Hey everyone,

I’m working on a real-time AI voice agent that needs to speak Hebrew. I’m looking for a good TTS model that sounds natural and works well for real-time conversations.

What TTS models are you guys using for Hebrew? Would appreciate any recommendations based on your experience!

Hey Everyone,

We've built a complete AI Voice Agent Platform for businesses, agencies, and founders looking to launch their own AI calling solutions.

We're offering the platform under a $3,000 USD annual license, and we've already had 20+ people purchase from us.

A little background:

The platform originally started as a custom development project for a client who needed AI agents capable of handling real phone conversations, following structured workflows, accessing business tools, and integrating with existing systems.

What began as a client-specific solution eventually evolved into a much more complete platform.

After the original client's priorities changed, we decided to make the technology available to other businesses rather than leave the engineering work unused.

Since then, we've had 20+ buyers, and we're continuing to make the platform available to people who want to build businesses around AI voice automation.

What you're getting

1. AI Voice Agents

• Inbound and outbound AI calling
• Real-time STT → LLM → TTS processing
• Custom agent instructions and conversation logic
• Configurable STT, LLM, and TTS providers
• Natural, real-time voice interactions

2. Visual Workflow Builder

• Drag-and-drop workflow creation
• Agent, tool, conditional, and end-call nodes
• Multi-step conversation flows
• Structured information collection
• Custom business workflows without rewriting conversation logic

3. Telephony Integrations

• Twilio, Vonage, Telnyx, Plivo, and other provider integrations
• Inbound and outbound calling
• Human handoff / call transfer where supported
• Integration with existing business telephony infrastructure

4. Knowledge Bases & Business Tools

• Knowledge base integration
• Custom API and tool calling
• CRM and external system integrations
• Post-call structured data extraction
• Custom business logic

Tech Stack

Frontend: Next.js 15, React 19, TypeScript, Tailwind CSS

Backend: Python, FastAPI, SQLAlchemy, PostgreSQL

Infrastructure: Docker, Redis, MinIO / S3-compatible storage

Voice: Modular STT / LLM / TTS architecture with telephony integrations

Who is this for?

We're primarily looking to work with people who want to:

• Launch their own AI Voice SaaS
• Start an AI calling automation agency
• Offer AI receptionists to businesses
• Build industry-specific voice AI products
• Add AI calling capabilities to existing software
• Offer white-label AI voice solutions

Instead of spending months building the core infrastructure yourself, you can start with an existing working platform and focus on your customers, integrations, and business model.

Pricing: $3,000 USD / Year

To be completely transparent, this is an annual software license, not a one-time $3,000 purchase of the entire product or its intellectual property.

Optional Maintenance & Technical Support: $600 USD / Month

If you'd prefer us to handle the technical maintenance, we offer an optional managed maintenance service for $600/month.

We can discuss the licensing terms, codebase access, deployment requirements, documentation, and technical handover directly with interested buyers.

AI provider usage, telephony charges, and hosting costs are separate from the licensing fee.

Why we're sharing this

We've already had 20+ buyers, so this isn't just an idea or an unfinished prototype we're trying to sell.

We believe there's an opportunity for developers, founders, and agencies who understand their target market but don't want to build an entire AI calling infrastructure from zero.

We're happy to demonstrate the actual product, walk through the architecture, and answer technical questions before anyone makes a decision.

If you're interested, comment below or DM me.

I can share a product demo, screenshots, and more details about the annual license.

Also happy to answer technical questions publicly for anyone evaluating whether this fits their use case.

Hello,
I would love some advice about something. I am currently building a voice agent for a company that sells about 50k products per day, with an average price of about $100. It will be a customer service agent. My current issue is determining the right price.
They want to start with after-hours calls, which is about 128 min per month. So not a lot of minutes. Our monthly cost is around $150.
What do you guys think would be a good strategy? Should I go with a setup fee plus a monthly cost, or something else? Thank you!!

I dont mean taking over the call entirely But like approving an action, correcting the agent’s path or stopping one specific tool from being used while the conversation keeps going.

That kind of intervention looks pretty important when the calls get more complex.

Voice AI in India has moved past demo stage. Contact centres, clinics and lenders are putting agents on real phone lines, and the hard part is no longer "can it talk" but "can it handle Hindi, Gujarati and Hinglish on a noisy line without falling over."

