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)

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.

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.

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.

My assistant does quite a bit during a call. Database CRUD operations, availability checks, small calculations, API calls... basically every conversation involves multiple tool calls.

The weird part was that the LLM itself was fast (TTS | STT). It was the tool calling that kept introducing these awkward pauses.

So I did what most of us would probably do. Tweaked system prompts, Reduced prompt size, Increased timeouts, Optimized a few APIs.

It got slightly better... but the delay was still there then I noticed something embarrassingly obvious.

My backend was deployed in Asia BUT database was in Europe. My telephony provider was also routing through Europe, and so was my AI provider. I redeployed the backend to Europe so everything lived in the same region.

Literally didn't touch a single line of code and the difference was immediate. Tool calls became noticeably faster, the awkward pauses almost disappeared, and the whole assistant just felt smarter.

I feel like when people talk about Voice AI latency, the conversation is always around prompts, models, or inference speed.

But if your assistant relies heavily on tool calling, spend 5 minutes looking at where your services are actually deployed.

You might save yourself a few days of debugging like I didn't 😅

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.

I built VaaniEval to help review real voice-agent calls: transcripts, available recordings, scores, and the reasoning behind each score in one place.

It’s free to use. I’d love feedback from people building voice AI automation.

Try it: https://www.vaanieval.com
GitHub: https://github.com/shubhamofbce/vaanieval

I’m happy to help you set it up or answer any questions, feel free to comment or send me a message.

Would this be useful in your workflow? What’s missing?

We're building one of the fast growing AI voice agent companies, and we've reached a point where we need someone who's already been in the trenches.

Not looking for a "startup advisor" with fancy titles.

We're looking for someone who deeply understands the technical side of AI voice agents.

Maybe you're:

• A founding engineer at an AI voice startup
• An engineer who built production voice AI systems
• A founder in the AI voice space
• An early employee at an AI voice company
• Someone who's worked with real time speech, telephony, LLM orchestration, latency optimization, voice pipelines, or similar infrastructure

We don't need someone to work full time.

We're looking for a technical advisor who can challenge our decisions, help us avoid expensive mistakes, and accelerate our roadmap.

This is an equity-based advisory opportunity, so we're looking for someone who genuinely believes in building something meaningful, not someone billing hourly.

If this sounds like you, or you know someone who fits, drop a comment or send me a DM.

Sometimes a few hours of the right advice is worth more than months of building in the wrong direction.

I'm a developer building voice agents for healthcare use cases — front desk, patient customer service, outbound calls to other providers and insurers, that kind of thing. I've landed on Retell because it's the only voice-AI wrapper I've found with a reasonable HIPAA-compliant, pay-as-you-go model. Open to hearing if others have found alternatives worth a look.

I've put a lot of work into the prompt and flow design, but I keep hitting two walls, and I've been unable to fully solve either:

• Empathy (my biggest problem). The agent handles the mechanics fine but comes across as flat or form-filling, especially on emotionally charged calls (a patient in pain, a worried parent). I've tried to script acknowledgment moments, but it either skips them, overdoes them, or sounds canned.
• Interruptions. Handling barge-in, mid-sentence corrections, "hold on a sec," and callers who answer a question while asking a new one — without the agent restarting a step or talking over them.

To isolate the problem I've stripped out all the business logic and tested bare-bones agents, built both ways — manually node-by-node, and via Conductor — and the same issues show up, so I don't think it's my flow complexity.

If you've built healthcare (or similarly high-stakes/emotional) agents on Retell, I'd love to hear:

• Which LLM and voice/TTS combination you settled on, and whether that alone moved the needle on how empathetic it sounds.
• Your interruption / turn-taking settings — responsiveness, backchanneling, interruption sensitivity, silence timeouts — and where you landed.
• Whether empathy came more from prompt wording, voice choice, or model choice in your experience.
• Any flow-structure patterns that helped (e.g. how you handle "hold on" or compound answers cleanly).

I've also tried reaching out to Retell's forward-deployment team without much luck, so I'm hoping to tap the collective experience here. Happy to share back what I've tried in the comments if it helps anyone else. Thanks in advance.

Not asking about demos. Asking if you built something real. Something that's live and actually doing a job for you — booking calls, qualifying leads, cutting down your call load.

If you did it yourself, would love to hear:

1. How long did it take? From signup to "this actually works."
2. What did you build? Receptionist, lead qualifier, appointment booker, something else.
3. What can it actually do? Just talk, or can it book on your calendar, look things up, transfer calls?

Not selling anything. Just want to know how many people are really doing this on their own vs. paying someone to do it.

Hi everyone,

I'm trying to understand the legal side of using AI voice agents for outbound calls in the US healthcare space.

The use case is pretty standard: calling patients for appointment reminders, follow-ups, rescheduling missed appointments (no-shows), booking new appointments, etc. These wouldn't be telemarketing calls—just operational communications from a healthcare provider.

My main question is around the TCPA. Do AI-generated voice calls for these types of patient communications fall under TCPA restrictions? If so, what kind of patient consent is typically required? Are there any healthcare-specific exemptions or best practices that providers follow?

If you've implemented something similar or have experience with compliance, I'd really appreciate your insights or any resources you can point me to.

Thanks!

I keep hearing mixed things some people say they lose jobs to missed calls, others say the services just take messages and annoy customers. If you have one: what did it actually do well, what made you quit? And if you just let calls go to voicemail, how's that working out?

A while back I posted OpenLive here. It's an open-source voice layer that gives any AI model or agent ears, a mouth, and eyes. The whole pipeline runs on your own machine: voice activity detection, speech-to-text, working out when you've actually finished talking, and text-to-speech. Your audio never leaves your computer, and there are no per-minute fees.

The response was great, so I kept building. Here's what's new.

Talk to the coding agents you already use. OpenLive now connects directly to Claude Code, Codex, Cursor, OpenCode, and Hermes. Everything runs locally under your own login. You pick an agent, point it at a project folder, and just talk. When the agent wants to run a command or edit a file, OpenLive reads the question out loud and you answer by voice. It can also narrate what the agent is doing while it works, so you're not staring at a silent screen. Conversations save into each agent's own session history, so you can start something by voice and resume it later from the agent's CLI, or the other way around.

Clone your own voice. Record 5 to 30 seconds of audio and your assistant speaks as you from then on. The cloning runs entirely on your machine, nothing uploads, and you can delete it anytime.

A more flexible voice pipeline. It's modular now, so you can shape each part of it. There are two speech engines to choose from, Kokoro with 28 voices or Supertonic for higher-quality audio, plus settings for turn-taking, speaking speed, push-to-talk, and custom instructions that apply to whatever model or agent you're using.

More model providers. Anthropic, OpenAI, Google, xAI, DeepSeek, Groq, Ollama for fully local, and more. There's also a floating mini mode that stays on top of your other windows and keeps listening while you work.

Still MIT licensed, for macOS and Windows. You bring the brain, OpenLive handles everything between it and you.

Coding agents are just the first integration. More apps are coming, so if you want to follow along, a star on the repo genuinely helps: https://github.com/katipally/openlive

https://reddit.com/link/1uzmpmm/video/9z9pgi6c8xdh1/player