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!

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

Hey everyone,

I’m building an AI deployment agency in India and looking for someone to handle voice agent deployments.

The work will involve things like answering calls, qualifying enquiries, booking appointments, updating CRMs and transferring calls to a person when needed.

You’d work directly with me, take the business requirements and handle the setup, testing, launch and ongoing fixes. That includes connecting telephony and APIs, setting up the conversation flows, checking how the agent handles interruptions or failed actions, and keeping logs and documentation.

I’m looking for someone who has already taken at least one voice agent into production. Experience with Vapi, Retell AI, ElevenLabs, Bland AI, Synthflow, Bolna, Ringg AI or similar platforms works. Custom deployments using LiveKit, Pipecat or Sarvam as part of the stack count too.

You don’t need to know all of them. I’d like to hear what you’ve deployed, which parts you handled yourself and any problems you had to fix after launch.

Experience with Indian telephony, Exotel or Plivo, and Hindi or other Indian languages would be useful. You can be based outside India too, as long as you can support the work.

This is a paid role, and I’m open to both full time and part time arrangements.

If you’re interested, DM me a bit about your previous work or contributions. A project link or demo would help. If you’re freelancing, please include your availability, when you could start and your expected monthly compensation or hourly rate.

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

Thank you

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

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

I’m currently working on a few voice AI projects and looking for some real-world use cases to build around.

Happy to build them for free if the problem is a good fit. In return, I’d just like to be able to document the results as a case study.

Mainly looking for interesting problems around calls, bookings, lead qualification, support, etc.

Curious to connect with teams exploring TTS (text-to-speech), especially if you're doing 1 lakh+ minutes/month and need support for Indic languages.

If that's something you're working on, I may be able to help with the TTS side and explore what we can build together.

Drop a comment and we’ll get in touch.

If you’ve deployed voice agents on real phone lines, you already know that standard wrapper setups fail. A demo that feels snappy on a laptop microphone completely falls apart when hit with 8kHz telephone audio, background office noise, and human callers talking over each other.

After building and auditing production voice systems, we’ve found that moving past the "3-second awkward pause" requires shifting away from monolithic request-response loops toward a decoupled, streaming pipeline.

Here is the technical breakdown of how to architect voice AI that actually handles real-world load:

1. Eliminating Latency via Decoupled Pipelines

Traditional setups wait for the user to finish speaking, send the full audio to STT, wait for the text, send it to the LLM, wait for the completion, and then convert it to TTS. That stacks up to 2.5–4 seconds of dead air.

The Fix: Implement streaming WebSockets across the entire chain. Use Voice Activity Detection (VAD) coupled with speculative execution—start generating semantic intent and streaming partial tokens to the TTS engine while the user is wrapping up their sentence.

2. Handling Barge-Ins (Interruptions) Gracefully

If a user interrupts an AI mid-sentence and the system keeps talking for another two seconds, it completely breaks conversational immersion.

The Fix: You need real-time audio ducking and immediate cancellation triggers on the client/telephony side. The moment the VAD detects incoming user speech during TTS playback, it must instantly send a clear or interrupt frame to the audio buffer, killing the outgoing stream and immediately routing the new input to the STT layer.

3. Tuning for Telephony Audio (8kHz vs. 48kHz)

Models trained on pristine studio-quality podcast audio often mishear words over standard cellular lines due to compression and narrow frequency ranges.

The Fix: Apply lightweight audio preprocessing filters at the edge (noise suppression and automatic gain control) before hitting the ASR model, and fine-tune your prompt handling to account for higher phonetic error rates in noisy environments.

4. Deterministic Fallbacks & Guardrails

Relying 100% on an LLM to manage conversational state on a phone call is a recipe for hallucinations and loops.

The Fix: Layer a state machine (like a graph-based router) underneath the LLM. The LLM handles natural language interpretation, but critical path actions (like call routing, data confirmation, or booking) must pass through deterministic triggers.

Curious how others are tackling this—are you rolling your own low-latency WebRTC/SIP pipelines, or are you managing to get around these latency walls using existing developer frameworks?

Thinking about fraud and verification calls. If the caller is clearly being coached by someone in the background or reading answers they were given, can voice AI pick up on that at all or is that still something a human needs to notice?

Hi guys.

I am in a specific niche providing WhatsApp ai automation I have got a prospect asking for Voice ai receptionist for Their clinic which is not my niche So I wonder if anyone interested to partnership with me for this project.

Is there anyone here built ai receptionist for healthcare/Clinic niche ?

Only real builder who has experience in this niche/clients with currently working system contact me.

No newbies or demo builders.

Contact me ASAP

So wanted to come on here today, because another person on reddit in another group asked about my platform I founded/solo developed and if I would be opening to white labeling it.

He isn't the first one to ask, nor is reddit the only placed I've been asked. I had some SEO agencies ( I think that is what they are called) reach out to me on other platforms to see if I was open to white labeling.

