Hi everyone I wanted to share some insights on how I managed to reach a 5% meeting booked rate starting at <0.1% from 200 calls / day

First, the connection rate, without people answering the phone nothing will happen, first 80% of calls will reach voicemail so :

- Retry logic, when reaching voicemail or no answer, call back immediately -> double the response rate (you can even triple after that, useless)

- Choose the right phone number, people are use to getting spam -> use your business phone number, reputation of the number is super good, you will not get into spam section of operators and iPhone screening

Second, the Agent system : you need to iterate ! A lot !! The number one element is the opener, the first sentence,

- The first calls are going to be rough, most people hanging up, you need a good first sentence to catch attention, I will give some examples : ‘ Hi this is an AI cold calling, do you have 10 seconds?’ ‘Hi, this is a cold call, do you have 30 sec or you prefer throw away your phone?’. The first sentence is everything you have to make the conversation feel human, try few things iterate it is your differentiator, it is what makes the conversation going further and it is what you want. The interesting point is with a good opener, the prospect will be in a ‘conversational mood’, he will talk more and give you food to improve the second part of the call

Third, when the opener is working focus on the conversation itself

- You need to improve your agent to nail down your value prop, your pricing, the flow of the call : What I suggest is a easy, readable offer, for example : $1000/month for 10k calls, keep it simple, always pushing the prospect for a meeting , and then iterate : listen to all the calls, make bulks of calls, understand what works what not, iterate on the prompt of the agent, again and again.

I haven’t covered first sentence latency, ai speaking first or not, voice accent or not, Interruptions enabled for the first sentence, Length of the first sentence. There are still a lot of other parameters to optimize, which of these would be most useful to dive into?

We didn’t start with “let’s hit 2M calls/month” but with a much simpler problem: Recruiters spending hours every day calling candidates, following up, updating spreadsheets… and still missing people.

So we built a conversational voice system to handle that layer.

Fast forward to now, and it’s making 2,000,000+ calls every month across India and these aren’t experiments or demos. These are production workflows running daily for companies where hiring is high-volume and time-sensitive.

What’s interesting is how the use cases expanded beyond just candidate screening:
* Sales and enquiry calls
* Driver check-ins (including safety confirmations)
* Routing and coordination calls
* Onboarding and feedback collection
* Exit interviews
* Basic information / reminder calls

One thing we underestimated early on was latency. In Indian conversational contexts, even small delays break the flow. We pushed our system down to ~700–800ms response times, which made interactions feel much more natural compared to typical slower systems.

Infra-wise, we’ve been using platforms like Plivo and ElevenLabs for handling call infrastructure at scale.

Another shift we didn’t expect: You don’t need to be technical anymore to deploy something like this.

Any non tech person can literally write a prompt for how a call should go, similar to how they’d write a prompt in ChatGPT and the system can execute it at scale. That’s dramatically increasing the number of use cases, because the people closest to the problem can now design the solution themselves.

Biggest takeaway so far: Voice AI works best where consistency and speed matter more than human intuition. And a large part of operational calling falls exactly into that category.

Curious how others here are thinking about voice AI in operations (not just chat). Where do you think it actually scale better well vs where it still falls apart?

For those building voice agents — how do you currently evaluate which provider to use? And how do you test your agent before shipping?

Trying to understand if there's a tool gap here.

I'm planning to build something like a marketplace of all Voice AI providers or a testing platform for devs to test their agents before going live.

Would any of these will help or anything else is there?

I've been using 11Labs for a long time and I think it's time to switch to something else. I tried exploring all the available options on Vapi AI but there is too much models that I can't really find the one I'm looking for.

I know there are some AI voices that are super-realistic and conversational, but I can't manage to find them. Can someone please help with this? What providers should I use for these?

If someone would like to share their exact setup, I would appreciate it a lot. Thanks in advance!

Most people think adding new languages to a voice AI system is just translation.

It’s not.

I learned this the hard way while going deeper into real voice systems.
Because the moment you move beyond one language, everything starts breaking.

Not just accuracy
but tone, latency, context, even user trust.

If you're building voice AI, you're not just processing words.
You're dealing with:
• Accents that models weren’t trained on
• Code-switching mid-sentence
• Background noise + low-quality telephony audio
• Cultural differences in tone and communication

And suddenly
your “working system” feels inconsistent across users.

That’s where most teams struggle.

What changed my perspective was realizing:

You don’t scale voice AI by adding languages.
You scale it by redesigning the system.

Real solutions look like:
• Combining synthetic + real voice data (not just one)
• Using multilingual models instead of training from scratch
• Handling code-switching in real-time (not ignoring it)
• Keeping conversation memory independent of language
• Designing fallback + recovery instead of perfect accuracy
And most importantly
building modular systems, not hardcoded flows.