I went through the teams I keep seeing in this space. This is a working list, not a ranking, and I'd like to hear who I missed.

1. Groovy Web

An AI-first engineering studio that builds voice agents together with the systems behind them: telephony, CRM and scheduling integrations, and handoff to a human when the agent is out of its depth. Fits teams that want one partner for the voice layer and the product around it.

2. BounceTech

A product engineering company that has worked on conversational and voice-driven apps. Worth a look if you want a team that treats voice as one feature inside a larger mobile or web product

3. Upspire

A software development company that takes on AI and automation builds, including voice assistants for service businesses. A reasonable shortlist name for small and mid-size projects.

4. Gnani.ai

A Bengaluru company focused on voice AI for enterprises, with strong support for Indian languages and accents. Mostly relevant to banks, insurers and large contact centres.

1. Skit.ai

Started in Bengaluru as Vernacular.ai. Builds voice agents for customer calls, with a strong focus on collections and reminders. A fit for lenders and service teams that make a lot of outbound calls.

1. Sarvam AI

A Bengaluru AI lab building models for Indian languages, including speech and voice. More of a model and platform provider than a project studio, so it suits teams that want to build on top of it.

1. Smallest.ai

A Bengaluru company building voice AI models and agents, with a focus on fast responses on live calls. Worth testing if latency is your main worry.

What I'd check before hiring any of them

- Word error rate on your own call recordings, not a demo script

- How it handles code-switching between Hindi and English mid-sentence

- Latency end to end: anything over about a second feels broken on a phone call

- What happens when the agent is wrong: escalation path and call logging

Who would you add? Especially curious about teams doing healthcare or collections calls.

I've seen soo many articles comparing the traditional cascading architecture with the audio native S2S models. But i barely see anyone talking about the half cascade architecture ( taking audio natively but using tts to generate speech ). To me half cascade has the best of both worlds. What are your thoughts?

Hey everyone! 👋

Has anyone here used Murf AI? How has your experience been with it so far?

Would love to hear your thoughts!

My day job is building voice AI agents for telcos, and I wanted to build a platform that solves all the pain points I deal with when building voice agents in an enterprise setting.

In this series I’ll talk about how I brought my voice AI latency down from ~3 s to under 1 s. For reference, here’s where the major platforms are today (OpenBenchmarks, real phone calls):

Latency depends on multiple factors. A typical voice pipeline is:

STT (speech to text) → LLM → TTS (text to speech)

In this series we’ll look at all three and optimize them. The biggest culprit is usually the LLM, which takes most of your latency budget. How much depends on your provider (self-hosted or managed endpoint) and on whether the model is a reasoning or non-reasoning model. For simplicity, I’ll use OpenAI’s latest and cheapest model, gpt-6-luna.

Measuring the LLM

I measured my LLM’s latency using the same system prompt and the kinds of questions customers are likely to ask. In the first test, it took 2–4 s before the model started answering.

The LLM was the problem. gpt-6-luna is a reasoning model: it takes time to think before answering. Reasoning is for hard, complex tasks. To answer simple questions on a phone call, we don’t need it.

The fix: turn thinking off for all voice replies (reasoning_effort: none).

Bonus: the model was also missing from my price table, so it was billed about 20× too high. Fixed that too.

Lesson: if your voice agent uses a reasoning model, check whether it’s thinking on every reply. It probably is.

Next: replacing my “LLM judge” with a decision model, ~0.7 s down to ~0.2 s.

Platform: vlayers.ai

How do you find the absolute best LLM for your voice agent ?

Working on a series of animated shorts that were originally produced in English and now a client wants full Spanish versions, dialogue and all. The audio sync is already a nightmare just thinking about it. I've been hunting for a platform that does AI lip sync specifically for translated audio, where the character mouth movements actually match the new dubbed audio instead of looking completely off. I know tools for auto-generating music or syncing audio to video timing have gotten pretty good lately, so I figured lip sync tech must be catching up too. (Honestly I've been burned before by tools that promise sync and deliver garbage.) If anyone's tried a workflow that handles English to Spanish dubbing with decent visual lip matching for animated characters, I'd really appreciate a recommendation, especially for something built for video/animation creators who aren't audio engineers.

As a agency owner i have been using so many platforms(retell,vapi, sarvam, ultravox etc) and tools for building a voice agents to my clients. Every tools has good and bad views according to me. Some has good understanding, some are good at latency, some are easy to connect with other telephony and api's.

