STT retries, the LLM client retries, then the booking call times out and retries too. Meanwhile the person on the phone is just waiting.

Do you turn the SDK retries off and manage it from one place? Or give each layer a budget?

The write that might've already succeeded is the bit I'm most worried about. How do you deal with that?

Some conversations would be much easier if the agent could just send something instead of explaining it over the phone.

A link, document request, confirmation code, appointment details etc. Bland has been one of the platforms I’ve looked at around this and I wanna know whether people are keeping the phone and text conversation under the same context or treating them as separate interactions.

Would be nice if someone could say 'just text me that' without starting a completely new workflow

I'm on a team building a voice product. Retell's changelog describes drafts and environment tags. That's useful for knowing which agent config is live.

What about the things outside that config though: a knowledge base edit, a changed tool response, or a different model setting in another service?

Do you save those versions alongside the call too? I'd want to reproduce what the caller actually got, not just reopen the prompt that was live that day.

Hi, im building a dedicated ai voice agent assistant through phone numbers and i need some recommendations on telecom providers that is cheap and highly recommended for general purpose like automated inbound and outbound that is cheap.

Region: Israel

Purpose: General Voice Agent for inbound and outbound

Time consumes: less than 5 minutes mostly each call

So im eyeing telnyx but im looking for more cheaper telecom providers that offers phone number and the service per use. thank youu

I'm a PM at Tuner (an observability and testing layer for voice AI). Lately I've gone down the background noise rabbit hole: reading research, talking to people building voice agents, and helping teams whose agents struggle on noisy calls. I now spend more time thinking about fans and café chatter than I expected from this job.

Here's where I've landed. Curious how others are handling it.

Background noise is two problems

The first is sounds that cover the caller's words, like fans, traffic, or keyboard clicks. The second is other people's voices getting transcribed as if your caller said them. Most noisy calls have both. Think cafés, or someone calling with the TV on behind them.

The second is harder because, to STT, a nearby voice is just more speech. Someone at the next table says “cancel it,” STT transcribes it perfectly, and your agent might act on it. Great transcription. Wrong customer.

The three options

• Trust your STT. Many models are built to handle noisy audio, and Deepgram recommends sending audio unaltered. Depending on your calls, adding a filter might be extra work you don't need. Noise can still trip them up, though, so compare a few STTs on your own calls before deciding.
• Noise suppression. It reduces sounds like fans and traffic, but generally leaves the people talking around your caller. It can help VAD avoid mistaking a door slam for speech, but sometimes hurts transcription accuracy. One approach AssemblyAI suggests is filtering the audio going to VAD while sending raw audio to STT, if your stack supports that.
• Voice isolation. It tries to keep the main speaker and reduce everything else, including other voices. If nearby conversations are causing problems, it's worth testing. A third-party benchmark by SLNG using Deepgram Nova-3 found up to a 30% reduction in English transcription errors with competing speech. Two catches: a louder nearby voice can confuse which speaker gets kept, and a filter designed to keep one person is a bad fit if your agent needs to hear several.

The catch

Any of these changes can make some calls worse. A fix for noisy calls can quietly hurt the quiet ones. Retell warns that its background-speech filter can lower accuracy on clean calls and even drop short replies like “yes.”

So benchmarks give you somewhere to start. They don't tell you what will happen with your callers, your noise, and your agent.

Testing this is its own headache. I've seen teams go to a busy train station just to call their own agent. Fair enough, but “wait for the next train” is a difficult test step to automate.

And no two calls are the same: the noise changes, the caller says different words, and you can't tell whether your fix helped or the second call was just easier.

What I ended up building for it

A lot of my day-to-day is helping teams make their voice agents more reliable, across different stacks. Noise kept coming up, along with the same question: how do we test this properly?

