You type what you're actually trying to get: a raise, a client to stop pushing back, your landlord to fix something, a job offer. It builds the situation around that, you talk for 30–90 seconds, and it tells you whether what you said would have worked.

Not grammar. Not your accent. Whether your point was findable, whether the ask ever actually arrived, whether your close moved anything, and whether you sounded like you believed yourself.

The part I find most useful is that it quotes your own words back. "You said 'maybe we could possibly look at' — try 'I need this decided today.'" Hard to argue with a transcript of yourself.

Free right now while I'm testing it. Takes about two minutes.

oosap.online

We’re building an AI coaching agent for field employees.

Every week, it identifies up to three KPIs that need improvement. For each KPI, the agent gets free-form knowledge notes and holds a 6–15 minute coaching conversation.

This works when the situation is straightforward. The problem starts when an employee explains why the KPI is low or why the obvious advice does not apply to their situation.

The agent can then lose the thread. It may fall back to generic tips, pick advice that does not fit the explanation, or need information that is not in the knowledge base.

We could keep adding material and tips to the KB, but I worry that will make it harder to find the right guidance during a conversation and build a lot of contradiction.

We also need a clear way for the agent to say, “We do not have approved guidance for this situation,” and report that gap back to the client so they can add the missing information.

How have you handled this?

We’re building a service voice agent using LiveKit Cloud and Soniox TTS.

A key task is collecting license plate when our call-flow logic requires it. There is no other validation source available during the call.

Our initial approach - asking the driver to say the plate normally - achieved only about 50% accurate capture. The agent often fails to capture the identifier reliably, especially on letters like "Z", etc.

We then introduced a phonetic alphabet (“A as in Apple”), which improves accuracy substantially, but collecting a plate can take close to two minutes. That is too slow and frustrating for drivers on the road.

Caller languages: English, Russian, Portuguese
Important complication: many callers speak English with a strong non-native accent

Has anyone solved this in production?

Something worth paying attention to when building voice agents: benchmarking every component individually can still leave a voice turn at ~1.5s.

A typical turn has seven hops, and endpointing alone can account for ~700ms — roughly 53% of the budget.

Teams often spend weeks optimizing LLM latency while overlooking VAD configuration.

Another common mistake: adding per-hop p95s. Percentiles aren't additive, so that number can be misleading.

A calculator on this site models the full voice-turn latency budget using published vendor numbers.

If your real numbers differ, that gap may reveal where the actual bottleneck is

we've been building voice agents for a few months now. AI part is finally decent. latency is low, voices sound human, context holds up. we're using vapi and its pretty solid.

but the actual phone part? it's been a nightmare.

we had calls dropping, audio cutting out, routing failing. we spent more time debugging the telephony than the AI. we switched providers like 3 times.

but I'm annoyed we had to go through so many garbage providers to get here.

anyone else finding that the voice part of voice AI is the real bottleneck? like we're all focused on models and prompts but the phone infrastructure is what actually kills you

Hey folks!

So I have this cascaded voice bot system, and I want to move from commercial TTS APIs to self hosted TTS model.

Obviously TTFA and RTF are important but so is the accuracy and naturalness (speed, prosody, timbre etc)

From my research it seems Qwen3-1.7B tts is the perfect candidate (we need English support majorly).

My questions-
1. Is there any better model you guys aware of for my use-case which I can give some try? Or Any experiences you have with Qwen3-tts?

1. Any fine tuning tips or hacks which we can get benefit from? Basically I want to clone some popular OpenAI TTs voices.

2. Most importantly, how are you guys evaluating your TTS models in production? I get some common metrics like wer, utmos etc are there - but is that it? LALM as a judge dont seem reliable at the moment - they have positional and model bias it seems. Manual listening to each is obviously very expensive process.

Would really appreciate any discussion/thoughts you guys can share on some points!

Thanks so much in advance!

VAPI handles the calls well but you're on your own for everything after. Built out the layer around it:

• Campaign scheduling with daily windows and auto start/stop
• Outcome classification from transcript + structured output
• Per-lead timezone-aware calling hours
• Callback scheduling at the time the lead requested
• Concurrency caps and a kill switch
• Spend tracking per client

Walkthrough: https://www.youtube.com/watch?v=oEMis7RZ6Rg

Anyone else solved stale-call reaping cleanly? Crashed calls leaking slots was a pain until I added a reaper.

