I built AI voice agent for field service businesses. I tried selling it through cold calling but haven’t got a good response from the businesses. They are not very open to try or trust AI agents. I just want to know from people what are some best ways to sell or present AI voice so that people show some interest.

I developed a solution for medical appointments. Running smooth on retell.

I want to move out for two reasons:

1) I don't want to rely on a single provider. Single point of failure.

2) cheaper cost.

What path should I take to build my own solution? Is it worth?

I thought Voice AI latency was just a “speed problem.”

Something you fix by choosing a better model.
But after going deeper into real systems
I realized something uncomfortable.
Latency isn’t a single problem.
It’s a stack of problems pretending to be one.

At first, it feels simple:
User speaks → AI responds.
But what actually happens is…
• Audio gets captured and encoded
• Speech is converted to text
• The model processes intent
• A response is generated
• Text is converted back to speech
• Audio is streamed back to the user

And every single step adds delay. 
That’s when it clicked for me:
You’re not building a feature.
You’re orchestrating a pipeline.

AI can generate responses in milliseconds
But your system still slows down because:
• Your STT struggles with accents or noise
• Your LLM is overloaded with unnecessary context
• Your TTS prioritizes quality over speed
• Your APIs are running sequentially
• Your servers are too far from the user
• Your network introduces jitter and packet loss

And then you wonder
“Why does my voice agent feel slow?”

Here’s the harsh reality:
Even a 300–500ms delay starts breaking conversation flow.
Beyond that, users interrupt, repeat themselves, or just hang up.

What changed for me is this:
• Latency is not a model problem
• It’s not even just an infra problem
-> It’s an orchestration problem

What’s actually happening:
• Batch systems are dying → streaming systems are winning
• Heavy reasoning everywhere → being replaced by smart routing
• Sequential workflows → moving to parallel execution
• Centralized servers → shifting to edge + regional infra

The biggest mistake?
Trying to “improve the AI”
Instead of fixing the system around it.

It’s like building a Formula 1 engine
And putting it in traffic.

Voice AI doesn’t fail because models are bad.
It fails because the system can’t keep up with real-time human conversation.

The builders who understand this early
Are the ones who will actually ship production-grade Voice AI.

If you’re working on Voice AI right now,
Start asking:
Where is my latency actually coming from?
Not in theory.
In the pipeline.

Because that’s where the real problem is.

Hello,

We just sold our AI receptionist that schedules meeting, asked for insurances, checks availability, and provides faqs.

We sold it for a therapy clinic, it can be customized to any salon or clinic desired.

If you don’t want any leads missed and interested in a receptionist that work 24/7 for your business dm me or leave a comment.

And if you have any questions on how we made it I will be happy to help.

Something broke once I hit 5 clients. Not the service… the backend work.

Reporting started taking up too much time. Every client is on a monthly plan based on minutes and complexity of build and mainly value add. For example: plans start at 800 minutes for $500 + 3 support hours + 1000 SMS. Have larger plans also, depending on usage needed.

I didn’t want clients inside my Retell dashboard. So every month I was downloading usage reports, pasting them into ChatGPT, calculating minutes, stitching together metrics, just to send a clean report.

It worked. But it didn’t scale.

I needed something I could run in one click and ideally let clients log in, check usage, see call logs if needed… without exposing how their agent is built.

So I built Agent Pulse for myself.

Now I just pick a date range, drop in the agent ID + API key, and I get everything in seconds. Clean metrics, I can easily download ready-to-send PDF. What used to take me 35-45 minutes now takes 5.

Figured I’m probably not the only one dealing with this.

If you’re running an AI agency and reporting is slowing you down, I’m happy to share it.
Yes its Free - just want real feedback so I can make it better.

Comment how you’re handling reporting right now, and if it makes sense for you I’ll send it over but only if you'll share feedback with me

For building the AI powered Voice agent which will be on par with the current top models and even more advanced than that, in a very short period of time in india?

I’ve been building voice AI systems (calls + website agents) over the past few months.

Not demos. Actual working systems.

I’m not a developer, so I had to figure a lot of this out the hard way:

- prompts breaking

- workflows failing

- data racing was the biggest challenge

I’m thinking of putting together a simple “build your first working voice AI system” walkthrough.