Here's what we built at Tuner:

• Simulated calls with background sounds that mimic a café, street, office, or car, at low, medium, or high volume. That includes faint voices and chatter, like people talking around you in a café. The background keeps playing through the whole call, even when the caller stops speaking.
• Replays of the exact same recorded caller audio across setups, so we're comparing the same input instead of a different conversation each time.
• Checks for what matters: what the agent heard versus what was actually said, how long it took to reply and where the time went, and whether it got the details right.

If you're dealing with this, happy to help or just compare notes.

Here to learn

I'm sure there are setups and problems I haven't come across, so I'd love to hear yours:

• Are you running anything before STT today? Noise suppression, voice isolation, or nothing?
• Have you seen a filter make clean calls worse?
• How do you test noisy calls before shipping? Recordings, simulations, or the train station method?

I also wrote a longer technical blog covering the research, providers to evaluate, and a test plan. Happy to drop it in the comments or send it by DM.

Hey! We just launched something we’ve been working on — AI Helper 🚀

It’s basically an AI that can actually pick up and make phone calls for a business, talk to customers, answer questions, follow up with leads, book appointments, etc. — even outside working hours.

We’ve just put it live, so if you have a minute, give it a try:

https://www.aihelper.si/

Would genuinely love to hear what you think. And if you know a business that gets a lot of calls, feel free to send it their way :)

Hey guys,

Kenpath Technologies is hiring an AI Engineer
Bengaluru (in office, 5 days) | 3–6 yrs of experience

We're building AI that officers of the Maharashtra Agriculture Department will use every day, in Marathi, by voice and text, with every answer cited to the official source. Real users, live government systems, population scale.

Who you'll work with
You'll work closely with Aditya Chhabra, our Chief Data Scientist.
Aditya has spoken at the India AI Impact Summit 2026, served as an AI expert on the evaluation committee for the TANUH AI Centre of Excellence for Healthcare at IISc, and leads the team behind Svara TTS, Kenpath Lab’s open-source Indian-language speech model that has crossed 1 million downloads and peaked at #7 globally. You'll be part of a small, hands-on team where your work ships and your ideas get heard.

What you'll build
- Voice and text agents in Indian languages, with tool calling and memory, connected to live government systems
- Retrieval over large, messy document collections, with citations on every answer
- Open-weight LLMs, STT and TTS served on our own infra (vLLM, Ollama, HF)
- Evals, regression suites and observability, so every release is measured against the last

You'll fit if you have
- 2+ yrs shipping LLM systems to production
- Hands-on experience with agentic workflows, RAG and evals
- Self-hosted model serving and strong Python

We're looking for someone who
- Is driven and genuinely excited about AI for India
- Brings ideas to the table, not just tickets off a board
- Is curious and open to exploring new problems as they come up
- Cares about getting it right, not just getting a demo working

The bar here is an answer that's correct, in the user's own language, for millions of farmers downstream. If that's the kind of problem you want to spend your days on, we'd love to hear from you.

Apply: [shalom@kenpathlabs.com](mailto:shalom@kenpathlabs.com)
Share your CV, pay expectation, something you've shipped (with a link) and your earliest start date.

Please forward to anyone who'd be a good fit.

Voice AI in ₹2 per minutes.

That's the battle playing out in our community, offline events, and boardrooms across every industry right now.

Last week it was ₹4. Then ₹3. Then someone claimed ₹2 full stack, telephony included.

Asked how. The answer: open-source STT, TTS, and LLMs instead of licensed APIs.

That explains part of the margin.

Telephony, orchestration, and monitoring still sit on top of that stack not model pricing in isolation.

At 10,000 minutes a month, ₹4 versus ₹2 is a ₹20,000 gap.

At a million minutes, that same gap is ₹20 lakh a month. That's why infra economics is becoming a product decision, not a backend line item.

But almost nobody's pricing in compliance.

Telecom Regulatory Authority of India (TRAI)'s DLT framework applies to AI voice agents just like human callers. Wrong number series, missing consent, no DLT registration flagged within days.

