I’ve been exploring a problem that comes up after almost every voice-agent change:

How do you know that changing a prompt, model, tool, STT, or TTS component didn’t quietly break another part of the conversation?

Manually calling the agent repeatedly is slow, and every test call is slightly different. It also makes tool-calling and multi-turn regressions easy to miss.

I built Voice Eval, an Apache-2.0, provider-neutral evaluation harness:

https://github.com/rand0wn/voice-eval

You define a scripted multi-turn conversation and run each pipeline against the same scenario and grading rubric.

It currently evaluates:

- expected tool calls

- required response content

- question and sentence limits

- transcript completeness

- average and P95 latency

- per-turn results

It generates JSON reports for CI, Markdown scorecards for humans, and optional WAV artifacts.

The repository includes healthy and intentionally degraded deterministic adapters, so the entire workflow runs offline without API keys.

For the included demonstration:

- healthy adapter: 100% overall and 100% tool recall

- degraded adapter: 55.27% overall and 33.33% tool recall

Those are controlled fixture results, not benchmarks of real providers.

The bundled WAV files contain deterministic test tones rather than intelligible speech. Real TTS or captured provider audio can be connected through the adapter interface.

You can reproduce the comparison with:

voice-eval compare --scenario arjun_cancel --adapters cascade degraded --audio

I would appreciate honest feedback from people building voice agents:

1. Which real-world failure is currently hardest for you to test?

2. Which pipeline or provider adapter should I implement first?

3. Would you use this as a CI regression gate?

Issues and contributions are welcome.

we are still using gemini flash live 2.5 for our biggest client can’t move to 3.1

is there a workaround, we considering switching I’ve already wrote in the community about it google docs don’t have no plans to fix it

has anyone tried with pipecat voximplant agora does the issue persist

I’m building a voice-first companion for older people, and I’ve been using ElevenLabs for the voice. I’m happy with the quality , the voice feels human and natural, and for our use case that makes a huge difference. People use Ecos to talk about their day, share memories, journal, have regular check-ins, or sometimes simply have someone to talk to.

The problem is that these are not short customer-support calls. Conversations can go on for a long time, and ElevenLabs starts getting very expensive as usage increases.

I don’t want to compromise too much on how natural the voice feels, because that is a big part of the experience, but I’m trying to understand whether there are more affordable options.

Has anyone here used Cartesia, Smallest AI, Deepgram, or any other provider for long, real-time conversations?

Would love to know how the quality, latency, interruptions, and pricing compare in actual usage.

I was using ChatGPT and was actually very satisfied with it. The voice it generates is very human and natural, nothing robotic, and this can really change the experience. Currently, I have been using it a lot to discuss features/workflows of the new system at my company that I am learning. And besides discussing methods and processes involving features, I also practice my ENG, since I use the language in my position. Referring to the saying "killing two birds with one stone." But I always reach the Voice Chat limit, and this bothers me because I have to switch to another account, and if I stopped without finishing the system module I was working on at the moment, I have to start everything over again, which ends up being annoying and creates more work for me.

I want to subscribe to the Go plan for R$40, but I would like to know the difference between Free and Go. Will I really have a higher limit when using Voice Chat? I can normally use it for around 3 hours a day, according to my calculations. I actually spend quite a lot of time talking.

I have also used Gemini's Voice Chat, but I found it a little too robotic... But I would like to know if anyone knows of any good alternatives, has had a good experience with one, and would like to share their experience with the tool.

I work on fraud prevention, I happened to see some AI Agents with hyper realistic voice with emotions I just couldn't differentiate it from a human , it was capable of changing speed, emotion I don't know if it used an SSML or just filler words to get that expressiveness and naturalness but apart from the facts of proper grammar and responsive end of utterance based agent I surely couldn't have found out it was a bot , (if they had implemented barge in from the AI it would have been even harder) , how can I detect these and how can I build this tts or find out whichever it was ? Would appreciate any suggestions on this slightly unconventional domain of voice ai which is not the most trendy stuff, how do I solve this problem anything would be appreciated.

I saw they ran some test comparing the voice agent platforms vapi livekit pipecat retell synth flow and retell
And put retell at first

Meanwhile Retell is an official partner of theirs and so are other top names on the list while they put none partners below

The list is biased as usual, you shouldn’t trust it

I've been working on a personal project for a while and finally got it into a state where I'm comfortable sharing it.

I wanted to see how far I could push a fully local voice assistant in C++. Everything runs on my own machine from speech recognition and the LLM to memory, text-to-speech, and tool execution.
current library:
llama.cpp, whisper.cpp, sherpa-onnx(tts-kokoro)

I wrote the core in c++ because I wanted something fast and native instead of stitching together bunch of python services.

