r/AIVoice_Agents

It feels like there's so much hype around AI for voice customer support these days, yet almost every time I call a company, I end up in the same old experience where I have to press 1, 2, or 3, repeat myself several times, or get stuck in a loop. It rarely feels like AI has actually made the experience better...

I've been trying to find examples of companies that have actually built good AI phone support, but most articles just talk about the vendors behind the technology.

I'm not looking for companies like ElevenLabs, or similar that provide technology. I'm looking for the actual brands you've called and where you thought: "That was helpful/good/etc."

Any experience you could share?

After building AI products for the past year, I noticed something.

A lot of "AI developers" aren't building AI.

They're building API wrappers.

There's nothing wrong with that.

But adding an LLM to an app isn't the hard part anymore.

The hard part is:

Evaluation

Memory

Context engineering

Agent orchestration

Failure recovery

Cost optimization

User experience

Guardrails

The model writes 100 lines of code in seconds.

You'll spend weeks making those 100 lines reliable.

Ironically, AI didn't eliminate software engineering.

It made good engineering more valuable.

Has anyone else felt this shift?

I'm trying to reasearch the topic but struggling. I have specific voices in mind. Some may be widely available like Mr Ping, but others I want like Arkantos from Age of Mythology, probably not so common. Whats the best way to go about this, and how much money can I expect to be forking out?

I work as Quality control Inspector in aviation, but I’ve been building side projects in AI for a while. A property management contact kept complaining that half their calls went to voicemail during showings and nobody calls a voicemail back.

So I built an AI voice agent that picks up instantly, has an actual conversation, qualifies the caller, and books the appointment. Bilingual too (EN/FR I’m in Montreal, so that wasn’t optional).

Things that surprised me:

People don’t hang up when it answers in under a second. That alone changed the math.
The hard part wasn’t the AI it was making the French/English switch feel natural mid-call.
Most of the “wow” from early testers wasn’t the call itself, it was that everything synced to the CRM automatically after.

Still learning a ton here. Anyone else dealing with missed-call leakage in leasing offices or brokerages? What have you tried? call centers, other voice AI, just eating the loss? Genuinely curious how others are handling this. (Built it under Anova, if anyone wants to look not here to pitch, just comparing notes.)

This is something I've been thinking about lately.

I work in digital marketing and regularly have client meetings with people from the US, UK, Canada, and Australia.

The biggest challenge isn't understanding English, it's speaking fluently in real-time.

There are millions of professionals around the world who are excellent at their jobs but hesitate to speak during meetings because English isn't their native language. Sometimes people are judged more for their fluency than for their actual knowledge.

With today's AI, we already have:

- Speech recognition

- Real-time translation

- AI-generated voices

So why can't meeting apps combine these into one feature?

Imagine this:

- I speak naturally in Hindi.

- The client hears fluent English in real time.

- The client replies in English.

- I hear or see the response translated into Hindi instantly.

It wouldn't just help Hindi speakers. It could help people who speak Spanish, Portuguese, Japanese, Korean, French, German, Arabic, Chinese, and many other languages.

I genuinely think this could change how international business works and create opportunities for millions of skilled professionals who simply aren't native English speakers.

Would you use a feature like this if Zoom, Google Meet, or Microsoft Teams offered it?

I'd love to hear your thoughts. Is this something you'd trust, or would you still prefer speaking directly in English?

We’re about eight months into a contract with a traditional call center outsourcer and it’s been rocky. Coverage gaps during peak hours, inconsistent agent quality, and a support ticket backlog that takes days to resolve. Our inbound volume is mostly appointment confirmations and basic account questions, which honestly feels like it should be automatable at this point.

About six weeks ago I started seriously evaluating AI calling platforms as a potential replacement. We’ve gotten far enough in the process that we’ve actually run a limited pilot on one of our lower-stakes call queues. The results on handle time and availability have been promising, but the handoff logic for escalations still needs work on our end. It’s not a platform problem exactly, more that our internal escalation rules were never documented clearly enough to configure properly.

The part I’m less sure about is the transition itself. We still have a few months left on our current contract, and I’m weighing whether to run both in parallel through the end of the term or just eat the early termination cost and commit fully. Anyone who has been through a migration like this, did you find it cleaner to do a hard cutover or phase it out gradually?

I made a mistake that ended up costing me a couple of months, so maybe this saves someone else the same pain.

My first voice agent stack was built with Retell AI. The backend was n8n, client data lived in PocketBase, and I had a custom CRM built in Next.js. Everything was hosted on Google Cloud.

The setup worked, but hosting costs kept increasing, so I migrated from PocketBase to Supabase. The built-in Row Level Security (RLS) was a huge upgrade.

Later, I wanted to move away from Retell because of the pricing, so I rebuilt everything using LiveKit.

That's where I made the biggest mistake.

I connected LiveKit directly to my CRM instead of putting my backend in the middle. It looked cleaner at first, but when something broke, I had no visibility into what was happening. Every bug meant digging through application logs and tracing code manually.

With my earlier n8n backend, I always knew exactly where a request failed. Without that middle layer, debugging became painful.