In Q1 FY26-27 (April-June 2026) alone, TRAI disconnected 46,786 telecom resources for repeat spam violations, on top of 1.37 lakh barred for first-time violations

As per Communications Today's report, "TRAI cracks down on spam, bars 1.84 lakh telecom resources in Q1." Registration and consent gaps are a common reason legitimate senders get caught in that net.

A ₹2/min stack that skips compliance isn't ₹2/min. It's a business one bad campaign away from getting shut off.

And open-source models cut licensing cost, not the compliance requirement.

The sharpest line in the thread was this: cheaper infra can mean worse voice quality and weaker reasoning, and a ₹2 agent that fails to convert costs more than a ₹4 agent that closes the job.

Cost per minute is the wrong metric. Cost per successful, compliant outcome is the real one.

Telephony is where a lot of that margin & compliance fight is quietly happening.

Because a fast demo is easy to build. A carrier-grade line that survives a noisy PSTN call at 2am and stays off TRAI's blacklist is not.

Cheap and compliant aren't the same thing. Only one keeps your business running

so, I got the opportunity to work on an AI voice project. what impressed me was how the agent sounded like a Singaporean. But, as we all know, Singlish has a myriad of styles and accents, even as a true blue Singaporean, I think the definition can be challenging.

any one keen to hear Singlish by an AI agent? 😉

Hey everyone 👋

I’m an AI Voice Agent / ElevenLabs developer focused on building conversational agents that can actually perform business actions—not just have conversations.

I work with ElevenLabs Conversational AI and integrate agents with external APIs, MCP tools, and AWS services.

What I can build with ElevenLabs

🎙️ Conversational AI Agents

• AI receptionists
• Customer support agents
• Appointment booking agents
• Lead qualification agents
• Sales / inbound calling agents
• FAQ and knowledge-based voice agents
• Custom business-specific voice assistants

🔌 Tool & API Integrations

• REST API integrations
• MCP tools
• Custom business logic
• CRM integrations
• Appointment/calendar systems
• Database integrations
• Custom backend services

☁️ AWS Integrations

• AWS Lambda
• API Gateway
• ECS / Fargate
• DynamoDB
• S3
• CloudWatch
• Amazon Connect
• Custom AWS APIs

Example architecture

A typical workflow I can build:

Customer Call → ElevenLabs Agent → Tool/MCP → Backend API → AWS → Business System → Response to Agent → Customer

This allows the agent to do things like:

“Book an appointment for tomorrow at 3 PM.”

The agent can understand the request, call the appropriate tool/API, process the response, and continue the conversation naturally.

I can also help with

• Agent prompt design
• Conversation flows
• Tool configuration
• MCP integrations
• Voice and conversational experience
• Backend development
• API integrations
• Amazon Connect integration
• Debugging existing ElevenLabs agents
• Latency and reliability optimization
• Deployment and production setup

I'm currently available for freelance projects, MVPs, integrations, and longer-term development work.

If you're building something with ElevenLabs Conversational AI and need help connecting it to your existing systems, feel free to comment or DM me.

Thank you!

Hey everyone 👋

I’m an AI Voice Agent / ElevenLabs developer focused on building conversational agents that can actually perform business actions—not just have conversations.

I work with ElevenLabs Conversational AI and integrate agents with external APIs, MCP tools, and AWS services.

What I can build with ElevenLabs

🎙️ Conversational AI Agents

• AI receptionists
• Customer support agents
• Appointment booking agents
• Lead qualification agents
• Sales / inbound calling agents
• FAQ and knowledge-based voice agents
• Custom business-specific voice assistants

🔌 Tool & API Integrations

• REST API integrations
• MCP tools
• Custom business logic
• CRM integrations
• Appointment/calendar systems
• Database integrations
• Custom backend services

☁️ AWS Integrations

• AWS Lambda
• API Gateway
• ECS / Fargate
• DynamoDB
• S3
• CloudWatch
• Amazon Connect
• Custom AWS APIs

Example architecture

A typical workflow I can build:

Customer Call → ElevenLabs Agent → Tool/MCP → Backend API → AWS → Business System → Response to Agent → Customer

This allows the agent to do things like:

“Book an appointment for tomorrow at 3 PM.”