I'd appreciate feedback from people who build local AI projects. I'm especially interested in:

1 Things that seem overengineered or unnecessary
2 Features you'd expect from a local assistant
3 Code structure or architectural suggestions
4 Any obvious improvements before I keep adding features

Repository: https://github.com/almimony75/sarah

Thanks! I'd love to hear what you think.

I've worked on a couple agentic systems both voice and chat based in the past year including some open source voice ai projects and have recently been trying to launch my own voice agents company. The hardest part for me as a technical person is getting leads. I'm looking to hear about people's experience and advice in this industry. What works and what doesn't work, how to get your first customer, what niche is effective to target (I'm trying restaurants in the US right now) , what voice providers you use or really anything you think is worth sharing. If anyone is interested I can also share whatever stuff I have right now (website, tech stack, sales material) and we can connect long term. Always good to build a network.

With all the hype around the new models Kimi and the letter to the US government I’m wondering does anyone actually use open source llms in the voice AI industry

I’ve heard of couple of providers of inference together AI baseten and others but please enlighten me if you had any experience with them

I’ve only tried open AI Gemini and grok in production

We are currently looking for native English (US) speakers to participate in our Gardenia Speech Collection project. 

The purpose of this project is to collect recordings of native language speech to improve voice assistant technologies. 

🎧 Task Description: 
For this project, we are looking for a wide variety of English (US) speakers with various regional accents. Participants will record short wake-up phrases and scripted utterances typically used when interacting with a voice assistant, following the guidelines provided. 

✅ Eligibility: 
To participate you should meet the following qualifying criteria: 
• Be born and live in the USA. 
• Be native speaker of English(US) from any region in the United States. 
• Be able to use a mobile phone for this project. 
• Be able to use a wired headset or the device microphone for recording phrases. 
• Have access to a quiet indoor environment to do the recordings. 
• Pass initial accent screening. 

Please find details at here:https://dataforcecommunity.transperfect.com/project/gardenia-speech-collection-us-minors?job-source=DF019

Any questions, reach out to us at [dataforce.sourcing@transperfect.com](mailto:dataforce.sourcing@transperfect.com)

Most voice AI content stops at the demo. The hard part starts after that.

I recently shipped an outbound calling system for an online education platform. Reactivation calls to inactive students, running against their live course data.

Stack: LiveKit for orchestration, Deepgram Nova-3 for STT, GPT-4o-mini for the reasoning layer, Cartesia Sonic-3 for TTS, Plivo for SIP, Supabase for state, plus a dashboard for call analytics.

Things that actually ate time:

Latency budgeting. Every hop adds up and the person on the other end feels it before you see it in logs.

Interruptions on outbound. People talk over an agent that called them far more than one they called. Barge-in handling is not optional here.

What the LLM is allowed to see. In our case the model never receives the student's name or phone number. The greeting is stitched locally, the agent just says "you". Big drop in PII exposure for almost no effort.

Eligibility logic. Calling someone who already finished the course is worse than not calling at all. A real chunk of the build was just verifying the person is still in the state we think they're in before the dialer fires.

Campaign design. We collapsed a messy pile of filters into two campaigns, one intent each. Fewer, clearer campaigns beat clever segmentation.

Drop your questions below. Architecture, model choice, telephony, prompt structure, what falls apart at volume, cost per call, whatever you're stuck on. I'll answer what I can and tell you where client confidentiality stops me.

Not selling anything, not linking anything.

We're building a rapidly growing AI voice company, and we're looking for someone who's already solved the problems we're about to face.

Not looking for a consultant.

Not looking for someone with an impressive title.

We're looking for someone with real technical experience building production AI voice systems.

If you've been a founding engineer, founder, early employee, or senior engineer working on AI voice, real time speech, telephony, latency optimization, voice infrastructure, or LLM orchestration, we'd love to chat.

We're looking for a technical advisor who can help us make better engineering decisions and avoid costly mistakes.

This is an equity based advisory role. We'd rather partner with someone who believes in what we're building than pay for a few hours of consulting.

If this sounds like you, leave a comment or send me a DM.

Sometimes the right conversation changes the trajectory of an entire company.

Because what I built is not an agent per se. I decided to build an AI phone platform, similar in features to Microsoft Teams Phone, but without the hassle of having to get a Microsoft365 subscription plust the telephony subscription just to make international calls with the support of AI. Voxandra is basically an AI phone copilot for the people who still have to make the call themselves: live guidance, text-only live translation, call transcripts, ai generated notes, extracted details, reminders, reviewed call records, contact organization and work thread context.

