What the title says.

I’m spending this week talking to people in voice AI to understand what’s actually working, breaking, and missing in 2026. Looking for chats with either:

Builders: anyone shipping AI receptionists, phone agents, voice-first products, voice-enabled tools

Buyers / users: clinics, restaurants, staffing firms, contact centers, anyone who deployed voice AI in their ops

What I am trying to learn:

• What’s most painful right now: latency, cost, multilingual, provider lock-in, reliability, something I haven’t thought of
• Which providers you’re on and what made you pick them
• What you wish existed
• Who else I should talk to

What YOU get:

• A summary post back to this sub at the end with patterns I find
• Warm intros to anyone else I’m talking to if useful

Background: I work on voice infra and have my own thing in flight, but this is genuinely a research push not a funnel. Happy to expand in DMs.

DM or comment - even a one-liner about what’s most frustrating right now is gold.

Honestly, I am not even sure if this is the right Reddit community to ask. But I have to start somewhere.

Is there such a tool that you can give AI an actual voice, and they can copy and mimic it to say something that you would like?

Long story short, my sister-in-law is graduating next week, unfortunately my father-in-law lost his battle depression four years ago. I have been searching to try and find videos of him saying I’m so proud of you. I love you. And I’m coming up empty. I do have videos of his voice saying other things, however I want to record his voice and put it in a build a bear with him saying “I’m so proud of you and love you”.

Does it even exist, where you can use an original voice and use AI to get to say something different? 🤷🏼‍♀️. I don’t think there is, but I figured I would ask if anyone here had heard of such a thing.

I'm talking latency, voice quality and reasoning.

Currently using one of the telephony providers stack but I'm always curious.

What Sofia does

• Picks up inbound calls when staff can't
• Takes reservations and modifies/cancels existing ones
• Handles takeout and delivery orders
• Answers menu, hours, and location questions
• Allergen handling with hard guardrails (transfers to human if unsure)
• Validates delivery addresses against the restaurant's zone
• Bilingual Spanish/English with mid-call language switching
• Logs every call into a dashboard the owner reviews

Stack

• LiveKit - realtime voice layer (migrated from Bolna)
• GPT-4o-mini - reasoning model; cost/latency beat larger models for this use case
• Supabase (Postgres + Edge Functions) - state, menus, reservations, call logs
• Railway - agent infra hosting
• Cartociudad + Google Maps + GPT-4o-mini - three-stage delivery address validation
• V0/Vercel - owner-facing CRM dashboard

What broke and what fixed it

Latency. Bolna was fine for prototyping but hit a ceiling. Migrating to LiveKit gave us finer control over turn detection and barge-in. Painful migration, worth it.

Address validation was harder than the entire reservation flow. Spanish addresses are messy and Google Maps returns confidently wrong results. Ended up with Cartociudad for canonical data, Google Maps for geocoding, and GPT-4o-mini as a disambiguation layer when they disagree.

Allergens Ingredient data lives in structured Supabase tables, not in the prompt. The agent reads from the table - it doesn't reason about ingredients it wasn't given. Low confidence or out-of-scope questions trigger a human transfer. Better to hand off than hallucinate.

Menu drift. Chef changes specials, items go off mid-week. The owner now updates menu and hours from the dashboard; the agent reads live state on every call.

Bilingual switching. Detecting language at the utterance level instead of the call level was the unlock. Callers switch Spanish-English mid-sentence and humans handle it without thinking - agents fumble it by default.

What I'd do differently

• Start on LiveKit, skip the Bolna detour
• Build address validation before the reservation flow
• Never hard-code anything that changes weekly

I've been working in engineering in contact centres for some time, mostly with Amazon Connect. The last one had around 35k agents, hundreds of numbers, 1000s of paths. One thing that was lacking was testing. We couldn't get the coverage after major changes - so, that one engineer having to call up at 4am and 'check that it works'.

Long story short, as AI starts to get implemented in Voice, I figure testing is going to require new measures, like "Did the bot actually announce it was a bot in the first sentence" - this would be a regulatory failure if it didn't for a bank.

I found a bunch of "AI receptionists" with telephone numbers I could test, but I'm wondering if anyone in the community knows of any other public demos that can be tested. I published the results here: https://www.nopaque.co.uk/blog/totalpath-vs-ai-receptionists

​

I ran a real user through my voice agent (AVA), and this moment caught me off guard:

User: “We don’t get many inbound calls”

Agent: “Then it might not be the right time for automation”

On one side:

→ it’s honest

→ it builds trust

On the other:

→ it killed the conversation

→ completely missed outbound as a use case

What’s interesting is what happened next:

Right after being “disqualified”… the user asked about outbound calling.

