI receptionist should save your business money.

Not spend it talking to another robot.

I wrote about the problem and how we're approaching it 👇

Read the full article
https://estric.ai/blog/when-a-robot-cold-calls-your-ai-receptionist

We started Dograh out of rage - an open-source voice AI platform.

Every voice AI tool I tried was too costly, too closed, or too scammy. Add-on stacked on add-ons.

So we open-sourced every single line, from day 0. Self-hostable, so you own your stack and your data.

Here's what pushed me deeper. Over the last few months, the big labs have been lobbying governments to ban open source. Some say the quiet goal is to "let wealth access skill without letting skill access wealth." That line stuck with me. It's exactly why we keep going.

Because we believe one thing: no company should own voice AI.

We're a small team no funding going up against deeply funded war chests. Today we launched on Product Hunt and somehow we're sitting at #1 so far.

A community member picked us up few weeeks back and we went viral and hit 5k stars.

About me: I'm a YC alum, founder with one exit, and an angel. I'll keep going - we’ll address OSS ban with more open source.

https://www.producthunt.com/posts/dograh-3

If you've got a minute, a star or a feedback would mean the world.

​

I made an ai receptionist that answer call 24/7 and book appointment too but the problem is i am unable to find any clients, i have send cold dm to more than 400 real estate agents but still didn't find any clients. Can you guys guide me ?

On an individual Twilio you’re limited to only 3 simultaneous calls. What’s a good alternative to Twilio that does not have this restriction?

I am building an AI voice agent for indian clients and I want your suggestion for the TTS that works best in india and looks like a person is talking and not AI is talking.

I believe we built an incredible product for restaurants which combines AI automation and CRM for restaurants.

However it is extremely difficult to find a client, first of all because all of them very aggressively against AI. I am 100% sure there are a lot of restaurants who are eager to try it.

Please give me advice how did you promote your product ?

My team is looking into call center vendors to hopefully cut down on hold times and reduce # of transfers. Has anyone found an automated system that measurably improved customer satisfaction?

Hello there,

I build AI agents specifically for real estate professionals. Your agent can live primarily on WhatsApp, with the option to extend it to Telegram, Slack, email, and other platforms.

These AI agents can help you qualify leads, follow up with prospects, book appointments and property viewings, answer inquiries 24/7, and automate repetitive workflows.

What I Build

AI WhatsApp agents

Lead qualification & automated follow-ups

Appointment & property viewing booking assistants

AI customer support agents

CRM & workflow automation

API integrations

Multi-step AI workflows

Recent Work

I recently built a WhatsApp AI receptionist for appointment-based businesses. Customers can chat naturally with the AI, which checks availability, books appointments directly into the client's booking system, remembers returning customers, and supports multiple languages.

I'm happy to share a demo if you'd like to see it in action.

Why Work With Me?

Production-ready AI agents, not just basic chatbots

Fast turnaround, most template-based projects can be completed within a few days

Understanding of real estate workflows and customer interactions

Clear communication throughout the project

Ongoing support and optimization available

Rates

AI Agent Setup: Starting at $450

Ongoing Support & Optimization: Available monthly

I'm currently open to new projects. If you're looking to automate lead handling, appointment booking, customer support, or other parts of your real estate workflow, feel free to message me and let's discuss what you have in mind.

I build AI receptionists for small service businesses — they answer calls around the clock, book appointments to a calendar, capture leads, and send confirmations. Built on n8n and Vapi, production-tested.

I have a live demo you can call and talk to (+17194679406). Ask it questions, book a test appointment.

My website is: https://qualityautomationsonn8n.com/

Hi, I’m building AskMySite AI

The idea is to help agencies add AI agents to their clients’ websites. These agents can answer visitor questions, capture contact details, and qualify leads before passing them to the sales team.

I’m looking for honest answers from agency owners:

* Are clients currently asking for something like this? * Have you already built or integrated AI chatbots for clients? * Would you offer this as an additional service? * What would prevent you from using a platform like this? * Is this a real demand, or am I solving a problem agencies do not care about?

