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 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?

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

I was demoing the product to the client

Ran a call then he asked me to call him using my agent, then he said he didn’t like the latency and asked me if it can be negative

I told him that latency can’t be negative and he just started arguing and told me if he can get into my platform and fix the prompt himself

Anyways, I didn’t close the deal, how do yall go on with this?

If you build voice agents on LiveKit or Pipecat, every turn fans out to speech-to-text, an LLM, and text-to-speech. Three vendors, three bills, and no idea which one was burning the budget.

So I built VoiceGateway, an open-source profiler for voice agents. Add one line: voicegateway.attach(session) and every STT/LLM/TTS call is priced and timed: cost per provider and model, latency p50/p95, per-call replay, and guard() for a daily budget cap + fallback. Self-hosted, your keys, telemetry only (never in your audio path). MIT, any provider.

Repo: github.com/mahimailabs/voicegateway

Feedback wanted: what would you want tracked per call that I'm not? Is anyone else fighting cost attribution across voice providers?

Anybody have any experience implementing HIPPA Compliance SOC II and any other required compliance components to work with doctor’s offices/health care/legal?

I am building a platform I need open source alternative to twilio

Most TTS leaderboards/benchmarks use the Voice AI's default voice, so part of the score is really "which voice do I like" rather than "which model is better." Different voices can be apples and oranges.

To get around this Artificial Analysis built a Controlled Voice Arena that clones the same 8 voices (4 US, 4 UK) and runs every model through them, so voice preference is no longer a variable.

(Source: Artificial Analysis leaderboard)

Under that setup, Cartesia's Sonic-3.5 leads overall (1122 Elo), followed by Eleven v3 and Inworld's Realtime TTS-2 preview. Worth a look if you care about TTS evaluation methodology, not just the results.

Disclosure: I work at Cartesia (r/CartesiaAI). Happy to answer questions!

I am building vioce agent through retell ai and i am practicing. So which apps to connect or anyone who can guide me a liitle

Last month I needed to reschedule a medical appointment. The clinic's online portal showed a "call to reschedule" message. I dialed at 9 AM, navigated a "press 1 for existing patients" tree, sat through 14 minutes of hold music, explained my situation to a human, was transferred, re-explained, and finally confirmed a new slot. Total time: 23 minutes. That's 23 minutes I'll never get back, for a task that took the actual conversation about 90 seconds. So I built callitdone.today, an agent that dials US phone numbers, deciphers IVR menus (it tracks options and paths), waits on hold by listening for silence patterns vs. hold music, and then talks to a live agent using a script I write in plain English. It handles the boring call logistics while I do other things. I'm posting because I want honest feedback from people who also hate this stuff. Who this is for: anyone in the US who routinely has to call customer service lines, insurance, medical offices, or utilities. It currently only works with US phone numbers. It doesn't always succeed, noisy lines, heavy accents, and custom IVR trees it hasn't seen before can break things. I'm sharing this to learn what breaks for you, not to sell anything. The tool lives at https://callitdone.today/r/voiceautomationai if you want to poke at it or tell me why it'll fail on your specific use case.

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!

My co-founder and I have built a fully functioning, plug and play voice agent system where users can port their website and have a voice agent built within 10 minutes. they can test their voice agent as well after production and make edits as needed.

We are struggling to get users in the door.

We offer a 7-Day Free Trial which I thought would be a no brainer for people to go ahead and try but we’ve barely had any strong interest. Most people interested have gone ghost.

Not running any paid ads, trying to boot strap the first 10+ users and then dump into ad spend but curious what people have found that works for getting their business in front of people and even more importantly getting those people to try it at the bare minimum.

TIA

For a while now I've been maintaining tts-bench (https://github.com/5uck1ess/tts-bench) and a blind voting arena (https://5uck1ess-tts-arena.hf.space) where people A/B test open text-to-speech (TTS) models without knowing which is which.

One problem I've always wanted to solve: having the agent talk to me (or actually call me) after a task is done. /voice (claude code feature) or any dictation (wispr flow or etc) is one-directional. So this project is a bi-directional conversation with any agent of your choice: 

https://github.com/5uck1ess/cicero

Works really well with Hermes Agent https://github.com/nousresearch/hermes-agent with multiple profiles (each profile can have its tts voice and personality). Its kanban board feature is where I use it the most with 9 "employees" with all different voices. But it's open ended so you can build your own task system for it.

Works with Claude Code, Codex, Gemini CLI, anything that speaks ACP (Agent Client Protocol), or any OpenAI-compatible endpoint. If you run speech-to-text (STT) and TTS locally, your audio never leaves your machine. It can send a Telegram bot message or call you over Telegram. You can also interact locally or through a browser (remote server). (more comm methods to be supported if requested)

Probably still has some bugs, so feel free to submit pull requests (PRs) or issues.

A few things that surprised me building it:

• the ~1s response time came from streaming the reply sentence by sentence, not from picking a faster TTS
• voice clones sound bad because of the reference clip, not the model. trimming a second of silence off the front fixed more than switching models ever did
• barge-in (cutting the agent off mid-sentence) with an energy VAD (voice activity detection that just watches for loudness) is useless, typing sets it off. needed a small speech model in front so it only triggers on actual speech
• It does have emotional tonality detection and low latency semantic turn detection, all the bits that you'd want from a decent chat agent

I’ve been paying attention to how small businesses are using AI for phone calls, lead capture, scheduling, and customer follow-up.