After spending weeks trying to make it work, I dropped the project.

Then I tried Dograh instead of LiveKit.

Unfortunately, I repeated the exact same mistake. I connected Dograh directly to the CRM again, without a proper backend layer. Same problems. Same debugging nightmare.

That was the point where I stopped chasing providers and redesigned the architecture instead.

Now I have a custom CRM backed by a dedicated n8n backend running on my own server.

Whether I use Retell, LiveKit, Dograh, or any future voice platform, I just create the agent, connect it over WebSocket or API, and everything plugs into the same backend.

The CRM never needs to know which provider I'm using. Swapping providers now takes minutes instead of weeks.The biggest lesson wasn't about Retell, LiveKit, or Dograh.

It was this:

Never let your frontend depend directly on your voice provider. Put your own backend in the middle. You gain observability, flexibility, and the freedom to switch providers whenever you want.

One thing I genuinely believe, though: self-hosting the open-source version of LiveKit or Dograh on your own server gives you much more control over the entire system. The infrastructure cost is relatively low, but building and maintaining that stack properly is still a significant engineering effort. Realistically, it would take me another 3-4 months to get it to the level I'd want.

Right now, this architecture gives me the best balance between speed, flexibility, and maintainability. I can switch providers in minutes instead of rebuilding my entire system every time.

Iam thinking about an AI voice enchanger for creators and influencers , and i think is they really want that or its only in my mind can you help for that

I’ve had this happen to me many times trying to see if I’m the only one out here or if there are other people that have experienced this I have it recorded. Thank you for comments and replies and for reading my post.

​Hey everyone,

​I'm a technical founder currently building conversational AI voice agents (handling inbound/outbound workflows, CRM syncs, etc.). The technical architecture is fully functional, but I’m looking to connect with someone based in the US to handle the business development and operations side.

​Since I am focused entirely on the engineering, I'm looking for a partner who can take ownership of:

​Go-to-Market Strategy: Identifying the right industries and use cases.

​Outreach & Sales: Managing early-stage discovery calls and client relationships.

​Operations: Navigating the US market and compliance landscapes.

​If you have experience in B2B sales, agency growth, or tech operations and want to team up on a serious voice AI venture, I’d love to chat.

​Drop a comment or send over a DM with a bit about your background!

Hi everyone,

I’m looking for advice from anyone who’s built software in the restaurant/POS ecosystem.
Over the last 6 months, my co-founder and I built an AI phone agent for restaurants. The product is production-ready and can:

Answer incoming restaurant calls
Take orders conversationally
Handle menu questions
Upsell items
Send SMS order confirmations
Transfer calls to staff when needed
Create orders directly in the POS

The entire voice infrastructure is built and working.
Our challenge is distribution.

We initially built a Clover integration using their APIs. After months of work, Clover’s legal team rejected our app, saying they currently don’t have an established policy for third-party AI applications.
We explained that:

Call recordings are optional and controlled by the restaurant.
Recordings stay in Twilio (not our servers).
We only store transcripts needed for restaurant review.
We don’t use customer conversations to train foundation AI models.
Even after clarifying all of that, the app wasn’t approved.
We also explored Toast and Deliverect, but those paths haven’t worked out either.
What confuses us is that companies like Loman AI and Certus AI publicly advertise Clover integrations and appear to offer very similar functionality.
So now we’re at a crossroads.
We see a few options:
Continue pursuing POS approvals (which could take months with no guarantee).
Integrate with Square and hope the approval process is smoother.
Remove the POS dependency entirely and become an AI receptionist that sends orders to staff for manual entry.
Pivot into a broader AI phone agent for other industries (HVAC, dental, plumbing, etc.) using the voice platform we’ve already built.
My questions are:
Has anyone here successfully launched a product that depended on Clover, Toast, or another POS approval process?
Is this kind of legal rejection common for startups?
Would you keep investing in restaurant POS integrations, or pivot to a product that doesn’t depend on a third-party platform approving you?
If you were starting today, what would you do?
I’m not looking to complain about Clover or any other company. I’m genuinely trying to understand whether this is a normal hurdle in enterprise software or a sign that we should rethink our strategy.
I’d really appreciate hearing from anyone who’s built in this space.
Thanks!

Hi! We’re looking to collaborate with someone who has already built a production ready AI voice calling agent. We’re not looking for someone to develop one from scratch..we’re specifically seeking an existing solution.

This is a strategic collaboration opportunity focused on educational institutions, with the potential to unlock a large new market together.

I have tried Vapi, Retell, Pipecat and Bland AI platforms.
So far Vapi is my favorite one of those for more customization options. Also I am using n8n for connecting workflows so they are always accurate.

But since I need very deep customizations even Vapi is sometimes short on that. Because I am not very technical and can’t code I never messed around with LiveKit too much.

So has anyone built a successfully working complex voice agent with LiveKit and can say if it is worth it to learn to at least read code?

Maybe someone has built a successfully working complex voice agent with other platform than LiveKit? If yes, can you share? Thank you:)

Hey everyone — I’m doing a small user research project on how people use voice input in AI coding workflows, especially with tools like Claude Code.

