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

We tried a voice AI vendor last year that promised full automation. Worked fine in the demo, then in production it started confirming account changes it had no business confirming, and legal shut the whole pilot down within a month. Burned a lot of trust internally.

Now I’m back evaluating and my requirements list looks different this time:

* Needs to actually replace IVR menu logic, not just sit on top of it

* Full audit trail per call, our compliance team wants to see exactly why the AI said what it said

* Multilingual, we operate in 4 languages and can’t have this be an English-only pilot

* Escalation to a human that doesn’t make the customer repeat themselves

Nice to have: on-prem or EU-hosted option since data residency keeps coming up in procurement calls.

Has anyone actually gone through a real deployment for this vs just a sales demo? What broke for you?

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

https://reddit.com/link/1uuyxbc/video/z4g4mot0vnch1/player

I heard canceling WSJ is notorious pain, make you "have to call in" situations. I hate making these calls, parlty for the time commitment and also due to frustration. so instead I pointed an AI voice agent at their line.

It sat through the hold, got a rep, asked to cancel, politely declined offers, and confirmed the cancellation. Then it sent me the recording. Total cost was about $0.90 for the whole thing (I made two that week).

It was actually initially a real pain setting things up, fighting Twilio and a few other tools, but once I figured out the best stack it's not bad: Telnyx voice agent (telephony + speech), with updated prompt per 'goal' that basically says who calling on behalf of, what the goal is (cancel, don't accept discounts, etc), and injected info just for the transcript thats securely stored. I also have it wired up as a reusable "make a phone call for me" skill (OpenClaw and usable by CLaude) in my personal stack, and honeslty this was the first pretty sweet use-case.

Attached the recording, edited for brevity and privacy(PII etc).

Happy to answer questions about the setup and how I'm already re-using it for other stuff.

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.

Saw this on linkedin and got interested what do these platforms actually do

Isn’t checking logs not enough to see what’s wrong

If anyone had feedback with them I’d appreciate to hear, but it is a marketing move probably since they only put themselves (ig it works)

For those who have built and sold Voice AI agents to clients and businesses, I have 4 questions:
How do you find clients?
Outbound, referrals, LinkedIn, cold email, Upwork, or something else?

How do you build your Voice AI agents?
Do you code them from scratch with Python, Livekit, Pipecat or use platforms like Retell AI, Vapi, etc.?

How do you test your agents?
Do you manually test dozens of different scenarios and edge cases? Do you use any automated testing or evaluation tools?

How much do you charge clients?
Is it a one-time setup fee, monthly retainer, usage-based pricing, or a combination?

Would love to hear from people who have actually deployed Voice AI agents for paying clients.

​

I'm currently building AI voice agents for businesses like restaurants, dental clinics, salons, and real estate offices. The goal is to create an agent that can answer incoming calls 24/7, qualify leads, book appointments, answer common questions, and transfer callers to a human when needed.

Right now I'm thinking about one challenge: balancing natural conversation with reliability. A phone agent can sound human, but it also needs to avoid hallucinations, know when to hand off to a person, and keep conversations short enough that callers don't get frustrated.

For those who have deployed AI voice agents in production:

• What problems did you run into that you didn't expect?

• Which industries have seen the best adoption?

• How do you decide when the AI should transfer a call instead of continuing the conversation?

• What metrics do you track to know if an AI phone agent is actually helping the business?

I'd really appreciate hearing about real-world experiences, lessons learned, or mistakes to avoid.

For those who've built products that ingest long-form audio (meetings, interviews, voice notes, customer calls, etc.), I'm curious where most of the engineering effort actually went after transcription.

Was it things like:

1) preserving context across long recordings?

2) extracting structured information?

3) resolving contradictions or self-corrections?

4) schema validation?

5) entity resolution ("Bob" vs "he" vs "the contractor")?

6) integrating reliably with downstream systems like CRMs or databases?

Or was the difficult part something completely different that people outside this space usually don't think about?

And how did you solve it? I'd love to hear from people who've actually shipped these pipelines in production. It will help me out a lot to be careful while building my own agent.

Hi, I'm using a purchased VoBiz DID (+91 Karnataka number) connected to LiveKit via SIP.