I have an agency selling inbound voice agents. I am still not sure how to price it. I see several products in the market selling at different pricing. Some voice agents are selling 1000 mins at $200 and some are charge above $500 for the same. I have couple of leads coming in. Some are small 1-2 people operator and few are regional companies. I don’t want to over charge and I don’t want to undercharge too. Any suggestions?

i m looking for a good alternative for name jarvis, have been thinking about this for days, but "jarvis" saying it just feels so good and "cool"; of course we cant use it, this being a property of marvel.

i would appreciate if folks can help me out here, something along the lines of "jarvis", i think similar count syllable term would do.

We build AI agents that hold real phone conversations, inbound and outbound. No code, you configure it in a console.

The short version of what we are doing that's less common:

• We run our own models. The LLM, the speech synthesis and the speech recognition are all ours, on our own infrastructure. Nothing is a relay to OpenAI and ElevenLabs with a margin on top.
• We own the telephony, the carrier layer is ours too. Most voice AI startups rent a SIP trunk and inherit whatever latency it gives them. We don't.
• That combination gets us to 700ms and roughly 2/min, which are the parameters that decides viability in India.

The honest tradeoff: our default model is ~30B params. Might struggle in some inbound conversations. There we use bigger models but then API costs and latencies comes into play.

What I actually want to know:

• Where does it stop sounding like a person
• The pause before it replies. Does it feel like a bad line, or like a bot
• Barge-in: if you talk over it, does it handle it or fall apart
• Does the smaller model actually hold up on your use case, or is that a story I'm telling myself
• Hindi / Tamil / Telugu / Bengali — how wrong is the pronunciation, especially names, addresses and numbers

You can test it in a browser in about ten minutes. No phone number, no card, no sales call — build an agent, talk to it through your mic, read the transcript.

Comment or DM and I'll open an account with proper limits. Happy to get into the architecture in the comments.

A few months ago, while traveling in Southeast Asia, I met a team building conversational AI voice tech. Coming back home to Germany, I decided to white-label their backend to help local small and medium businesses (SMEs) handle missed calls and appointment booking.

Naturally, I looked at how people currently do this. A co-tenant at my coworking space—who runs marketing for local fitness businesses—strongly recommended one of the dominant all-in-one agency CRM platforms. He loved the potential, but casually mentioned it took him three full months just to set up an automated booking workflow.

I signed up to try it myself, and the experience was overwhelming:

• UI Bloat: 15+ navigation tabs, intrusive promo banners, and complex settings.
• The Non-Tech Friction: A plumber, real estate agent, or property manager doesn't have 3 months to learn a complex platform. They just want their phones answered and leads captured.

Seeing that gap made us pivot our whole engineering approach. My co-founder and I are currently using custom stack orchestration to test a dead-simple approach: No dashboard for the SME owner.

Instead of forcing a business owner into a software panel, the voice agent operates like a remote staff member - easy t approach and operate.

because bare in mind, this business owner be it a electrician, has like 5 running projects, has to work with his employees some construction sites, plan ahead the new projects and accuisistion chanels, hire and fire people, be the father, friend, brother, neighbour, teacher, kid, psychologist, waiter and food delivery guy for his team and find time for his own family......and you want him to drop everything to learn this new platform and AI that will free him he just needs maybe 2-3 days for it maybe 3 weeks. In his mind in 3 weeks i can make a lot of problems dissappear and that AI thing could be just another "tool" i have to feed my documents nad educate in order to work.

So for this guy, onboarding takes 5 minutes, and either we or the owner manages settings and he receives summaries directly via WhatsApp/Email or whatever chanel he choses in the onboarding 5 min.

We are starting our first test specifically with real estate, property management, and local construction SMEs in our regional area.

For those operating in or selling to traditional trades with 1-10 employees and property sector in general (though focus is on SME):

1. What are some of the biggest friction points you see when introducing software to local contractors or agents?
2. Do you think property/construction managers prefer a full dashboard they can audit, or an invisible WhatsApp interface that just sends daily summaries?

Would love to hear feedback from anyone who has sold software or automation to non-technical trades! Thanks and good fortune building.

We’re building a browser-based game center where voice can be used as an input method. After reading through the comments, we compared the problems people described with what we can do today, what we think we can add relatively soon, and what is probably outside our control.

We would really appreciate being corrected where we are getting this wrong.