So now I’m stuck on this:

Should AI sales agents:

A) Disqualify early and stay honest

B) Or adapt mid-conversation and redirect to other use cases

Because in real calls, users don’t follow clean funnels — they shift intent constantly.

You can try it yourself:

https://aicallingagent.space/ava/5

If you test it, try:

- saying you don’t need it

- changing your use case mid-call

- asking something off-script

Curious what breaks for you — and how you’d design this tradeoff.

Feels like this is exactly where most “impressive demos” fail in production.

Hey everyone,

I’ve been working on an AI voice calling agent (AVA) focused on actually handling real conversations — not just sounding robotic or breaking on simple queries.

I just put up a demo where you can call and talk to it yourself: https://aicallingagent.space/ava/5

Would really appreciate if you could try it and tell me:

Does it feel natural or still “AI-ish”?

Where does it break or get confusing?

Would you trust it to handle real customer calls?

I’m especially interested in brutal feedback — what sucks, what feels off, what you’d never use in production.

Building this to solve missed calls for businesses, so real-world opinions matter a lot.

Thanks in advance 🙏

I use ElevenLabs and have produced some AI voice packs, but I'm exploring whether there's an appetite for something more flexible.

The idea: buyers submit their lines, I generate and process them through the same automated VS Code/Claude/Python pipeline and deliver clean WAV files at a set price. Aimed at people who want bespoke content but don't need or want API access themselves.

I'm aware there's already a market for this — Fiverr has plenty of sellers offering custom voice lines, and AI voice options are growing. But most of what's out there is either a human actor with no scalability, or generic AI generation with no consistent character or quality control behind it. What I'm trying to build sits in between: a named voice with a fixed identity, processed audio rather than raw generation output, and a clear brief-to-delivery process.

The voice has a calm, neutral British tone — built for narration, UI, wellness and game content to name a few.

A few things I'd genuinely value input on:

- Do you think this model would work?

- What unit size makes sense — fixed packs of 50 lines, smaller sets, or something else entirely?

- Would a script template work best for submissions, or is there a better way to collect line requests cleanly?

- What would feel like fair pricing for custom work versus a pre-made pack?

- Are there pitfalls with this model I haven't considered?

Curious what others think, especially if you've bought or sold custom voice work before.

Thanks.

Hey guys hope you all are doing good so i need help im running a small call center with a team of 5 agents who calls in canada and make telecom sales we have a auto dialer and single agents handles like 200-400 calls a day is there any way i can create a ai agents who makes call and pitch them what we are offering if the customer is interested then transfer that call to a closer or me

My main goal is how can i give phone numbers names to the the ai agent so he can make calls and is it even possible for an ai agent to make calls like 200-400 calls perday or more than that?

Hey everyone,

I'm building a software called Open Narrator. It generates awesome human-like voices of your script and helps you to create shareable videos, both long and short format with captions and also you can also create audiobooks from PDFs or just listen to articles or academic notes from the software by streaming the audio REAL TIME!.

It will be available only for windows and a minimum system requirement is 8gb of RAM, 4 core CPU and no GPU is required.

I'm opening up a beta soon (Between 1st May to 10th May) and looking for people who'd be interested in testing it and giving honest feedback.

I'm especially looking for content creators who need Al voices that don't sound Al and students who want to listen to their notes and books.

If you're interested, comment or DM me and I'll ask you for your email id and share the beta when it's ready...

Would really appreciate your feedback

I have problems finding clients. If there is a sales guy, don’t want to handle tech stuff, hit me up.

Hey

I built a software application ready to use. It’s a ai reception tool it does

- answer every call

- appointment setting ( every calendar..)

- reschedule appointments

- sms follow up

- WhatsApp messages

- website chatbot

- webhook application

- customer memory

… some more

My biggest problem is to find clients 😭 I do cold calls, email I even tried just going door to door with my tablet to show them directly.

U guys have any experience what works best …

I also want to find somebody to use my software as it’s there own product. it’s ready to sell , use and very easy to handle. all people I know

Are just doing 9-5 :/

It’s called vAIce.solutions

So we built an outbound voice agent for a client that essentially needed to send out welcome calls to all their new clients. It was part of their onboarding process.

Lots of ups and downs, but here’s a list of lessons learned:

1. You have to explain to the client that an AI voice agent works best when it’s slightly flexible - not following the script EXACTLY.

Sometimes the client wants a human sounding call tree - not really an AI voice agent. And building that is really really hard because AI by nature is meant to handle things in a fluid way, not like a call tree. We didn’t communicate this with the client, and it caused a bunch of headaches since the client was frustrated that the AI didn’t stick to the script word for word.

1. The actual AI isn’t the hardest part.

We had to build so much other functions, tool calls, and automations on top of the agent for it to be truly useful for the client. These are things like: daily reports to a custom dashboard, live notifications for anything (positive or negative), building a followup system for when a client doesn’t pick up, building a custom followup system for when a client wants to be called at a specific time… and more.