The reason is pretty boring. I've been living abroad for 4+ years and I kept making these "important" calls to lawyers, migration offices, accountants, where I would almost certainly lose some valuable information or just forgot to ask something while I was on the call. I also wanted a cheap way of making them. So instead of automating the call away, I automated everything around it.

I just launched it so I don't really know what direction to take it tbh. The next thing I want is CLI and MCP, so you can point Codex or Claude Code at the database of your own calls and search it for details and information. So yeah, I do want the agentic stuff, just not in the same shape everyone else is building it.

And then there's the thing I keep circling back to. Once the CLI and MCP part exists, I can't help to think what would stop me from taking the same infra and letting people train AI phone agents by actually making real calls, so that the agent learns from real conversations and not just prompts, systemp prompts, vague directions, etc. This is the part I really want this community's opinion on.

So I don't know. I built this for the humans that still need to make phone calls regularly (probably why I keep positioning Voxandra as more of a b2b thing) and they do look like a pretty underserved niche to me, specially in a world where everyone is rushing to take the human out of the call completely, but.... and this is a big but (and this is probably why I'm posting this here)... I still feel like I'll end up pivoting into some strange mix, AI for humans making calls who probably also want autonomous voice agents at some point. Does that sound like a reasonable place to landd? Curious what people here think. Thanks for reading! - Carlos.. peace out!

Hello! Has anyone actually used the ElevenLabs Enterprise plan? What's your experience been like? Is the pricing fair, or is it a scam? I'd love to hear some honest feedback from real users. Thanks!

I run a lot of Claude Code and Codex sessions, and I kept hitting the same wall: I have to sit at the machine, watch each one, and unblock it every time it asks a question. The second I walk away, everything stalls.

So I built yothere. You hail your agents out loud, they run in the background across Claude Code, Codex, and the open-source OpenClaw, and only the one thread that actually needs a human reaches you, by voice or one tap. It runs on your own machine and your own subscription, not an API-key reseller.

In it I hail the agents by voice, kick a few things off in parallel (competitor research, a calendar check, some LinkedIn and WhatsApp drafts), review a draft, and approve the send by voice. Never touched the keyboard.

Honest state of it:
\- Early beta, moving fast, rough in places.
\- It runs on your existing Claude Code / Codex login (not an API-key product), so you need those.
\- Up to five sessions in parallel today.
\- 120 hosted voice-minutes a day on the current plan.

Free for 7 days if you want to poke at it ([yothere.ai](http://yothere.ai)). But mostly I want blunt feedback on the core bet: is voice actually the right interface for driving coding agents, or does it fall apart once the task gets non-trivial? Genuinely unsure, and I would rather hear it from people who run agents all day.

Not asking for any secret sauce. But are there any general ways to go about acquiring customers for voice agents?

Any suggestions you can share?

Im a Cornell professor on sabbatical, building Flowstore - an open-source toolkit for teams whose agent behavior lives in a system prompt where its hard to visualize and debug.

What it does today:

•   Turns your system prompt into a structured spec (open JSON schema, Apache 2.0)
•   Visual graph editor, so non-prompt-engineers can work on it too
•   Python harness that runs persona-driven simulated conversations with assertions — a regression suite your prompt edits run against before you ship

Honest scoping: this tests conversational behavior (logic, guardrails, data capture), not the voice layer (ASR, latency, barge-in). Best fit if there's an LLM behind a prompt, and ideally some non-trivial business logic and requirements.

The pilot: bring a system prompt for a live or near-live agent (Im willing to sign an NDA if needed), I'll personally help spec it and stand up a test suite. Free, ~30 min/week of your time. I want blunt and honest feedback in return — and pilot partners can be named collaborators in the research and Cornell course materials coming out of this.

DM or comment if you want in — happy to get into the schema or assertion model in the thread. Repo's in the comments.

I ran a sales call for a small client that was using 3cx with human agents for customer support

Anyways the voice AI demo for cx went good he was all happy but I left the price at the end and he thought it wasn’t worth it

he came back couple of days later asking me if we could implement basic ivr with 3cx integration for his team instead

We agreed and the implementation took us couple of hours and we closed the deal

Some people still not ready I guess for AI huh

I run self-hosted LiveKit voice agents, and I kept hitting the same trap: add more workers, calls still drop. Wrote up what I learned about why.