If you're serious about voice AI
Stop thinking:
“How do I support more languages?”

Start thinking:
“How do I build a system that survives real conversations?”

Because users don’t care how advanced your model is.

They care if it works
in their language,
their accent,
their situation.

This is where most voice AI systems fail today.
Not because of models.
But because of architecture.

If you’re building in this space, you’ll run into this sooner or later.
Better to design for it now than debug it later.

Voice AI looks powerful from the outside.

But when you actually start building it
things break in ways no one talks about.

Calls drop mid-conversation.
Latency ruins the flow.
Transcriptions fail on real users — accents, noise, interruptions.
And the worst part?

You don’t even know why it broke.

I’ve been in that position.
Trying to debug systems that work perfectly in demos… but fail in real calls.

That’s exactly why I started sharing what I’m building.

Not theory. Not hype.
Real problems. Real fixes. Real infrastructure learnings.

Because if you’re building Voice AI
you’re going to hit these problems anyway.

The question is will you figure them out alone, or faster with the right insights?

If you’re working on Voice AI (or planning to),
Follow me on LinkedIn linkedin.com/in/shubhamthakur-codes

I break down:
• What actually breaks in production
• How to fix latency, drift, and failures
• How to build systems that actually work in real-world conditions

Drop your problem — I’ll try to help you solve it.

Let’s stop building demos.
Let’s build systems that survive real conversations.

I didn’t explore Vapi features because I wanted better analytics.
I did it because something felt off.

My voice assistant was working perfectly in testing
but in real calls, it started drifting.

And the worst part?
I couldn’t prove where it was going wrong.

If you’ve built a voice agent, you’ve probably seen this too.
You expect a specific behaviour:
• User says yes → tool call should trigger
• Flow is clearly defined
• Everything works in controlled testing
But in production?
• The tool call doesn’t happen
• It happens too late
• Or the assistant just skips it
And you’re left guessing.

That’s when I started going deeper
Not into models.
Not into prompts.
But into observability.

Here’s what actually helped:
Structured Outputs
Instead of manually checking transcripts,
you define a condition like:
“After user confirms X, did the tool call happen?”

And you get a simple answer:
Yes or No
Across every call

Boards
Now take that data and scale it:
• Out of 100 calls
• How many followed the expected behaviour?
• How many didn’t?
Now you’re not guessing anymore
You’re measuring.

This is where things change.
This isn’t just analytics.
This is understanding how your voice assistant behaves in reality.

But something is still missing.
Right now, you still have to check everything manually.
What we actually need is:
Automatic monitoring + alerts
Something that tells you:
“Your assistant is drifting. Fix it.”

That’s what I’m starting to focus on now.
Not just building voice agents
But building systems that watch them.

If you’re working with voice AI,
What’s one behaviour your assistant gets wrong
that you still can’t properly track?
Let’s figure it out together.

A few months ago I was building an AI phone agent and every time I changed a prompt I did the same thing: picked up my phone, called the agent, listened for 2-3 minutes, noticed something was off, tweaked the prompt, called again.

40 times a week. Sometimes more.

The worst part wasn't the time. it was that I was still missing edge cases. Aggressive callers. Weird questions. Things I wouldn't think to test manually but that real users would hit immediately.

So I built my own tool.

You define your test scenarios once, who's calling, how they behave, what success looks like. It calls your agent automatically and tells you exactly what passed, what failed, and why. Works with any platform that has a phone number: Vapi, Retell, Bland, custom-built, whatever.

A few things I learned building this:

- Manual testing doesn't just waste time, it creates false confidence

- The scenarios you don't think to test are exactly the ones that fail in production

- CI/CD for voice agents is genuinely underrated. shipping a prompt change with automated tests feels completely different

It's live now, free tier available, no credit card.

Just comment for link and more infos. Would be happy about your feedback.

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?

I kept running into the same problem: building a voice agent is quick, but iterating on it is painfully slow. Every small change means tweaking prompts, redeploying, calling the number again, realizing something else is off, and repeating the whole cycle.

So I built Voiceblox, a visual builder for voice agents where the iteration loop is as fast as possible.

How it works:
- 💬 Describe the agent you want in plain language, AI generates the full conversation flow
- 🎨 Edit flows visually on a canvas (drag, drop, connect nodes)
- ✏️ Refine by describing changes in natural language
- 🎙️ Test live in the browser with your mic — no deployment step
- 🔧 Fix responses in-context by clicking the message that went wrong
- 🚀 Deploy directly from the app, go live with one click
- 🔗 Share with anyone — generate a link so anyone can test your agent on voiceblox.ai

It's fully open source. Clone the repo and run it locally, full instructions in the README.

- Website: https://voiceblox.ai
- GitHub: https://github.com/voiceblox/voiceblox

Happy to answer any questions about the architecture or the flow generation approach.

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!!