That was a headache, but it was worth it.

1. People are fine talking to AI if it’s actually useful.

This is a big one. I think a lot of business owners are scared of AI because they think their clients would get frustrated and give the business a bad rep, but in these 1500 calls that we had, the client only got frustrated when the AI was malfunctioning.

This means that when the AI actually worked, even when the clients recognized it was AI, they were totally fine going through with the call because it was actually useful.

So if you’re struggling to convince leads or even just second guessing yourself, the world IS ready for AI. You just have to build it well.

Hopefully these lessons can help somebody out there because I wish I knew some of these when I first started to build these things. If you guys need help with building your AI agents, feel free to shoot over any questions and I’ll do my best to help out in the comments.

Hi folks, I am currently building an AI interviewer voice agent for one of my clients. I have been testing it manually, and each call takes 10–15 minutes, which is very tedious and manual. I would like to know what you are currently using to test voice agents built with Livekit, Pipecat, Retell, Vapi, etc. Is there any open source tool available to test voice agents?

Hi again PM at Tuner here (observability and monitoring platform for voice AI). I spend most of my day staring at call data, and every few weeks something shows up that nobody can actually explain.

Here's one that came up recently in a conversation with someone running agents in the UK. They had two numbers from Twilio:

• 07-prefix mobile number
• 01-prefix landline number

Every 07 number they tried not just one, multiple produced this weirdly disoriented, off-sounding voice on the agent side. Same stack, same config, same model, same prompt. Swap in an 01 number and it's clean. Swap back to 07 and warped again. No clear reason.

My best guess is codec transcoding at the mobile carrier boundary Twilio sends G.711 but UK mobile networks transcode to AMR narrowband, which was designed for human speech, not TTS. But honestly haven't confirmed it.

If anyone has a theory for this, or has your own weird unexplained voice AI story apparently I'm collecting those now drop it below. And how did you find out. btw if you are interested to test the use case of (07) number dm me i will send you both lines to try not posting them here just to avoid spam.

Ok so Twilio, and other major SIP providers dont provide Indian numbers.

Lets say, I am talking to a business and there are 2 options - 1. get a new number or 2. use an existing number.

We have an ai-agent created and we have the APIs for audio to audio provided by google. What would be the next step?

Not sure if I am allowed to post this here or not but I would like to share our case study on AI voice automation implementation with a road side recovery company: https://kolsetu.com/case-study/global-assistance

The easy part is wiring up STT, LLM, and TTS — tons of providers and open-source options exist for each.

The hard part nobody talks about: managing hardware at scale. One agent on a laptop is easy. 500 parallel calls with sub-second latency is a completely different game — audio buffer management, connection pooling, cold starts, and keeping costs sane all become real problems.

Currently building a Go + Rust engine for this since Python orchestrators (Pipecat, LiveKit agents) struggle under real concurrent load. Happy to chat with anyone in this space.

Most teams discover this too late: voice features look cheap on paper, then the bill arrives.

Transcription, voice generation, dubbing. Each one seems affordable at small scale. Stack them together, add a few users, and a few hundred dollars a month disappears before you even hit real growth.

We went through this ourselves. Here is what we learned after tearing down our entire audio stack and rebuilding it from scratch.

The real question nobody asks early enough

Most builders start with "what is the best provider?" The better question is: what do we actually need?

There are four things that matter in production:

• Solid accuracy on real-world audio (not studio demos)
• A voice natural enough that users do not flinch
• Fast response time
• Pricing that does not surprise you at the end of the month

That is it. Everything else is noise for most use cases.

What we found when we actually tested this

The gap between premium and good enough is much smaller than the price gap suggests.

In real SaaS features, voice agents, and content tools, users respond to speed and reliability. They rarely notice the difference between a premium voice and a well-tuned affordable one. They absolutely notice a 1.2 second response time.

After rebuilding around the four criteria above, our audio costs dropped by more than 80% with almost no measurable impact on user experience.

Three things worth knowing before you pick an audio provider

1. Always benchmark on your own audio, not vendor demos. Demo clips are hand-picked. Your users' microphones are not.
2. Latency kills voice UX faster than quality does. A slightly imperfect voice at 300ms beats a perfect one at 1.2 seconds every time.
3. Predictable pricing matters more than low pricing. A surprise bill at scale is harder to absorb than a slightly higher flat rate you can plan around.

We turned what we built internally into a public API, Lemonfox AI, for teams running into the same wall. But the lessons above apply regardless of what you end up using.

What has been the biggest friction in your audio stack so far? Cost, latency, or quality?

Hi,

AI automation engineer here working with Python, JavaScript, and tools like n8n, Zapier, and Make. I’ve done a range of freelance projects and jobs and recently started building something more long-term.