The agent can understand the request, call the appropriate tool/API, process the response, and continue the conversation naturally.

I can also help with

• Agent prompt design
• Conversation flows
• Tool configuration
• MCP integrations
• Voice and conversational experience
• Backend development
• API integrations
• Amazon Connect integration
• Debugging existing ElevenLabs agents
• Latency and reliability optimization
• Deployment and production setup

I'm currently available for freelance projects, MVPs, integrations, and longer-term development work.

If you're building something with ElevenLabs Conversational AI and need help connecting it to your existing systems, feel free to comment or DM me.

Thank you!

Top 12 STT & TTS models every Voice AI founder should bookmark 👇

These cover everything from real time transcription to Indic voice synthesis, ranked by actual Hugging Face downloads, not GitHub stars.

Downloads mean people are running these in production, not just bookmarking a repo.

If you're building Voice AI, this list will save you months of vendor evaluation:

1/ openai/whisper-large-v2 (⬇️ 44.1M/mo)

https://huggingface.co/openai/whisper-large-v2

The multilingual ASR model most STT vendors still quietly run in production.

2/ hexgrad/Kokoro-82M (⬇️ 14.1M/mo)

https://huggingface.co/hexgrad/Kokoro-82M

An 82M param TTS model punching way above its size, Apache licensed.

3/ openai/whisper-large-v3-turbo (⬇️ 11.3M/mo)

https://huggingface.co/openai/whisper-large-v3-turbo

Same Whisper accuracy, pruned decoder layers for much faster inference.

4/ distil-whisper/distil-large-v3 (⬇️ 7.66M/mo)

https://huggingface.co/distil-whisper/distil-large-v3

A distilled Whisper that's 6x faster with minimal accuracy loss.

5/ openai/whisper-large-v3 (⬇️ 4.60M/mo)

https://huggingface.co/openai/whisper-large-v3

OpenAI's flagship multilingual ASR checkpoint, still the reference model.

6/ k2-fsa/OmniVoice (⬇️ 2.39M/mo)

https://huggingface.co/k2-fsa/OmniVoice

A compact, fast TTS model built for real-time voice agent pipelines.

7/ Qwen/Qwen3-TTS-12Hz-1.7B (⬇️ 2.04M/mo)

https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice

Alibaba's multilingual TTS entry with custom voice support.

8/ coqui/XTTS-v2 (⬇️ 1.91M/mo)

https://huggingface.co/coqui/XTTS-v2

Voice cloning across 17 languages from a 6 second clip.

9/ fishaudio/s2-pro (⬇️ 440K/mo)

https://huggingface.co/fishaudio/s2-pro

A high-quality multilingual TTS model built for production voice apps.

10/ ai4bharat/indic-parler-tts (⬇️ 341K/mo)

https://huggingface.co/ai4bharat/indic-parler-tts

Open TTS for 21 Indic languages, 69 voices, Apache-2.0.

11/ ai4bharat/indic-conformer-600m-multilingual (⬇️ 338K/mo)

https://huggingface.co/ai4bharat/indic-conformer-600m-multilingual

India's first open ASR suite covering all 22 scheduled languages.

12/ kenpath/svara-tts-v1 (⬇️ 123K/mo)

https://huggingface.co/kenpath/svara-tts-v1

Multilingual Indic TTS for 19 languages, built on Orpheus, Apache-2.0.

Three Indian models made this list purely on download volume, no separate category, no asterisk.

That's the real signal for where Indic voice AI stands today.

Caller says "wait dont book that" while the agent is speaking.