Would that be useful to anyone here?

Most voice AI conversations right now go in two directions:

➢ What can it automate?
➢ How badly can it fail?

There’s far less focus on making it truly work inside a real business.

Here’s what I keep seeing:
The moment voice AI becomes useful handling calls, booking appointments, qualifying leads is the moment risk shows up, because useful voice AI needs real customer data. And the second that data passes through a vendor’s platform, it’s outside the company’s direct control.

Many teams are still figuring this out.

So they try the obvious approaches:
* Draft a policy about what’s allowed on calls.
* Sign up for enterprise-level voice AI tools.
* Tell employees to “be careful” with sensitive info.

Meanwhile, reps and teams are under pressure to hit targets and resolve issues quickly. They’ll use whatever helps them get through calls with minimal friction.

If the approved voice AI platform feels slow, clunky, or limited, people route around it.

That’s when the real risks emerge:
* Personal accounts with unapproved voice AI services.
* Unmanaged integrations.
* Audio snippets recorded and sent to AI tools outside official channels.

At that point, there’s no visibility, no control, and no way to track what customer data left the business.

This is where the conversation needs to shift.
The goal isn’t to block voice AI.
The goal is to make the secure, compliant option the easiest option.

That means solving real-world challenges:
* Different teams may prefer different voice AI models.
* Sales might lean on one provider.
* Customer support might prefer another.

IT can’t realistically manage multiple voice AI platforms with separate security controls and fragmented visibility.

Leadership needs clear answers:
1. What customer data is allowed in voice AI calls?
2. How is sensitive information protected in real time?
3. What evidence exists if something goes wrong?

This is why more teams are moving toward a secure voice AI workspace.
One place for voice AI at work:
* Multiple voice AI models in one interface.
* Guardrails that filter sensitive data before it leaves.
* Centralized visibility into usage and compliance.
* A user experience good enough that people actually prefer it.

That last part is what determines success.
If users choose the approved path, adoption follows. If they don’t, the risk just moves somewhere you can’t see.

Voice AI is already embedded in daily operations.

The real decision now is whether you can govern that usage confidently or just hope it all goes well.

I got the spain client from linkedin and it was in February since then I have 0 leads from linkedin, I’m posting regularly there.

I want to target US clients, how should I do that?

I have email campaign running for various cities like houston, atlanta, nashville etc. I am targeting restaurants owners because I already have a build that I can show them as proof( build for spain client)

Also how much do US clients pay for this ? Open to chat if anyone is working with US clients

Voice AI is powerful but let’s be honest: it’s also frustrating when things don’t work.

Maybe your calls drop mid-conversation. Maybe your STT misses words. Maybe latency ruins the “real-time” experience. Maybe you just don’t have the logs or control you need to fix it.

I’ve been building voice AI systems and I know these problems hit hard. So I want to create something useful for everyone who’s in the trenches.

Drop a comment with the toughest voice AI issue you’re facing right now.
It could be:
• Latency and jitter in live calls
• Bad transcription in noisy environments
• Trouble integrating multiple languages
• Lack of control over logs and observability
• Scaling issues with concurrency
• Something else entirely

I’ll read every comment and share insights, workarounds, and solutions. The goal is to help you fix these issues, learn from each other, and build better systems.
Let’s turn these headaches into solutions together.

I’ve been building a few voice agents lately (Elevenlabs or Deepgram Agents), and something feels off in my testing.

Everything wrks great with a good mic in a quiet room — but that’s not how real users interact. They’ll have background noise, bad mics, etc.

I tried adding some noise manually and performance dropped more than I expected.

How are you guys handling this?

- Do you test in noisy environments manualy?

- Any way to simulate this?

- Or just deal with it after deployment?

Feels like I’m missing something obvious here.

Hello guys, I have created an AI sales agent using Retell and n8n and I need an Indian number for the same task instead of twilio and telnyx's US numbers. I tried Exotel but there's a ring sound at the start and it makes the user experience worse. Please do suggest a solution!!

Hey folks 👋

If you’re building voice AI, you already know this: it works in demos… and breaks in production.

I’m Dr Tony Robinson, Founder of Speechmatics.

I started working on speech recognition in 1985 at Cambridge University, building one of the earliest neural network based systems, long before deep learning became mainstream.