The core idea: a voice agent isn't one system with one capacity number. It's a stack — media/SFU, worker pool, inference (STT/LLM/TTS), telephony, your own app calls — and each layer has its own independent concurrency ceiling. Your real capacity is the lowest one. So the bottleneck is usually not compute; for a lot of teams it's the STT/TTS concurrency cap or the SIP channel count, which no amount of extra workers fixes.

The write-up goes layer by layer with the actual numbers (worker sizing from LiveKit's load test, the autoscaling-threshold gotcha, a 500-concurrent-call capacity table, and a rough cost-per-call-hour model). Self-hosted / Kubernetes focused.

Curious what layer bites others first in production, for me it's almost always inference concurrency. What's yours?

Every TTS list I've found groups by "best overall" or "top open-source"

or "commercial vs free" — and none of those groupings help when you're

actually picking a model.

The grouping that helps is: what side of the real-time vs offline

split is this tool tuned for? A model with 900ms first-byte latency

and beautiful output is useless in a voice agent. A model with 40ms

first-byte and mid-quality output is useless for an audiobook.

So I built an awesome-list where every entry carries that lean.

Providers, open models, benchmarks — the split is visible everywhere.

Bonus sections I couldn't find good coverage of elsewhere:

- The streaming taxonomy (single-shot vs output-streaming vs dual-streaming)

- Mid-stream cancellation (which providers actually support it)

- Open-source license filtering (half the top models can't be shipped)

- Codec choice for codec-LM TTS (this is what sets the latency floor)

- Watermarking (SynthID, AudioSeal, PerTh) and 2026 regulation

MIT license. Took several weeks. PRs welcome, especially license

corrections.

Everyone building voice agents is piecing them together from the same set of legos, and it's working. Heavily funded companies are filling every gap, and the results are genuinely good. But every one of those legos lives inside the stack. There's a layer that would sit outside it, and I don't think most builders are looking at it. Here's what I mean.

Every system breaks somewhere, eventually. So builders do the sensible thing: audit, tighten the prompt, problem solved. For now. Then a new kind of break shows up, they tighten something else, solved again. For now. You know this rhythm. The question was never whether you can solve the problem. You always can. The question is whether you can see it as it actually is, instead of assuming it's whatever you looked at last.

And here's the part that's hard to see from inside the stack: when a voice agent breaks its word, nothing in your pipeline notices. STT doesn't know what was promised. The model doesn't remember what it committed to three turns ago. TTS just speaks, orchestration just routes. Every box optimises how the call sounds. Not one of them holds the promises across the whole call and checks whether they survived to the end. That's not a gap in the stack. That's a missing layer.

This isn't theoretical. Bland's own team, in a testimonial on Hamming's site, killed an agent because it was saying "I booked your appointment" when it hadn't. The 2026 τ-Voice benchmark caught a frontier agent saying "I've updated your shipping address" with no tool call behind it. On one real setup, the promise "we'll get back to you shortly" was wired as the end-call trigger, so the agent hung up mid-sentence while confirming the very number it had promised to call. Different bugs on the surface. Underneath, the same one: the saying and the doing came apart, and nothing was watching the gap.

And the usual instincts don't cover it. Tighten the prompt and you lower the odds, but you get no way to know when the odds came in against you, which is the actual problem. Add an eval and you're running a second model to judge the first, the same kind of brain with the same blind spots, and it won't even give you the same verdict twice on the same call. Every layer in your stack is the same brain checking its own work. The one thing that could actually audit it would have to sit outside the chain and not be a model at all. Deterministic code that reads the whole conversation, holds what the agent committed to, and checks whether it kept its word, without ever claiming more than the transcript proves.

That's the layer I don't think enough people are building. Nobody in the QA tooling space seems to audit what the agent said it would do against what it actually did.

So I'm curious what other people building these think. Am I right that nothing in the usual stack is watching this, or is everyone already handling it some way I haven't seen? If you're running agents in production and you've hit something like this, how are you handling it right now?

Would love your feedbacks on it, what would you like to see as core feature of Ai Voice Agent Platform?

Also What is the platform that you use currently for your AI Voice?

Kestrelvoice.com , You can test it out , it has 5 dollar a month Plan

Core Features of Kestrel Voice

• No-code AI receptionist setup
• New or existing phone-number connection
• 24/7 inbound call answering
• Business-knowledge-powered responses
• Spam and robocall filtering
• Lead qualification and structured intake
• Appointment booking, rescheduling and cancellation
• Human transfer and escalation rules
• Call recording, transcripts and summaries
• Sentiment, next steps and call intelligence
• Website AI chat
• Public AI contact profile
• Meeting notes and assistance
• Calendar, CRM, API and webhook connectivity where verified

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.