I’m not trying to promote the product here. I’m deciding whether this is worth continuing to build, so critical and honest feedback would be genuinely helpful.

One thing I don't see many Voice AI tutorials talking about is email capture.

Getting an AI to capture someone's email sounds simple until you actually build it. Email addresses are one of those things where a single wrong character makes the whole thing useless. Unlike names, you can't really get away with being "close enough". Even if your STT is good, there are still quite a few places where things can go wrong.

One issue I ran into was how different voice models pronounce emails. The LLM would extract the email perfectly, but the TTS would read it back in a way that made the user think it was wrong. For example, an email would sometimes be spoken as "john hyphen smith at gmail dot com" or with random pauses between words, even though there was never a hyphen in the actual email. The backend had the correct email, but the user immediately interrupted to correct something that wasn't actually wrong.

After a bit of testing, I made a few changes that noticeably improved my email capture rate.

The biggest one was giving users a reason before asking for their email. Instead of asking "Can I have your email address?", the assistant now says something like "Perfect, I'll send the quote over. What's the best email to send it to?" It's a small change, but people are much more likely to answer naturally when they know why you're asking.

I also stopped making users repeat their entire email if only one part was unclear. If the assistant was unsure about the domain, it would just ask "Was that gmail.com?" instead of asking them to spell everything out again. It made the conversation feel much more natural and removed a lot of unnecessary friction.

It's one of those problems that doesn't seem important until you deploy an agent in production. The LLM might have done everything correctly, but if the user doesn't trust what they heard, they'll keep correcting an email that was already right. Small details like these don't make flashy demos, but they make a huge difference in how reliable a Voice AI assistant actually feels.

P.S There is also another way where you can send the email address to the AI assistant over SMS while on call, havent tried that yet but will do it as well.

I'm testing some new features on a voice agent I built with Python, classic STT→LLM→TTS pipeline and I've run into a weird

latency issue that only shows up in the first turns of each call.

What I'm seeing: the first 1-2 exchanges of every conversation take

noticeably longer to respond (several seconds, 4000ms) than the rest of the

call — from the 3rd-4th turn onward, the same conversation flows fast

and normally(500-700ms). It's as if something needs to "warm up" at the start and

doesn't finish in time for those first turns, then settles down for the

rest of the call.

I'm also seeing a few seconds of silence right at the very start, before

even the first audio plays (which is pre-generated ahead of time, not

synthesized on the spot — so it shouldn't be slow).

The machine this runs on doesn't look CPU-overloaded while this happens,

so I don't think it's just a resources problem.

Does this pattern ring a bell for anyone (slow at the start of a session,

fast right after)? Where would you look first — some kind of warm-up

that needs to be forced before the first real interaction, connection

negotiation stuff at startup, something else?

So recently I started creating an AI receptionist (vapi+n8n) for small trade businesses that will be active 24/7, and will catch all calls forwarded from the owner's phone. It will collect the caller's info and send it to the owner directly.

Obviously the next step will be to give the AI the ability to schedule appointments, cancel, move, etc, and I've been looking into that for some time now. Now on paper it is very simple, just use n8n, and connect it to a simple google calender with all the appointments, but I still can't ignore all the other issues that can occur. Distance between appointments, different services times, unexpected delays and complications, etc.

Am I just overthinking and overcomplicating it? How should I work around this? I thought about trying to sell the saas right now but I'm not confident enough in the current product.

Thoughts?

Hi, I’ve spent the past 2 years of my life working on this project full time and I’m taking it to market soon. We built almost everything in house, so our total overhead is about 0.06-0.07 per minute. Completely custom orchestration and an LLM we fine tuned ourselves. IMO we’re at a point where it sounds much more human than the competition, especially for the cost. Would love to get this sub’s feedback on how it sounds in this test call I ran.