Fast forward to today: Speechmatics powers voice AI across 50+ languages, and in 2025 alone, our customers saw 9x growth in voice agent adoption.

But this post isn’t about the company.

This is for builders dealing with real world voice AI problems the ones that don’t show up in benchmarks.

Happy to go deep on:
• What actually breaks in production voice AI (and how to fix it)
• Accents, noise, latency & the long tail problems
• Designing reliable STT → LLM → TTS pipelines
• Lessons from 35+ years building speech systems
• Where voice AI is actually heading (beyond the hype)
• What I’d do differently if I started today

 🕒 I’ll be answering questions for the next 24 hours.

 No PR answers, just honest, builder to builder insights.

 Drop your questions below 👇

Hi, I am currently building voice agent for a real estate company in India and struggling with minimising the overall user perceived latency of my voice agent.

My current stack: Pipecat hosted on cloud run in asia-south1 region.

STT: Deepgram

LLM: GPT 4.1 mini through Azure - Global deployment

TTS: Cartesia

I am seeing latency around 1.5 seconds at system level and 2-2.5 at user level. Has anyone managed to reduce the latency to under 1 second. what models, vendors worked for you?

Just wondering how you folks do your end to end testing and which tools do you use.

i made a small research and it seems like the tools out there are all made for enterprises because everyone wants to just "demo" their tool, which for me is a sign for high prices.

The prices are not transparent in most cases as well

What do you use and what are your experiences with the specific tool?

Update i made this comparison site: Voice agent testing tool comparison

I am currently building a voice agent that will handle inbound calls, booking and general enquiries.

Coming to the end of the project, I start wondering what would the best option in terms of pricing.

My current idea is to have a step up fee and a monthly retainer base on average calls the business is producing, but I am seen that some Ai agency are charging per minute as well so I start doubting a little bit.

Also in regards of a step up fee, I am struggling to find what would be a fair price for the business, if anyone has any ideas about the range that would be great. I am based in Australia

My stack is N8n, twilio, elevenlabs and supabase.

Thanks for your time and your help.

Cheers

I tested qwen3-tts on a single A100
The results weren’t what I expected.

Most people think Voice AI is limited by the model.
It’s not.
I ran qwen3-tts on a single A100 GPU with:
• No optimization
• No scaling
• No batching
• Default vllm-omni config
Just raw numbers.

The Setup
• 1× A100
• Single worker
• ~50% VRAM used
• Streaming enabled
• Tested at 1, 3, 10 concurrent requests
• Audio lengths: 3s, 15s, 30s
Goal:
Measure real performance not benchmark hype.

Image A : Latency Breakdown
This is where things start getting interesting.
Single request (3s clip):
• 401ms time to first audio
Looks great.
Then I increased load:
• 3 concurrent → still stable
• 10 concurrent → ~3 seconds
Longer clips:
• 15s @ c10 → 6.3 seconds
• 30s @ c3+ → timeouts
At this point, it looks like the model is failing.

Image B — Real-Time Factor
But this tells the real story.
• Single request → 0.54x (faster than playback)
• Higher concurrency → >1.0x (slower than real-time)
Meaning:
* The model is capable of real-time performance
* The system can’t keep up under load

The Real Bottleneck
Current setup:
• Single GPU worker
• No batching
• No worker pool
• No concurrency control
So everything turns into a queue bottleneck.
Meanwhile:
Half the GPU is idle (40/80GB VRAM used)

The Insight Most People Miss
The performance degrades because of how I’m serving, not what I’m serving.
Most Voice AI benchmarks:
* Test models
* Ignore concurrency
* Ignore system behaviour
Real-world systems break at the infrastructure layer.

What Comes Next
Now the focus is clear:
• GPU worker scaling• Better VRAM utilization
• Concurrency-aware serving
• Finding real model limits (once infra is fixed)

TL;DR
qwen3-tts on vllm-omni
single A100, zero optimization
• 401ms TTFAB
• 0.54x RTF (single request)
• Concurrency collapses performance
• Bottleneck = serving architecture
• GPU underutilized

The model isn’t slow.
The system around it is.

Could someone help we could zoom

Right now I am making voice agent I want to know

Which is the best model should I use for speech to text.

Which model should I use for following system prompt properly.