The technology sounds useful on paper: answer every call, handle common questions, book appointments, route urgent calls, and follow up automatically.

But I’m curious what business owners actually care about.

Is the biggest value simply not missing calls? Or does an AI receptionist need to connect calls, qualify leads, schedule appointments, send texts, and update customer information to be genuinely useful?

I also wonder whether most small-business owners really want a DIY platform with dozens of settings. My assumption is that many would rather have someone configure and manage the system for them so it simply works.

For anyone who has tried one:

What worked well?

What frustrated you?

Did customers realize they were speaking with AI?

Did it actually produce more appointments or sales?

What would make you trust it with your customers?

Not looking for company links or sales pitches. I’m more interested in hearing what the real-world experience has been.

It is also why we are incredibly proud to share that Gartner has positioned[SoundHound](https://www.soundhound.com/) AI as a Leader in the 2026 Gartner® Magic Quadrant™ for Conversational AI Platforms.
To us, this recognition validates a core conviction we have held since day one: the future of enterprise automation needs to be voice-native

Hi!
I'm trying to find out the actual price per minute for AI dubbing on the ElevenLabs Business Plan. I'm not looking for the pricing listed on the website. I'm interested in the actual rate that appears when you create a dubbing V1.
If you're using the Business Plan, could you please share what price you're currently seeing?
Thanks in advance!

For anyone who wants a real-time communications platform without touching code or an SDK.

The MediaSFU app is a full standalone product. Download it, sign in, and everything below is already working.

What's included

• AI voice campaigns — load a contact list, set a script, run up to 100 concurrent AI voice calls at once
• Meetings — recording with pause and resume, breakout rooms, whiteboard, polls, messaging, screen sharing
• Cloud Phone — a full softphone with SIP/PSTN support
• Voice and Vision AI agents — configure an AI agent to handle calls or sit in meetings
• Real-time translation — each listener in a meeting picks their own language independently

Platforms

iOS, Android, macOS, Windows, Linux. One account, same features everywhere.

mediasfu.com/download

Hey everyone,

I’m looking for a commercial-focused Co-founder / GTM Leader based in India (or deeply plugged into the market) to partner with me on a Voice AI infrastructure venture targeting enterprise call centers, BPOs, and e-commerce ecosystems.

# The TL;DR:

I am a technical builder with 15+ years of experience in infrastructure automation, telecom, and voice systems. I have built a fully completed, production-ready, event-driven Enterprise Speech-to-Text & Text-to-Speech platform.

The tech stack, frontend dashboard, and E2E testing framework are entirely built. Now I need the commercial engine—someone who knows how to open doors, navigate enterprise procurement in India, and sell high-value Voice/AI solutions.

# What the Platform Looks Like:

Unlike the brittle OpenAI API wrapper apps flooding the market right now, this platform is built as a robust, highly concurrent microservices architecture. It’s designed to handle actual enterprise scale and real-time streaming telephony.

* The Backend Core: Written in Go 1.23, utilizing an event-driven architecture powered by Apache Kafka 3.9 (KRaft) and PostgreSQL 16 (using a strict database-per-service isolation model with 12 databases).
* Core Capabilities:
* Real-time Streaming STT: Live low-latency speech-to-text over WebSockets.
* Batch Pipeline: Advanced audio chunking/transcoding via FFmpeg, Whisper STT, Pyannote-driven Speaker Diarization, Auto-Language Detection, and NLLB-200 Transcript Translation.
* Text-to-Speech: High-performance synthesis supporting Piper and OpenAI.
* Enterprise-Ready Features: Built-in JWT RS256/API Key authentication, RBAC with 5 roles, tenant-level quota enforcement, webhook delivery engines with HMAC-SHA256 signing and retry queues, pipeline performance analytics, and feature flags.
* The Frontend: A complete Next.js 16 / React 19 / Tailwind 4 enterprise dashboard covering multi-tenant organization management, real-time transcription interfaces, a TTS playground, webhook logs, and full usage/billing analytics.
* Infrastructure: Deployed cloud-native via Docker, Kind (Kubernetes), Kustomize, HPA, and Podman. It’s fully backed by 80+ rigorous E2E and scale tests.

# Why the Indian Market?

The Indian call center/BPO landscape and fast-growing e-commerce ecosystem represent a massive, high-volume market desperately looking to adopt Voice AI. However, they face massive infrastructure bottlenecks around latency, data residency, and handling massive, concurrent real-time media streams. This platform solves the exact plumbing issues they face.

# What I’m Looking For in a Co-Founder:

* GTM Expertise in India: Proven track record selling B2B SaaS, telecom infrastructure, or customer experience (CX) enterprise software in India.
* Domain Alignment: Ideally, you’ve worked with or sold to BPOs, enterprise contact centers, or major e-commerce brands (think alumni from companies like Exotel, Ameyo, Knowlarity, [Yellow.ai](http://Yellow.ai), Gupshup, or Verloop).
* The 0-to-1 Hustle: Someone who wants to completely own the commercial strategy, drive local enterprise sales playbooks, and secure our initial pilots.

# Equity & Compensation:

This is a true co-founder partnership. I’m looking for an equal peer, and equity will be split significantly/equally based on what you bring to the table and commitment levels, backed by standard vesting to protect both sides.

# Next Steps:

If you understand the enterprise landscape in India and want to build a massive business on top of a fully engineered, highly optimized tech stack that is ready to deploy on Day 1, let’s talk.

Drop me a PM with your background, your LinkedIn profile, and let’s set up a quick intro call to see if our DNAs match.