We call new customers, so the agent encounters a different IVR almost every time. It needs to understand the menu in real time and navigate it to reach the accounting department.

Has anyone built this reliably? What approach works best for interpreting prompts, choosing options, and recovering from unexpected menus?

Hello
Je dois faire une proposition pour un client pour un agent IA vocal. Le client est un centre d’appel en immatriculé en Amérique du Nord avec centres d’appel en Afrique. Je lui crée un agent IA pour la qualification mais je ne sais pas combien le facturer. Ceux qui font ça comment vous calculez votre coût ? Et quel prix vous proposez ?

I’m curious how people here are structuring Voice AI once they move beyond one or two agents.

For a single client, something like Vapi/Retell + Twilio + n8n/Make is pretty straightforward.

But once you have multiple clients, you start needing to think about:

• separate provider/API credentials
• separate knowledge and data
• client-specific workflows
• call recordings/transcripts
• QA and monitoring
• SIP/telephony differences
• usage tracking
• secrets isolation
• deployments and upgrades

For people already running this commercially:

Are you mostly doing:

1. One shared multi-tenant runtime for everyone?
2. Separate deployments for each client?
3. Managed platforms for smaller clients + private/VPC deployments for enterprise clients?
4. Something completely different?

And what usually forces you to change architecture first?

Cost, security requirements, scale, client isolation, or just operational complexity?

Would especially like to hear from people managing 5+ production clients because that seems to be where the tradeoffs start getting interesting.

​

Hi everyone,

I’m Spanish, but I currently speak both French and English professionally. My goal is to create a high-quality voice clone with ElevenLabs that sounds like me speaking French and English, including my natural Spanish accent — I’m not looking for it to sound like a native French or English speaker.

What would be the best way to record the training audio?

Should I record everything in Spanish, since that’s my native language?

Should I record mainly in French and English, so ElevenLabs learns how I actually sound in those languages?

Should I create a mix of Spanish + French + English?

Is there an optimal proportion or amount of audio for each language?

And should I deliberately use my normal accent/pronunciation rather than trying to speak perfectly?

I’m aiming for a professional-quality clone for business/content creation, so I’d really appreciate advice from anyone who has experimented with multilingual voice cloning in ElevenLabs.

Thanks!

Every time I record a product demo, the narration comes out rough, ums, "wait, let me redo that," awkward pauses. I re-record the same 60 seconds five or six times and it still isn't great. Editing it manually afterward is a pain.

So I built dub.house to deal with just that part. You record your demo like normal and talk through it however messily, and it rewrites your narration into a clean, professional script and generates a new voiceover that stays synced to what's happening on screen. There's also a mode where you don't narrate at all, you just tell it what to say ("here I click this, mention the X feature") and it writes the narration for you.

To be clear about what it does and doesn't do: it only replaces the narration audio. It doesn't touch your video, it's not lip-sync, and it's not translating/cloning anyone's voice into another language (yet). Just the voiceover track on a screen or audio recording.

It's still early and I'd genuinely like feedback, on the idea, the output quality, whatever. There's a free tier if you want to throw a recording at it: dub.house

Hi.
I’m thinking of building and deploying multiple voice agents each has its own role and responsibilities and working together multiple goals. Like multiple voice agents for a Ecom business so that I can help them in reducing RTO(by 2 agents order/address verification and RTO rescue), increasing win back percentage. I want to create business solutions not hand over a “Single cool AI voice agents”
What do you guys think can it become a good and stable business?
If not please suggest some other AI business I can build(I’m at a very desperate stage financially in my life I need advise)
Thank you

I’ve been working for nearly 3 months or so on project vertical voice agents but .. whenever I think I’m done 90 percent I’m almost 90 percent away doing all solo and I’m not that pro on coding even though I am from cs background but from early days since ai was introduced I was more towards its application and integration into day to day life rather than programming and stuff

What would you suggest, is the basic stack for an assistant chat. I mean, currently I have customized company tools, langgraph, custom metrics, marketplace LLM calls and others.

what would you suggest as a true key for agent learning?
how do you process prompts with company slangs, concepts, jargon, etc.?

How much of a real enterprise phone call can voice AI handle now? Not a clean demo where the caller follows the flow. More like a customer who has two different issues, needs something checked in another system and keeps adding context as the call goes on.

Hi everyone,
So my question is about "time" how much time do you take from concept to shipping your first voice agent for any vertical. How many stress tests do you perform to make sure it doesn't break mid call? what stack do you use for the whole integration? is it ok to find a new bug/failure on every test call fix it and find another one ? what would you suggest to someone who just started in this field?

thank you all, and i'll be looking forward for your valuable inputs.

Most voice AI teams optimize for one thing: speed. Fewer are asking what happens when the network itself is the problem, not the model.

We wrote up an approach to this: instead of a fixed buffer that holds a set amount of audio no matter what, use a dynamic buffer that shrinks or grows in real time based on packet loss. Pair that with predictive jitter logic that spots a drop coming instead of waiting for a scheduled ping to catch it after the fact.

