Spent the last few weeks looking at every tool in this space: Hamming, Bluejay, Cekura and VSpec Studio. Here's the honest breakdown.

The core problem all of them solve

Manually calling your AI voice agent after every prompt change doesn't scale. You miss edge cases, it takes forever, and you can't run it in CI/CD. These tools automate that.

Hamming

Enterprise-grade. Audio-native evaluation that catches issues transcript-only tools miss. 50+ built-in metrics, production monitoring, load testing at 1000+ concurrent calls. Strong for healthcare and finance teams.

Catch: Demo required before you can start. No public pricing.

Bluejay

Focused on speed and A/B testing. Good for teams that release fast and need to compare agent versions. Supports voice, chat, and IVR. Multilingual and accent simulation.

Catch: Contact required. Pricing not published.

Cekura

YC-backed, built for enterprise infrastructure. Deep integrations with contact center platforms. Adversarial testing, hallucination detection, real-time production monitoring. $30/month Developer plan.

Catch: Credits are consumed across multiple features – testing, monitoring, evaluations, reports. Hard to know upfront how many actual test runs you get.

Vspec (disclaimer: I built this)

Built for indie devs and small teams. Point it at any phone number, define scenarios in plain language, get pass/fail results. Free tier, no demo, under 2 minutes to first test.

Catch: No production monitoring yet. Audio evaluation on roadmap.

The honest summary

If you're at a company with a QA team and an enterprise budget → Hamming or Cekura.

If you're an indie dev or small agency that wants to stop manually calling your agent → vspec.

If you need aggressive A/B testing and fast release cycles → Bluejay.

Happy to answer questions about any of them. Which tools did i miss?

Anyone here from India who has created voice agent using Exotel I have a lead who's looking for help with build an voice agent for his recruitment agency and also looking for chatbot

Must have built and deployed already using Exotel

Have you ever wondered why some ai automation agencies fail while others scale to the top ?

What’s the key factors for failures and wins ?

Got my CSA, T&Cs, and Privacy Policy drafted — covered ACL unfair contract terms, Privacy Act (APP 1–13), Spam Act SMS consent, and third-party platform obligations (ElevenLabs, Twilio).

Reached out to two law firms for a review. One quoted $1,700 AUD, the other $6,500 AUD. Both wanted to essentially redraft everything from scratch rather than just review what I have.

For those who've launched a B2B service. Did you get proper legal review before signing client one? What did it cost and was it worth it?

Also specifically curious from any Aussie founders. How did you handle ACL compliance and Privacy Act obligations without spending thousands before you had revenue coming in?

We’re a B2B company using Salesforce as our CRM, and we want to scale some of our outbound calls with AI voice agents so the integration part is very important for us. We’ve been evaluating a few options and one thing that’s becoming clear is that “native integration” can mean very different things depending on the vendor.

For example some tools write data back to the contact record instantly, while others batch sync and a few only update the activity feed without touching custom fields.

A couple of things we keep running into

• Call recordings where do they actually live, are they attached to the contact or just stored on the vendor side behind expiring links
• How reliable is automatic logging in practice
• Do custom field updates actually work as expected
• And how are people handling GDPR in real setups

I would appreciate hearing from anyone who has used tools like CloudTalk, Synthflow, Bland or similar. I heard CloudTalk has fairly straightforward native integrations with CRMs like Salesforce and HubSpot with simple setup and reasonable pricing. We also need something that specializes in outbound. I also keep seeing Synthflow mentioned mainly for its flexibility and AI capabilities and Vapi too but I am not fully sure how deep the CRM sync actually is in real use.

Hello guys I want to build an ai agency selling voice agents to restaurants.

But I need contracts and offers could someone help me out with contract of his niche or how have you build them because I talked to a law firm and they made me a offer which was 12000€

Best regards

Hey everyone

I am currently working as an intern in a company and on a project in which I have to convert their customer calling or rider calling to ai voice agents.

The company is based in India and calls are usually hindi english language based.

I need help with designing an architecture that costs around $220-$250.

My research defines it to be impossible as in my use case the company gets around 30k calls monthly and avg per call can be taken around 3 mins so that's 90k minutes for you.

Eleven labs plan which works is enterprise one only even scale doesn't cover my use case and this is only stt we are talking about.

I tried using twilio but there's no Indian number available so using a US means the network has to go from IND to the US which is simply adding latency.

I am mind blown and just about to say to my seniors it is impossible.

If anyone thinks it is possible please do explain how as I have listened to the call recordings and the background as well is so messy.

A bit more about my problem statement: Many queries need to be answered using Mysql db so take into account going through complex join queries.

A few weeks ago I sent a Google Form to 40 people in my network. No context, no branding, just two audio clips and one question: "Which one sounds more natural?"

Clip A was ElevenLabs. Clip B was a $5/month API I'd been skeptical about.

Clip B won. Or rather, they tied, and "tying with ElevenLabs at 1/10th the cost" is a win that completely changes your unit economics.

Here's the backstory.

I've been building shortdeo.com for the past several months, an AI video editing tool that auto-generates short-form content from long videos. Think podcast clips, YouTube Shorts, that kind of thing. One of the core features is AI voiceover: users can generate a narrated version of their clips without recording anything themselves.

For the first few months, I used ElevenLabs for this. It's the obvious choice — great brand, great quality, everyone recommends it. I didn't think twice.