I’m not selling anything or recruiting for a beta. I’m trying to understand the real workflow pain points: dictating prompts, explaining bugs, editing code by voice, replying to work messages, writing docs, or talking through requirements while using AI coding tools.

**I’m looking for people who:**

* **Use voice input heavily as part of their workday** * **Use Claude Code or other AI coding tools regularly** * **Have run into real friction with voice input, not just “it’s kind of annoying”** * **Are currently working in the US**

Bonus if you’ve tried any dedicated voice/input hardware, like a wireless mic, recorder mic, external keypad, presentation remote, foot pedal, or anything else you use to control your workflow.

Also very interested if you use voice outside a desk setup — mobile coding, talking to AI while walking, commuting, moving around the house, etc.

**The interview would be a 60–90 min video call. Compensation is a $75–100 Amazon gift card, depending on fit and interview length.**

Mostly I’d love to hear how you actually use voice in your Claude Code / AI coding workflow, what breaks, what tools you’ve tried, and what you wish worked better.

If this sounds like you, comment below or DM me with a quick note on how you use voice input + AI coding. Happy to answer questions too!

I've been in the Voice AI space for the past year, and the more I explore it, the more I realise how vast and fast growing it really is.

To stay on top of things, I'm spending the next 3 days exploring as many voice agents as I can. Have already tried 5 since morning.

If you're a founder, builder, or voice ai company, send me your voice agent. I'll talk to it and test it across at least 5 different scenarios and share my evaluation with you.

I'm doing every test myself, no automations.

🔴🔴🔴 This is a recorded line. Every call is recorded and transcribed (I use the recordings to improve the system, and you'll also hear this notice when you call in). By calling, you're consenting to being recorded if you're not cool with that, don't call. Don't share anything private, sensitive, or personally identifying treat it like a public demo, because it is. 🔴🔴🔴

🔴🔴🔴**It's a regular Canadian number (Quebec, area code 450), not toll-free, calling may cost you long-distance or international rates depending on your carrier and country.**🔴🔴🔴

I've been building a self-hosted AI voice agent platform (Asterisk + local GPU models, running on a little 4×RTX 3090 rig in my place). So come call it.

📞 Number: 1-450-400-1274

When you call, you'll get a menu.

Press a key to talk to one of the personas:

• 0 – Me (the actual human who built this, so, maybe dont?)
• 1 – Larry's Lambos 🏎️
• 2 – The Turtle Specialist 🐢
• 3 – Kim from Hensley and Sons Hardware Supply
• 4 – Hot Dog Barn 🌭
• 5 – Gerald from Hensley and Sons Hardware Supply
• 6 – Edna
• 7 - Agatha the Astrologist 🔮
• 8 - Sofia, the Mallorca travel specialist 🏝️
• 9 – The Chashu Hotline 🍜

Just talk to them like a normal call it's real-time speech-to-speech, no app, no signup.

A few honest caveats:

It's a tiny home rig that can only handle a handful of calls at once. If you get a busy signal or a rejection, it's swamped wait a few minutes and try again.

The AI will say weird/wrong things. That's half the fun. It can't do anything except talk (no transfers to real services, no texting you, nothing).

It's a hobby project, not a company. No data is sold; recordings just live on my box.

Would love feedback on latency, voice quality, and how the personas hold up. Roast away.

🔴🔴🔴 This is a recorded line. Every call is recorded and transcribed (I use the recordings to improve the system, and you'll also hear this notice when you call in). By calling, you're consenting to being recorded if you're not cool with that, don't call. Don't share anything private, sensitive, or personally identifying treat it like a public demo, because it is. 🔴🔴🔴

🔴🔴🔴**It's a regular Canadian number (Quebec, area code 450), not toll-free, calling may cost you long-distance or international rates depending on your carrier and country.**🔴🔴🔴

We've made incredible progress over the last year.

LLMs are smarter than ever.

STT is highly accurate.

TTS can sound almost indistinguishable from a real person.

Latency is getting close to real-time.

And yet... after talking to most AI voice agents for less than a minute, you still know it's AI.

Personally, I don't think it's just latency or voice quality anymore.

It feels like humans subconsciously pick up on hundreds of tiny conversational signals—knowing exactly when to speak, when to pause, when to interrupt, when to acknowledge with a quick "mm-hmm," how to recover from awkward moments, and how to adapt naturally as the conversation unfolds.

I'm curious what others building in this space think.

If you could solve only ONE problem to make AI voice agents genuinely indistinguishable from humans, what would it be?

Turn-taking?

Interruptions (barge-in)?

Endpoint detection?

Backchanneling?

Emotional prosody?

Long-term memory?

Context switching?

Something else entirely?

I'd love to hear from people building production voice agents. What has been the hardest problem for you to solve—and why?

Hello dentists,

I have a question for you. We are considering implementing an AI voice agent for dental practices that can book appointments, reschedule appointments, and follow up with patients essentially functioning as an AI receptionist.

Do you think this concept would be helpful for dental clinics, especially in the USA? I’d love to hear your thoughts and feedback.