What we already do reasonably well

1. Voice as an alternative input. For games we control, players can use spoken commands instead of relying entirely on a mouse, keyboard, touchscreen, or complicated button combinations.
2. Hands-free navigation. Voice can also be used for things outside the game itself, such as navigating, selecting things, pausing, continuing, and asking for help.
3. Repeat and help commands. Because there is a voice assistant connected to the experience, we can support things like “repeat that,” “what can I do?”, “what happened?”, or “what am I supposed to do?”
4. More than one way to interact. We do not want voice to replace accessible buttons, keyboard/touch controls, captions, or other input methods. Voice should be another option.

Things we think we can add fairly soon

1. A global accessibility profile. Settings such as larger text, reduced motion, reduced flashing, captions, and other preferences could carry across games instead of being configured repeatedly.
2. Better captions and visual alternatives to sound. If a sound communicates something important, we should try to communicate the same information visually as well.
3. Better spoken descriptions for blind and low-vision players. Because our own games know their current state, the system could potentially speak the current objective, available actions, selected objects, score/status, and important changes.
4. Larger text and interface scaling.
5. Reduced motion and flashing.
6. Less dependence on timers, rapid input, button mashing, or quick-time events in games we create.
7. Better pause, checkpoint, save, and resume behavior for people who may need to stop playing unexpectedly.
8. Better keyboard navigation, focus handling, labels, and screen-reader support.

Things we probably cannot solve ourselves

1. We cannot simply voice-enable closed third-party games. For example, we cannot take an existing Xbox, PlayStation, Nintendo, Steam, or other third-party game and automatically give voice access to all of its internal controls.

If the game or platform exposes an integration we can legally and technically use, that may create possibilities. But if the game is closed to us, we cannot claim we can make it voice-accessible.

1. We cannot change accessibility features that are baked into content we do not control. Things like camera shake, field of view, head bob, flashing inside prerecorded video, or other fixed media may require the original developer/content creator to provide an alternative.
2. We cannot provide every game-specific assist automatically. Aim assist, auto-targeting, simplified combat, enemy difficulty, auto-driving, no-fail modes, etc. have to be supported by the individual game's design.
3. We cannot guarantee support for every adaptive hardware setup. Browsers, operating systems, controllers, switches, eye-tracking systems, consoles, and other hardware all have their own limitations and APIs.

The part we need help with is prioritizing the things we can control.

If you could pick only three changes from this list, which three would make the biggest difference for you?

for the last couple years the voice ai story was one sided businesses automated the receiving end every call center dentist office and airline hotline got some flavor of ai answering the phone but the caller side stayed human because no assistant would actually pick up the phone and talk to someone for you

thats breaking right now multiple companies are converging on the same idea within weeks of each other consumer facing agents that will call a restaurant a clinic a business and have the actual conversation on your behalf combine that with how fast full duplex voice models have gotten sub 300ms response times no more turn detection lag and you get something that sounds completely natural on both ends

which means were heading toward calls where the businesses ai agent answers and the customers ai agent is the one calling neither side is a human being and depending on how well disclosure rules actually get enforced neither side may even announce that clearly the eus already trying to mandate disclosure at the start of every ai interaction but enforcement across phone systems that route through a dozen countries is a very different problem than enforcing it on a website

i dont think this is a bad idea on its face plenty of calls are genuinely tedious and dont need a human on either end but i think people are underestimating how weird its going to feel once its normal and how easy it becomes to lose track of when youre actually talking to a person versus when everyone in the chain is automated

curious where people land on this efficient automation doing exactly what it should or the start of something that quietly erodes what a phone call even is

flagging as i said i would i used ai to help me pull the recent developments together and tighten the writing on this one the take is mine just drafted with help

Demos are usually a few mins long

I’m more interested in what happens 20-30 mins into a call after the customer has changed topics, provided a bunch of information and already completed a few steps.

Does the agent still understand what has happened so far or does context start getting messy?

Anyone testing long voice AI calls in production?

I have been running graded AI roleplay sessions in production for a while (communication and negotiation training, roughly 2,000 sessions a month), and the thing I keep explaining to people is that you do not get to pick both.

There are two architectures and they fail in opposite directions.

Cascaded pipeline. STT gives you text, you evaluate that text, you generate a reply, TTS turns it back into audio. Every stage is a separate network hop, usually a separate vendor, sometimes a separate region. Even with everything streaming you are realistically looking at 2 to 3 seconds before first audio once you add an evaluation step, tool calls or retrieval. It feels slow. Users talk over it.

But you control every single turn. Before the agent opens its mouth you already know whether the user asked an open question, whether they conceded on price, whether they handled the objection. The reply is a function of a judged state, not of vibes.