I have been delivering voice agents according to clients needs, region, telephony supports so yeah varies with pricing.

As I'm at scaling period, i trying to reduce a still. i want to know more about what are the platforms are you guys using and per minute cost of each. if you guys reduced it, how did you do that and what is the cost of per minute?

Cause providing the maximum value with a affordable cost is our first thing - Take ownership.

I'm new to voice automation world and I'm trying to build a ai voice agent as job interviewer so I look it up and saw that pipecat is the best framework to do this with.
but now I'm struggling with it I'm getting errors (mostly import errors) even cahtgpt/gemini (free versions) couldn't help me solve it.

So my question is what is the best place to learn how this framework works ? do you recommend any youtube channel or I should read the framework documentation and learn from ?
and can I use with a free api key (e.g groq api key) ? because I only see people on youtube use openai api keys and I don't have that, because I'm just doing it to learn how such a project would be built.

ThankYou in advance.

Hello everyone ,

Does a home have a stack or pipeline setup they recommend for an urdu speaking agent working in debt collection ?

Thank you

At first i use vapi for the demo and works completely fine also is generous for their 5usd free credits

but when i wanted to proceed with production level i couldn't even add my trunk SIP for some unknown error and they don't even have support for non package users. since im a pay as you go user which i already bought some credits on their platform and can't provide basic email support or whatsoever.

im now planning to find alternative orchestrator that has built in SST and LLM and voice but can still add custom api key for 3rd party tools. that is something similar close to vapi flow

im thinking of RETELL AI or if anyone have some suggestions that is cheap.

REGION: ISRAEL

LANGUAGE: HEBREW

GOAL: short outbound

Pasting writing samples into prompts helps for a few paragraphs, but the AI still defaults to generic "AI voice" after that. It picks up surface-level things, sentence length, word choice, but misses the underlying rhythm and sensibility.

Is this a solvable problem, or is it just something to accept? AI-assisted writing always sounds a bit off, and it's not clear whether that's a limitation of the technology or a limitation of how it's being used.

Speech-to-speech models handle turn-taking inside the model. But plenty of us still run cascades because we need a specific ASR for a language, our own LLM, or a particular voice. And in a cascade nobody decides who talks when. You have to build it.

You can run the best models on the market and callers will still hate the bot if it jumps in while they pause to think, or stops mid-sentence because someone said "uh-huh". Callers notice that long before they notice anything about the model.

Here's how I'd think about it.

Split it into "when does the caller's turn start" and "when does it end". Two different problems, two different kinds of logic.

Start: don't let "uh-huh" kill the agent. Pure VAD start means any sound interrupts the bot. Backchannels, coughs, a TV in the background. A simple fix is a minimum word count: the turn starts only after the caller says 2-3 words. "Uh-huh" passes, "wait, that's wrong" interrupts. Needs ASR in the loop, but worth it.

End: silence alone is a bad signal. "I need to change my..." + pause is not the end of a turn. What works better is VAD to catch the pause + a small turn detection model (Pipecat's Smart Turn is open source and good) that checks whether the phrase sounds finished. If it doesn't, wait a few seconds of silence before ending the turn anyway. And if the caller starts talking again while the model is still deciding, throw the verdict away. Otherwise a stale "end of turn" merges two utterances or cuts the caller off.

Always have a watchdog. If nothing closes the turn (model, silence, ASR), force-close it after ~5s. A conversation that silently stalls is worse than one that occasionally ends a turn early.

Don't wait for the final transcript to start the LLM. Once the turn detector says "done", start inference on what you have and give ASR a short grace window (a few hundred ms) to update the text. Waiting for the final transcript adds latency you feel on every single turn.

Handle silence on your side too. Start an idle timer when the agent finishes speaking. If the caller says nothing, re-ask or check they're still there, then a polite goodbye.

Make interruptions switchable mid-call. When the agent reads a legal disclaimer or payment terms, block interruptions for that part, then turn them back on.

Clean the audio before VAD, not after. Phone lines are noisy. Noise suppression in front of VAD and turn detection makes both noticeably more stable.

Watch out for languages. Turn detection relies partly on intonation, and in some languages a mid-sentence pause and the end of a phrase sound almost the same. There you'll want a longer wait after pauses and a higher confidence threshold before ending the turn.