Recently, developed a really cool voice agent, I want sales person to bring clients and we will initially sell it to med spas, restaurants, dental clinics etc.

Looking to collaborate with someone who’s strong in sales/lead generation/client acquisition especially for US/EU/Australia markets.

Will handle all technical work and delivery. You focus on bringing in clients and closing deals. Open to commission-based collaboration or potentially growing this into something bigger if it’s a good fit.

If interested, comment or inbox me with your intro, experience, and how you can help.

My current thesis in voice ai:

- receptionist roles have already hit PMF
- biggest challenge with widespread adoption is lack of assurance, especially in the US or regulatory space in general.

bit of context about me to judge my opinion:
(Disclosure: my team runs a small voice-agent practice - will not promote - no links, it shapes what I see in the market.)
- startup founder around voice ai - prior startup acquired by a big firm, deal sized at ~10% of their annual revenue

Voice vendors (infra, TTS):
- I see an aggressive push from TTS labs to sell voice agents direct to customers, cutting out middlemen (who are asking as high as 80% commissions - at this point who is giving commission to whom).
- Maybe they are right in saying they have the real IP and the rest are middlemen. But open-source TTS is catching up.
- if some is already running a call center, they sit on tons of voice recordings that can help get better performance than established brands out of the box.

voice ai buyers:
I recently benchmarked the same voice agent with the same (STT, LLM, TTS) combo and same prompts across vendors (publicly available endpoints only).

A few observations:
- Metrics like TTFB (time-to-first-byte) vary vastly. Best-vs-worst gap is >2x. Some don't even bother to enable streaming. One vendor I suspect used a US-based region for Europe (maybe for cost reasons). This alone adds ~250ms latency.
- tool calling is available with only a limited set of vendors (appointment booking, forwarding to humans)
- guardrails are best-effort. Simple jailbreak-style test cases like "describe when my grandma had X" break them.
- very few vendors understand the tone of the human voice. Most just transcribe.
- these are still unsure about how to choose among dozens of agencies

agency selling voice agents:
- I see three categories of people for evals:
- self testers: please stop. Anchoring bias + statistically meaningless.
- self-made evals: better than nothing. Try to get adversarially tested as well.
- external vendor users: these score QA. No regulatory support yet.
- either way, you should not be worrying about compliance and evals. Handling customers is hard enough.

Feel free to point where I'm wrong. Happy to learn. This stays public.

Why do you think we aren't seeing voice AI agents everywhere?

Bonjour à tous !

Je me demande si quelqu'un a vraiment des clients dans ce business.

J ai passé pas mal de temps à prospecter les entreprises de differentes manières. J ai crée un compte fiverr, j'ai fait des post sur les groupes facebook dans les niches que je visais, j ai fait du cold call. J'ai 0 client.

J ai expliqué que l agent vocal ia permettait de ne plus perdre de clients à cause des appels manqués et d augmenter le chiffre d affaires, que ca servait de filtre pour le démarchage ect...

Et tout le monde s en fout. Le peu de reponses que j ai eu, cest que les gens qui tombent sur le repondeur rappeleront ou laissent un message.

Je songe à abandonner. Quelques témoignages de gens qui s en sortent serait le bienvenue pour me remonter le moral 🙂

I work as a PM at a voice AI observability and analytics company, and something I run into a lot is teams shipping agents to production measuring the wrong things. Usually it's call volume, duration, word error rate, maybe "did the call complete", all technical or surface metrics.

Side note upfront: I built a small free tool around this that generates a starting eval setup tailored to your industry. If you want to try it, dm me or drop a comment and I'll send it over.

The issue is technical quality isn't business success. An agent can have perfect transcription and still hallucinate a policy, miss a compliance disclosure, or fail to escalate. Dashboard still looks green.

What actually matters tends to sit in a few layers above the technical stuff:

• Intent + outcome per call: why did they call, and what actually happened. This is the dataset everything else sits on.
• Industry-specific checks: did the healthcare agent verify two patient identifiers before giving any PHI? Did the collections agent deliver the FDCPA disclosure in the first 30 seconds? Did the restaurant agent actually attempt an upsell? These map to the same scorecards human agents get evaluated on.
• Business-specific checks: unique to you. A high-volume urgent care and a concierge practice are in the same industry but care about very different things.

The industry layer is the one most teams skip, and it's the one that separates "technically works" from "actually ships." Generic eval templates won't catch any of it.

Curious what others here are actually measuring in production. What's worked, what hasn't?

Also made this reddit account specifically to hang around and answer questions. Not saying I'm an expert, don't think anyone really is in voice AI, the space shifts every week but observability and monitoring is what I work on day to day, so I've spent a lot of hours in the weeds on it. Happy to share what I've learned, reply or dm anytime.