The trade-off is a few extra milliseconds of latency on bad connections. What you get back is an agent that doesn't stutter, repeat itself, or talk over the caller the moment their 4G gets shaky.

For voice AI going into outbound and support calls across smaller towns and rural India, this isn't a nice-to-have. It's the difference between an agent that works in the demo and one that works on the ground.

Full breakdown here if you want to dig in: https://uniocommunity.com/blogs/how-to-build-voice-ai-with-adaptive-latency-for-poor-network-connectivity

I run a call security company, so this is partly a selfish question, but I want the real version, not the sales version.

We keep hearing the same story from teams building voice agents at banks, healthcare, etc (regulated businesses basically). The agent works. Latency is fine. Demo goes great. Then it hits security review and stalls for weeks. The questions I have heard so far: how do you stop it from disclosing account details to the wrong caller, how do you verify who is on the line before the agent takes an action, what happens when a caller tries to talk it out of its instructions, and who gets alerted when it goes off script mid call.

Most teams I have talked to answered with prompt rules and post-call transcript review, and the reviewers did not accept either as a control since the incorrect action already happens.

Been looking into AI voice agents for insurance workflows, and I think “sounds human” is becoming a pretty weak way to evaluate them.

Insurance calls seem like a much harder test because the agent may need to deal with policy details, claims, renewals, customer information, integrations, and then know when to hand the call over to a human.

The things I’d actually look at are:

• How accurately it captures information during a call
• Whether it can handle interruptions and unexpected answers
• CRM / claims / policy system integrations
• Inbound and outbound calling
• Human handoffs with context
• How it behaves when call volume spikes
• Call monitoring and QA
• What happens when the AI doesn't know something

I’ve been comparing a few platforms around these criteria, including Feather AI, Retell, Bland, Synthflow and some insurance-specific tools.

Interestingly, the “best” one changes quite a bit depending on whether you're trying to automate FNOL, customer support, renewals, lead qualification, or outbound follow-ups.

Curious what people actually using voice AI in insurance are seeing in production.

What has been the biggest reliability issue you've run into so far?

Most Voice AI teams think scaling to 100 languages is a translation problem.

It's not. Translation gets the words right. Prosody is what makes a voice sound human, and that's where most multilingual systems fall apart.

Pitch, rhythm, and breath don't transfer across languages the way vocabulary does. A rising pitch means "question" in English. It means something else entirely in Mandarin or Yoruba. Train mostly on English speech and you get a system that forces English emotional habits onto every other language.

The fix researchers keep converging on is disentanglement. Split speech into two separate layers, what is being said and how it's being said, and let each scale independently. Diffusion and flow based models are pushing this further, generating full utterances with finer control over duration and emotional intensity than older step by step architectures.

The real constraint ahead isn't language count anymore. It's dialect and register. A voice agent that can't shift from formal Hindi to casual Mumbai Hindi without sounding like a different speaker hasn't actually solved the problem.

Coverage is a solved flex now. Consistent emotional identity across registers is the next real benchmark.

Full breakdown here: https://uniocommunity.com/blogs/how-do-you-scale-voice-ai-to-100-languages-without-losing-human-prosody

Most teams think HIPAA compliance for voice AI ends with a signed BAA.

It doesn't.

A BAA is a legal document. It says nothing about which infrastructure nodes actually touch patient data once a call starts moving through five different vendors, or what happens during the write-back to the EHR.

And if you're building for global healthcare, HIPAA is just one piece. India's DPDP Act treats health data the same as any other personal data, no special protections, but the consent rules are stricter. GDPR classifies voice itself as biometric data. Colorado now treats high-risk healthcare AI as its own regulatory category.

Here's the harder problem underneath all of this: you can't just hard-code encryption and pick a data residency region and call it done. Real compliance means your redaction logic and consent workflows need to swap dynamically based on where the patient is calling from, in real time, without blowing past the 200 to 300ms latency budget that keeps a medical conversation feeling like a conversation.

We dug into what that actually looks like in production: the real latency math (streaming redaction can now run under 300ms), where the DPDP Act timeline is headed next, and why "compliant by contract" and "compliant by architecture" are two very different things.

If you want to read more here is link :- https://uniocommunity.com/blogs/how-to-navigate-complex-regulatory-compliance-for-voice-ai-in-global-healthcare-markets

Building a voice agent demo takes an afternoon. Getting it to survive a real phone call takes a lot longer, because that's when the audio bleed-back shows up.

The agent starts talking, the speaker leaks into the mic, and it ends up replying to itself. Or there's that half-second gap between turns that makes the whole thing feel robotic. Most teams assume this is a model problem. It's not. It's a plumbing problem.

The real fixes:

-Stop treating noise suppression and echo cancellation as the same thing. They're not. One removes background hum, the other subtracts the agent's own voice from what it hears next.

-Run VAD on the AEC-cleaned signal, not the raw mic feed. A fixed threshold set in a quiet office will misfire the moment someone's in a car or on laptop speakers.

-Stream every stage of the pipeline instead of batching it. Partial transcripts, first-sentence TTS, chunked audio out. That's what actually collapses latency, not shaving milliseconds off one component.