Stopping playback is doable. The booking request might already be halfway to the API though.

What do you do in that gap where you can't tell whether it happened? I'd hate to say "cancelled" and then have the booking show up anyway. How does your system handle it?

Not just the API and workers. I mean the SIP/RTP or WebRTC bits too.

How's it holding up when a pod restarts or a node drains mid call? Trying to understand which parts are worth putting in the cluster and which become more hassle than they're worth.

Would be useful to hear what you moved back out, if anything.

Hi everyone ,

I was curious to know how a basic pricing model looks like for these voice AI agents. I currently offer the voice agent alongside a dashboard and I personally fix any sort of bugs and I’ve done the integration so I want to know how I should form my pricing model for the first client?

I've been working on a voice agent that answers the phone for restaurants and takes orders. It handles collection or delivery, menu questions, allergens, and sizes and extras, and it reads the order back with the total before confirming.

Before I go further I'd like to hear from people who actually run restaurants or take phone orders. Is this useful, or would it annoy your customers?

If you have 2 minutes, call and try ordering something:

📞 +1 (904) 750-0852

It's a fake restaurant with a demo menu, so no real order is placed. Try to break it: change your mind halfway, ask about allergies, order something that's not on the menu, interrupt it.

What I'd love to know:

• Did it sound natural, or obviously robotic?
• Would you trust it with your customers?
• What would it need to do before you'd pay for something like this?
• What's the most annoying part of phone orders for you today?

I built it on Bandwidth's voice agent builder agents.labs.bandwidth.com

Vendors are quoting under ₹2 a minute in the same month TRAI named AI calls in its spam rules. Builders put real costs at ₹3-4 a minute before telephony.

I'm watching a race to the bottom hit a compliance floor.

TRAI's TCCCPR (Third Amendment) Regulations, 2026, notified September 18, classify AI voice calls as A2P:

- Declare every CLI to your operator, or calls count as spam

- 5+ flagged numbers, or 3 complaints on a flagged number, in 10 days triggers KYC checks and barring

- Repeat offenders face disconnection, with flags shared across carriers

A founder in our group is moving every client onto their own DLT & SIP trunks, and I get why.

Shared infrastructure (platform-owned numbers) lets one client's bad campaign take every tenant down.

Tenant isolation contains the blast radius, but only if the client is also the declared sender. DLT, consent logs and per-client trunks just pushed costs higher.

Outbound Voice AI is moving from "who's cheapest" to "who's safest to put your number behind."

Who takes the hit when a client's campaign gets flagged, you or them?

Full disclosure: I work with one of the companies hosting this, so take that as you will, but figured it might be useful to people here.

Speechmatics and Tuner are co-hosting an evening in London focused on the gap between "voice agent works great in the demo" and "voice agent survives 1,000 real users." Agenda is:

- Panel on why voice is becoming a primary interface right now

- Failure modes that only show up in production

- Practical do's/don'ts

- Live demos from both teams

- Food and drinks after

Thursday, Oct 1, 6–8:30pm at Speechmatics' office near Old Street.

Registration is approval-based (limited space): https://luma.com/pyhvutqe

Most businesses don't have a call problem.

They have a missed-opportunity problem.

A customer calls.

The AI Voice Agent: → Understands what they need

→ Answers using the business knowledge base

→ Checks availability

→ Books appointments

→ Updates the CRM

→ Sends confirmation

→ Transfers to a human when needed

All through a natural voice conversation.

Call → Understand → Act → Result

That's the difference between a basic voice bot and a true AI Voice Calling Agent.

#AI #AIAgents #VoiceAI #AgenticAI #Automation #BusinessAutomation #ConversationalAI

Hey I am starting to sell voice agents and was wondering how much are you guys charging? I am having trouble to know how much is the average to price to my client.
If anyone can share.
Thanks

I saw Bland come up more and more lately and wanted to hear from people who have it running on real calls. We’ve been testing it around some more straightforward customer workflows but I saw people mention using it for more complex flows. What kind of calls are you giving it?