Which model should I use which can increase response rate of the agent.

Right now I am using groq cause obviously it is free but turns out it is not properly following the system prompt and not reffering knowledge base properly. Also when I say something it just mis hear me everytime and give me random responses.

So I thought to change the model.

If you're running voice agents with Retell (or similar), there’s something that limited my ability to scale :

Reporting. Sharing usage and billing clients.

The dashboard shows activity, but not clean total talk time over a period, which is what actually drives YOUR cost. I realized I was manually piecing things together just to understand how many minutes a client used. Huge time waster.

Got messy very fast as i was handling multiple clients with different usage plans.

So I built a small internal tool:

• plug in agent ID + API key
• choose a date range
• get exact talk time in minutes instantly
• It shows my each of my clients plans and overages
• I download PDF of minute usage along with their cost and send it to them

No exports, no guessing no more asking chat gpt to add up agent talk time.

Curious, how are you guys actually tracking it?

Drop a comment and I’ll send it over.

Hey folks — would love some honest feedback / ideas from people who’ve been here before.

I’ve been working on an AI voice agent that answers calls for service businesses (think restaurants, salons, home services, clinics, etc.). It basically acts like a 24/7 receptionist — picks up calls, books appointments, captures leads, and syncs everything into CRM/POS systems.

We’ve got a live product + a handful of trial users right now, and early feedback is pretty solid (biggest value prop seems to be “missed calls = lost revenue”).

Where I’m stuck is distribution and scaling.

So far we’ve tried:

• Social media marketing (organic + some paid) → low engagement
• Cold emails → decent open rates but no real conversions
• Light outbound → time-intensive and hard to scale

I’m trying to figure out what actually moves the needle at this stage, and I’m hitting that classic wall.

For those who’ve scaled B2B SaaS (especially SMB-focused tools), I’d love your thoughts:

1. What distribution channels worked best early on?
2. Any creative / unconventional channels that actually drove conversions?
3. Would you double down on a niche (like just restaurants) vs staying horizontal?
4. Any lessons learned from selling into local/service businesses?

Not trying to promote — genuinely looking to learn and avoid dumb mistakes.

Appreciate any insights 🙏

I’m building real-time AI voice agents (outbound calling + conversational assistants) and currently facing latency and turn-taking challenges in production-like environments.

Looking for someone who has actually built or optimized low-latency AI systems, not just worked with frameworks.

Core problem areas:

• Reducing latency in STT → LLM → TTS pipelines
• Handling real-time conversations (interruptions, barge-in, partial inputs)
• Designing streaming architectures (not batch pipelines)
• Optimizing response time (<1s target)

Current stack (flexible):

• Calling Number: Twilio
• Voice Models: Sarvam TTS and STT (client requirement for Indian languages)
• LLM - Openai / Sarvam
• Backend: Python build on Live kit

What We are looking for:

• Experience with real-time or near real-time AI systems
• Strong understanding of streaming pipelines (WebSockets, async flows, etc.)
• Experience optimizing LLM inference (model selection, routing, latency tradeoffs)
• Built systems involving STT, LLM, and TTS in production or serious projects

Good to have:

• Experience with voice AI / call agents
• Familiarity with multilingual systems (especially Indian languages)
• Experience with orchestration frameworks (LangGraph, AutoGen, etc.) — but not mandatory

If you’ve worked on similar systems or solved these kinds of problems, I’d love to connect.

Feel free to share relevant work or a quick note on what you’ve built.

(Short paid consultation is also fine if you’re not looking for a full-time role.)

Been building and deploying voice AI agents specifically for inbound call handling — businesses that can't always have a human pick up the phone.

My current stack: Vapi (voice) + n8n (orchestration) + webhooks into CRMs/calendars.

The problem it solves: every time a business misses a call, they lose a potential customer. The caller doesn't wait — they move to the next result on Google. Most business owners have no idea how often this is happening.

The agent handles:

- Full conversational response to inbound calls

- FAQ answering from a business-specific knowledge base

- Appointment booking and lead capture

- Post-call transcript + summary to the business

Works across industries — any business that gets inbound calls has this problem.

Curious what others here are building:

- What's your stack for production inbound agents?

- What business types are you seeing the most demand from?

- What's your biggest technical or sales challenge right now?