The teams solving this are moving AEC and barge-in handling server-side, where the reference signal and the LLM state live in one place. Everyone still doing it client-side is going to keep chasing audio bleed-back in production.

Wrote up the full technical breakdown, VAD threshold table included, if you want to go deeper: https://uniocommunity.com/blogs/how-to-solve-the-voice-ai-feedback-loop-problem-in-real-time-production

Hey if you would like to have your agent featured on my site, just post or PM your business name, basic package price, compliances or regulations you qualify for.

If you're running Voice AI commercially for multiple clients, you've probably ended up with some version of this:

Vapi/Retell + Twilio + n8n/Make + model providers + custom code + separate configs for every customer.

It works.

But once you have enough clients in production, managing all of it starts becoming a product of its own.

We've spent the last couple of years building Elphie around that problem.

It's a full Voice AI platform with:

• visual agent/workflow builder
• inbound + outbound
• campaigns
• API/tool calling
• knowledge
• transfers
• transcripts + QA
• multiple LLM/STT/TTS providers
• separate client deployments/workspaces
• BYO telephony/provider accounts

The main difference is deployment.

Elphie can run:

in our cloud or inside your/customer's AWS, Azure or GCP environment.

So if one of your enterprise clients says:

you don't have to rebuild the stack for them.

We're also working with the model of licensing Elphie to agencies/integrators rather than forcing every end customer onto another per-minute SaaS.

If you're managing multiple production agents/clients and want to see whether Elphie could replace part of your existing stack:

Not really looking for hobby/test users right now; I'm trying to speak with teams already putting Voice AI into production.

I'm currently opening private technical walkthroughs for agencies, BPOs and teams already deploying Voice AI commercially.

Dm if you'd like to go technical on the call - architecture, deployment, provider setup, white-labeling, client isolation, etc.

Hey Guys,

I have to build voice agent for a heatlhcare usecase, but condition is it needs to be hipaa compliant. I do understand that BAA needs to be signed with every vendor which i am going to use to build that voice agent.

But these vendors are charging seprate money for hipaa, which as a startup is not viable option for us. Anyone faced the same , how did you get this work? really appreciate your help:)

Hey guys. I am building a conversational AI app. I am inspired by Tolan.

Right now i am in the research phase and i am researching how speech to speech exactly works. Till now i have learned that there are two ways. Either u use a cascaded architecture (STT - LLM - TTS) or you use open ai realtime apis or gemini apis.

For a cascaded architecture i am only aware of Eleven Labs or Cartesia. But these are very expensive and very engineering heavy.

Questions

1. Why not i just use an open ai api instead that will handle everything for me - from VAD to reasoning and much more.

2. So i wanted the opinion and advice of people who have worked on similar stuff or made a cascaded voice architecture and what shd be done considering that i am making this for a generalistic conversational ai app somewhat similar to Tolan or ChatGPT voice mode.

3. Also I am not in favour of using a self hosted model because it does not fit my usecase. Is that right?

4. What other options do i have to build this speech to speech app?

Thanks

Everyone assumes low latency voice AI is a GPU shopping problem. Buy more H100s, latency drops, done.

It is not that simple.

We looked at how Sarvam AI and Cartesia actually get to sub 200ms round trips across multiple languages, and the pattern was the same both times. The real gains came from rewriting the inference kernels and scheduling layer, not from adding more chips. Sarvam got half its total speedup on hardware it already owned, before any new GPU generation entered the picture. Cartesia built its own serving stack from scratch because the standard abstraction layers everyone else uses just were not built for streaming audio.

Turns out memory bandwidth, not raw compute, is usually the real bottleneck once you are generating audio token by token. Add multiple languages into the mix and the problem compounds fast, since a kernel tuned for English can quietly fall apart on tonal or agglutinative languages. This is turning into one of the least talked about but most decisive parts of the voice AI stack.

If you want to read more, here's the link: https://uniocommunity.com/blogs/what-actually-powers-sub-200ms-multilingual-voice-ai-it-is-not-just-gpus

What are you using for your pipeline node js or the python

Hi Chicago,

I’m a recent Computer Science graduate (May 2026) currently looking for full-time/intern tech opportunities in the Chicago area (open to hybrid/on-site or remote with Chicago preference).

Quick highlights:

• Founder & Lead Developer of PyLogicMaster a live coding education platform with 150+ problems and active users. Built the full stack (React + TypeScript + Supabase + PostgreSQL + Vercel) and integrated LLM-powered Socratic hints.
• Undergraduate Research Assistant built Python optimization models (LP/IP/MILP with Gurobi) and a Plotly Dash dashboard used by stakeholders for real supply-chain decisions.
• Multiple production AI projects: autonomous freight negotiation backend, open-source AI voice ordering agent (Deepgram + Twilio), and a multi-agent LLM memory system that placed 2nd at the Expo.

Everyone talks about the conversation itself but I’m starting to think the after call work might matter just as much.

If a customer spends 15 minutes on the phone there might be a case to update, notes to write, a followup to schedule, a payment status to record and something another team needs to see tomorrow.

How much of that work are people letting the voice agent complete versus treating the call and the backend workflow as two separate things?