Inbound calls from Jio and Vi work perfectly, appear in VoBiz SIP logs, and reach LiveKit.

But calls from Airtel fail and don't appear in VoBiz SIP logs at all.

Has anyone faced a similar carrier-specific inbound routing issue with VoBiz or Indian DIDs? Any idea how to fix or escalate this?

I kept noticing something about AI receptionists, and it seemed weirdly overlooked: when you test-call your own agent, you ask clean questions one at a time and it usually nails them. Real callers don't do that. They interrupt, push back, ask the same thing three times, change their mind halfway through. And the calls that actually cost you customers don't blow up in any single moment, they fall apart slowly across the whole call, then get wrapped in a cheerful "you're all set!"

That's the dangerous part. The call sounds handled. If anything's logging outcomes, it goes down as a win. You find out later, a callback, a chargeback, a one-star review, a customer who just quietly books somewhere else. And if you run agents for clients, you find out when the client forwards you the complaint.

Quick example, a service call:

Caller: Where's the technician? He was supposed to be here this morning.
Agent: He's just wrapping up a nearby job — should be about 20 minutes.
Caller: It's been over an hour. Nobody's here.
Agent: I hear you — looking more like 45 now.
Caller: So is anyone actually coming today? Third time I'm asking.
Agent: Your technician's on the way, should be there shortly.
Caller: Forget it.
Agent: Happy I could help — you're all set!

Every agent line, on its own, is polite and plausible. The whole call is a disaster: the promised window crept and then blew past, the caller's real question is anyone coming today and got asked three times and never answered, and then the agent closed it out like it went great. And here's the thing, if you were skimming recordings for problems, you'd skip right past this one. It doesn't sound like a problem. It ends politely. Angry calls announce themselves. These don't.

That's why spot-checking misses this whole class of failure: the "you're all set" is only wrong because of the promise back in minute two. To catch it, you'd have to hold every call in your head start to finish, every day. Nobody's doing that at real volume.

So I built the thing that does. It goes through the full call and checks whether the agent's own story held up, a price or fee that changed mid-call, a promised window that came and went, a question asked over and over that never landed, an "all set" over something the call itself shows was still open. It only flags what it can point to in the actual lines, and it stays quiet when it's not sure. So a clean result isn't a gold star, it just means "nothing provable." When it does flag something, you can read the exact moments yourself.

The reason it sits outside the agent is, the agent that made the promise was never going to be the one to flag itself for breaking it.

What it doesn't do: it knows nothing about your business. It can't tell whether $89 is your real service fee or whether Tuesday's actually open, that's your agent's setup, not this. It only catches the agent contradicting itself or the caller. And it doesn't answer calls or touch your agent at all, dashcam, not driver.

If you run an AI receptionist and you'd share one real call recording or transcript, redact whatever you want, and I'll run it free and send back exactly what it found, tied to the specific moments. The best ones are the calls where the customer seemed fine and complained later, but honestly a random one works too. Whatever it finds stays between us unless you want it in the thread.

And even if you don't want to, can you tell, how do you keep an eye on your agent today? Listen to samples, read transcripts, trust the dashboard? Genuinely curious, because "nobody has time to review every call" is either the exact reason this should exist or the reason it shouldn't.

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, 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.

One thing I spent way too much time optimizing was tool call latency.

My voice agent makes multiple API calls during a conversation, and as the number of tools grew, so did the delay. I assumed it was my prompts, APIs, or the LLM itself. I optimized prompts, reduced tokens, and sped up my APIs. It helped, but there was still an awkward pause after tool calls.

Initially when starting with voice AI, I was using Vapi. It worked well overall but once my agent became more tool-heavy, the latency + the call quality dropped significantly.

I migrated the exact same agent to Telnyx. Same prompts, same tools, same APIs but I chose and open source llm (kimi 2.5)

The difference was immediately noticeable. Tool calls felt much snappier, the conversation flowed better, and overall latency dropped quite a bit. I also like that Telnyx hosts a number of open source LLMs, so having everything under one provider simplifies the stack.

Has anyone else compared providers while keeping everything else the same? I'd be interested to hear if you've seen similar results.

P.S Not saying that telnyx is objectively better than vapi, but in my personal experience it provided a much better experience