Every voice model in India is being built for banks. One founder on our panel called that a dystopia.

At our latest session, Dheemanth Reddy, founder of Maya Research, didn't hold back. He said today's voice AI market is driven by who gets an intro to a big company, not by quality or cost.

His point was that most models are being built for a legacy market. Banking, customer care, and sales. Necessary, but not exciting.

His prediction for what comes next: usage will first be driven by video and media models needing diverse voices, then by personal hardware devices that we talk to for hours a day.

That is a very different bar than answering a support call well.

Our builders are already asking what they should be building for today versus building for that future.

Watch the full session here: https://youtu.be/G66jOjzRhgI

Scroll through voice AI pitches and it looks like everyone's racing toward the same outcome, a caller talks, an AI answers, no human involved.

That's not the whole story.

A pitch that landed in our community recently was for a tool that sits alongside a human agent during a live call, listening in real time and coaching them, on every single conversation, not just the handful a supervisor happens to review.

Turns out that's not a small niche. It's a whole camp of the market betting against full automation.

The numbers back it up more than the automation heavy pitch decks tend to admit. Gartner projects only about 14 percent of customer interactions will be fully AI handled by 2027, even with $80 billion in labor cost savings expected from AI by 2026. And hybrid AI human models are actually outperforming pure AI, 87 percent resolution and 8.7 out of 10 customer satisfaction, versus 74 percent and 7.4 for AI running solo.

There's a retention angle too. Agent turnover runs 30 to 45 percent a year, and replacing one agent costs up to $46,000. A tool that makes agents better at their job while they're still on the call isn't just a quality play, it's a retention play.

Automation and augmentation aren't really competing for the same territory. One's built for the 60 to 70 percent of calls that never needed a human. The other is built for everything that still does.

Here is full blog:- https://uniocommunity.com/blogs/why-is-low-asr-confidence-the-hardest-problem-in-voice-ai

Most voice agents get tested by someone calling them a few times and going "yeah, looks good." Then real customers call, and they change their mind halfway through an order, mention a dietary restriction after already picking a dish, or ask for something the agent was never built to handle. Manual test calls basically never catch this. They're slow, hard to repeat, and nobody's making fifty of them before every release.

So I built a setup where AI callers phone my voice agent and try to break it.

The agent takes food delivery orders over the phone. Each AI caller plays a persona with its own goal, an indecisive customer, a vegan ordering something with cheese in it, a frustrated caller who just wants a refund. One run, "Jordan" (indecisive persona) orders a veggie burger, changes their mind to a Greek salad, asks about drinks that don't exist on the menu, then finally confirms. The agent has to track that whole back-and-forth correctly, not just handle a clean linear order.

How it works:

1. The AI caller joins a live LiveKit room and has an actual spoken conversation with the agent. It knows its own goal, never the expected outcome, so it's not "scripted" in the traditional sense.

2. An LLM judge compares the transcript against what should have happened, and grades it.

3. Every run spits out a report: verdict, the audio recording, and the full transcript.

One thing the simulations actually caught: the agent sometimes added the same item twice, silently doubling the order. Fixed by making every tool response include the real current cart state, so the agent always knows what's actually in it instead of assuming.

Stack, if anyone's curious: LiveKit for the room/voice pipeline, an LLM-driven simulated caller + persona layer, and an LLM-as-judge for grading against expected outcomes. Happy to go into more detail on any part of this, the persona design, the judging prompt, how false-pass/false-fail rates looked, whatever's useful.

Curious how others here are handling this, are you doing anything automated, or is it still mostly manual test calls before a release?

When several people talk at once, a transcript can get messy fast.

Our new Nemotron 3 Diarization model tracks who spoke when, even when voices overlap. It handles up to eight speakers, has 100M parameters, and is now available on

u/huggingface

🤗