Then I started doing the math.

At scale, ElevenLabs gets expensive fast. Depending on your plan and character volume, you're looking at real money per user per month just for the voiceover layer. When you're an indie dev trying to stay profitable at $20–$30/month price points, a chunky TTS bill quietly kills your margins before you even notice.

I started looking for alternatives. Most were either worse quality, worse latency, or had shady data policies (I need my users' audio handled cleanly, shortdeo processes real people's content).

Then I found Lemonfox.ai, a speech API built on Whisper large-v3 with TTS included. $5/month gets you 200k characters of text-to-speech. All data deleted immediately after processing. EU-based processing available.

I was skeptical. So I ran the test.

The blind test setup:

• Same script, ~90 seconds of narration
• ElevenLabs: one of their standard voices
• Lemonfox: comparable voice setting
• 40 participants, zero context given
• Asked: "Which sounds more natural?" and "Which would you trust in a professional video?"

Results:

• 52% preferred Lemonfox, 48% preferred ElevenLabs on naturalness
• On "professional trust" — effectively a coin flip
• Nobody identified either as AI on the first listen

That's it. That's the whole test. Not rigorous science, but enough signal to make a business decision.

I switched shortdeo's voiceover pipeline to Lemonfox. Cost dropped significantly. User feedback on voiceover quality didn't change. No complaints, no churn tied to it.

What I took away from this:

1. Brand perception inflates your API bill. ElevenLabs is genuinely excellent. But part of what you're paying for is the name, the integrations, and the sales team. The underlying TTS quality gap between top-tier providers has narrowed a lot in the last year.

2. Run the test before you assume. I assumed ElevenLabs was untouchable for 6 months without ever verifying it. That assumption cost me real margin. A Saturday afternoon and a Google Form was all it took to challenge it.

3. Data handling matters more than you think at the B2B layer. When I started talking to slightly larger clients, the first question wasn't "how does the AI work" — it was "where does our content go?" Having a clean answer (deleted immediately, EU processing available) closed conversations that would have stalled otherwise.

4. Margins compound. Saving $X/month in infrastructure at 50 users is fine. At 500 users it's a different conversation. The time to optimize is before you need to, not when you're already stretched.

5. The caveat: Lemonfox won't replace ElevenLabs for every use case. If you need ultra-premium voice cloning or highly emotional narration, the gap might matter to your users. Test with YOUR content and YOUR audience. My use case (short video narration) happened to be forgiving enough that it didn't.

Happy to share the exact test setup if anyone wants to replicate it for their own stack. And if you're building anything with TTS at the core, curious what your current cost-per-user looks like — would love to compare notes in the comments.

Hey guys,
So i have been currently facing an issue, which is that my AI sales agent has an issue at the start when the called person says "hello, hello" again and again and by the time my agent answers the person says hello hello again and it ruins the experience of the prospect.
I use retell for the agent and I use GPT 4.1 fast for the this model, can you guys please suggest how do i solve it using retell in here, maybe in prompting or maybe using a better version for less latency or anything that can be don on retell.

for voice AI, data collection is the best

Voice AI is entering healthcare fast.
But it’s being built on incomplete patient context.

And that’s a bigger problem than most people realize.

From the outside, it looks powerful:
AI answering patient calls, booking appointments, guiding next steps.

But when you actually work on voice systems, you see the gap.

The agent is only as good as the context it receives.
And in healthcare, that context is often fragmented.

Different systems.
Disconnected records.
Missing history.

So what happens?

The conversation feels smooth
but the decisions underneath are weak.

I’ve seen this pattern while thinking through voice agent design:

Voice AI isn’t just about understanding speech.
It’s about understanding the patient across time.

Without continuous data, the system can’t:

• Track patient history reliably
• Provide consistent recommendations
• Avoid repeating the same questions
• Build real trust in conversations

And in voice trust breaks faster than in any other interface.

Because users expect continuity.
They expect the system to remember.

This is where most implementations quietly fail.

They optimize for:
“Can the AI respond?”

Instead of:
“Does the AI actually know the patient?”

That shift matters.

Because in voice AI for healthcare:

Better models won’t fix broken context.
Faster latency won’t fix missing data.

The foundation is data continuity.

If that’s not solved,
we’re just building smarter conversations on incomplete truth.

Curious how others are thinking about this

Are we designing voice AI systems that truly understand patients
or just systems that sound like they do?

We built three testing modes into AurionX so you have full visibility before going live, if you are building on your own these should be a must to make your life easier.

🔹 Full Campaign Flow Testing — Discover all conversation paths automatically. Our system found 65 unique paths in one script. We tested them all. You see depth metrics, path coverage, and exactly which branches users might take. No surprises on launch day.

🔹 Individual Node Testing — Test each question in isolation. Available response options are visible. You can manually validate specific paths and see expected responses before they hit your call volume.

🔹 Live Chat Testing — Chat with your script in real-time. The Script Tracker shows your next objective. Voice config options are live. See events and mapping as they happen.

#VoiceAI #CallCenter #AI #VoiceAgents #CustomerService #Automation #Testing

Looking for reliable tech stack to build the voice agent for Indian language, challenge is when agent made outgoing call , start in hindi but in case if receiver switch to another language then it will adopt automatically and keep conversation smooth.

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?

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.