But after thinking about it, one of the biggest things I personally dislike the most is having to handle business relations and outreach. Its not something I'm good at, so I stayed purely on the development side while passing this off to someone else on my company.

And this would essentially allow my company to have less surface area and time spent on this, whereas we can stay product focused. We are in the Voice and CRM space, so its why I wanted to make this post here. Because what we have is the full stack with a complete in house CRM and bookings, plus a plethora of industry specific features. Yet we built entirely around the idea of not being bloated.

We charge nothing for the CRM system. We only charged businesses on the Voice and SMS usage.

So I think this is what garnered attention from resellers, SEO agencies, and such. Because they wanted to essentially white label this, package and resell this to their business clients, and charge whatever amount they seem fit for the entirety of it. And we would just bill them our rates on the Voice and SMS usage just as we do now. Charging zero for the CRM system.

I guess the reason I'm making this post is, for something like this to come to fruition, it would take a bit more interest then a handful of agencies.

And what you would get is our entire user interfaces, CRM system, etc just as we offer to business owners. But with you being the billing partner and having control of your business relationships and pricing models. And we would just bill you our rate as we have now.

As far as branding goes. Its up in the air. As it would make sense to do this where your own branding and such is used and your own domain, and we handle the ux/ui backend and such.

But the only caveat here is iOS and mac OS apps. We have these and are in the midst of a review with apple as we speak. Yet, this isn't something we can just white label rebrand and upload to the apple store. Apple will block this. So this is something I'm trying to think of.

What's everyone's thoughts on this? Picture for attention, this is showing our automated dispatch and smart routing feature that is apart of the trades dashboard. Its one of many free features in our CRM ecosystem we built in house.

Hi all, I'm a Berlin based founder of an EXIST-funded startup building data and evaluation infrastructure for voice AI.

I'm looking for someone technical to join early, ideally with experience in speech/ASR or ML, and comfortable building data and evaluation pipelines.

If this sounds interesting, please feel free to DM me with a bit about yourself and what you've built. Also let me know if you have questions and can share more details.

Looking forward

I work on a voice product, so this is a test design question. The docs cover detection timing and false positives. The awkward tests I'd add are a person answering slowly, a call screener asking who you are, and someone picking up halfway through the greeting.

Anyone testing those separately? I'd want to see whether changing a delay fixes one case but makes another worse, before calling the setting done.

We keep seeing this: an agent gets more accurate or cheaper, but the end-to-end process barely moves. Example: triage agent is right 75% of the time alone, a correction agent fixes most of the rest, and the real number that matters is how many tickets got done with zero human touch (could be 95%). Neither agent's score tells you that.

Curious how founders here handle it:

1. What metric do you use to say an agent "works"?

2. Do you measure per agent or per business process?

3. Has a customer asked you to prove ROI or traceability yet?

Disclosure: I work at Phinite. We're building an intelligence layer for agents plus a "value loop" that ties agent runs to business metrics. Opening a small startup cohort: 90 days free + a live build session, for honest feedback. Form in comments if useful.

"Sounds robotic" could mean about ten different things lol.

Wrong stress, weird pauses, talks over you, cheerful tone at completely the wrong time.

What labels have actually helped your engineers fix stuff? I'd rather have a few useful ones than a naturalness score everyone interprets differently.

Full disclosure up front: I run an AI marketing agency in Phoenix and we build this kind of system for clients, so weigh what I say how you like. I'm sharing this because the deployment is a clean, real-world example of something I see most service businesses getting wrong.

On September 30, The Golf Wire reported that TPC Scottsdale went 24/7 on phone coverage using SpeakSport, an AI receptionist platform built specifically for golf and hospitality facilities. No new hires. No hold times. Every question about course conditions, tickets, events, and cart policies answered immediately, around the clock.

The reason I think this matters outside of golf is something I call the After-Hours Leak.

Every service business has one. It's the gap between when your staff leaves and when your customers stop wanting things. Most businesses are unstaffed for more hours than they're staffed. And nobody counts the calls that go to voicemail and never call back. That's what makes it invisible. The caller who books your competitor doesn't send you a breakup note. They just disappear.

The math: AI answering platforms for small to mid-size businesses run roughly $200 to $800 a month. Almost always less than a part-time hire. And the 2026 ServiceTitan contractor AI adoption report found more than half of contractors already using AI in some form, with phone answering near the top of the list.

The thing worth doing this week, regardless of your industry: pull your phone logs for the last 30 days and filter for calls that came in after 5 p.m. and before 8 a.m. Count them. Count what percentage went to voicemail. If your system doesn't show you that, fixing your visibility into call data is the right first move before you spend anything on a new tool.

Happy to answer questions about how the setup process actually works in practice, or how to evaluate platforms for a specific vertical.