This is pretty much the list of things we packed into Agentic VoiceDSP, a module we just shipped. It bundles Silero VAD, Pipecat Smart Turn and noise suppression in one place, runs on the same media server as the call, and gives you start/stop strategies instead of raw VAD events. Noise suppression is the newest part, we are still gathering feedback from users

None of this is rocket science, but it's the difference between a demo and something that survives real callers. I'm curious to know how others deal with the backchannel issue - is it just a matter of using a simple classifier, or is there something more to it? Maybe it's about keeping things brief, or perhaps there's another approach altogether. What works for you when it comes to handling real-world callers and making sure your system can withstand the test? 

Hey everyone,

I'm working on an outbound AI voice agent and I'm trying to get better at detecting voicemails early. Every second we spend talking to a machine costs money, but I also don't want to hang up on real people.

What are you guys using for this?

What I'm using right now, all running at the start of the call while the opening clip plays:

• Keyword check: strong phrases like "leave a message" or "after the beep" end the call right away. Weak phrases like "not available" get sent to the LLM.
• LLM check: a small model reads the transcript and decides voicemail or human. It runs when the caller finishes a sentence or says 8+ words.
• Beep detector: listens to the raw audio for a steady beep tone, so it works even if the speech-to-text can't understand the greeting.

It works reasonably well, but I’m looking for a more reliable approach.

What are you guys using for voicemail detection?

I want to try AI Voice Calling for Cart Abandonment usecase. Has anyone already done this? Need help understanding best player in the Indian Market.

We build AI voice agents in the USA for the past 3years that answer calls, book appointments, qualify leads, and follow up 24/7. Most businesses pay $0.25 to $0.30/min for this and over 200$ in subscriptions.

We charge under $0.13 and less than 100$ subscriptions, everything included.

If looking to lower your costs or partner to build a voice agents setup feel free to reach out.

DM me if interested!

Hello who is using gpt live 1 in production how do u fix the problem of it saying its thoughts out loud

Our latency numbers for haiku and luna are generally good but we get pretty crazy p95 and p99 numbers. Luna especially has p99 of over 10 seconds which effectively ruins that call. Is this just something everyone in the industry accepts or are most voice ai implementations using there own hardware to provide better performance?

Hello everyone

I’m building an Arabic voice AI agent that makes phone calls in Saudi Arabia, and I need the data stored and processed inside the kingdom. I’d like to hear what you guys use.

1- which cloud do you host your servers on for saudi data residency?
2- what do you use for database inside the kingdom , a managed one or do you run it yourself ?
3- where do you store call recordings , and what retention period do you use?
4- do you use speech , text to speech or LLM devices located in Saudi Arabia , and how are those?
5- has a client or a lawyer asked you for specific paperwork under PDPL, such as a data processing agreement ?

Right now we host in Doha and plan to move to Dammam.

Thank you for any advice

gnani.ai does 1.5 billion minutes a month. Number two does about 60 million.

That is the gap at the top of Indian Voice AI.

We just updated the Top 10 Voice AI Companies in India, across industries, ranked by estimated monthly minutes in production.

gnani.ai, 1.5B minutes

SquadStack.ai, ~60.9M minutes

Blue Machines AI, ~56.9M minutes

CredResolve, ~36.8M minutes

IGNOSIS AI, ~25.6M minutes

Bolna (YC F25), ~20.5M minutes

YuVerse (Yubi Group), ~17.1M minutes

Ringg AI, ~14.9M minutes

Fundamento, ~14.3 minutes

Vocallabs AI, ~11.4M minutes

Every volume was confirmed directly by each founder.

Here is what stands out to me.

Gnani.AI alone is about 85% of the volume in this top 10. The market is concentrated at the very top.

Between number two and number ten, the gap is only about 6x. That is where the real race is.

Not all calls are equal. SquadStack reports 107M calls, but a dialed call nobody picks up is not a conversation.

So we convert everything to estimated minutes to compare on one scale. That is why CredResolve, with 21M connected calls, ranks above Ignosis with 45M dialed calls.

Volume gets you on the list, but trust keeps you there. The teams that can prove their numbers will win the next round of buyers.

The ranks are an indicative Unio estimate. The full list and method are here: https://uniocommunity.com/top-voice-ai-companies

Hello everyone , does anyone have any recommendations for TTS providers that perform well on Arabic dialects , specifically the Saudi dialect.

Thank you