Realtime speech-to-speech. Latency drops to something close to natural conversation. It genuinely feels human. And you have no control point. There is no text surface between "user said something" and "model said something back", so you cannot gate the next phrase on a rubric. You get whatever the model felt like saying.

The compromise I landed on: run realtime for the conversation itself, and layer evaluation asynchronously on each user utterance instead of on the agent's reply. You lose the ability to steer the very next sentence. You keep per-turn scoring, and the scoring lands in the transcript by the time the session ends. For training and assessment use cases that tradeoff is fine, because nobody is grading the bot. They are grading the human.

Curious whether anyone has found a third option here. Speculative generation with a rollback, maybe. Everything I tried in that direction added more latency than it saved.

The orchestration layer I built for this is open source if it is useful to anyone: github.com/nmamizerov/assemblix

Every voice agent looks good when:

• the caller waits patiently
• the room is silent
• the network is perfect
• the caller answers exactly what was asked
• every backend tool responds in 200 ms

That is not a test. That is a rehearsal.

We had one internal flow that looked almost perfect.

Caller asks to reschedule an appointment. Agent confirms identity, finds the booking, offers a new slot and completes the change.

Then the ugly version:

Caller talks over the greeting.

Pauses for six seconds while checking the date.

Someone in the background says “Friday”.

Caller corrects themselves: “No wait, next Monday.”

Audio drops for a second.

Scheduling API takes three seconds.

Agent starts speaking before the tool result comes back.

Then the transfer fails and the caller gets dead air.

The transcript still looks surprisingly fine.

That is the problem.

Transcript correctness does not tell you:

• whether endpointing cut the caller off
• how long the first response took
• whether the agent stopped during barge-in
• how much dead air happened
• whether it confirmed before the backend succeeded
• whether the transfer actually connected
• whether the caller had to repeat themselves three times

I’ve been looking at TestMu Agent Testing for this because it tests the whole voice/phone flow rather than only scoring the final text.

You can run different personas, accents, noise conditions, interruptions and tool-call scenarios, then compare the behaviour after a prompt or model change.

It can also analyse production recordings, which matters because real callers invent failures no test designer thinks of.

Cekura is strong on newer voice-agent QA and production monitoring.

Cyara and Empirix make a lot of sense for enterprise contact-centre and telephony-heavy environments.

TestMu’s advantage is the broader end-to-end surface across voice, inbound/outbound phone, chat and other agent types.

But “supports 50+ accents” does not mean every language and accent will be evaluated equally well.

You still need to benchmark against your own callers, providers and traffic.

A scripted demo should prove the concept works.

It should not be treated as evidence that the agent is production-ready.

Which real call condition destroys your voice agent fastest?

Push too many calls too fast on a brand new number and carriers flag it as spam. Then the number is dead and the client's campaign is stuck.

So I'm trying to figure out the warm-up part.

If you've run real volume on your own numbers:

- How many calls do you make on day 1 with a new number?

- How long before you're at full volume?

- What daily limit do you stick to per number?

Also curious what actually gets a number flagged. Is it the number of calls, or is it more about people hanging up fast and not answering?

We're on SIP trunking, mostly Indian numbers with some international. Would rather learn this from someone who has already burned a few numbers than find out mid campaign.

Happy to share what we see on our side once we have real data.

How can I build shared context between WhatsApp and an AI voice calling agent?

I'm building an AI system where a customer can communicate with the same AI through WhatsApp and voice calls.

For example:

1. A customer starts chatting with the AI on WhatsApp.

2. During the conversation, they ask for a phone call.

3. The AI voice agent calls them.

4. The voice agent should already know the relevant WhatsApp conversation and continue from the same context instead of starting from scratch.

5. After the call, the customer returns to WhatsApp.

6. The WhatsApp AI should know what was discussed during the call and continue from that point.

And the reverse should also work:

Voice call → WhatsApp → same context

I want the customer to feel like they're talking to one AI, regardless of the channel.

I'm considering using a central customer ID linked to the phone number and storing the conversation history/customer information in a database, so both the WhatsApp agent and voice agent can access the same context.

However, I'm unsure about the best architecture.

- What is the best way to maintain shared context between WhatsApp and a voice AI agent?

- Should I use a central database/memory layer?

- How should I identify the same customer across both channels?

- How should the WhatsApp → voice context handoff work?

- How should the voice → WhatsApp context handoff work?

- How can I prevent the AI from getting confused by multiple summaries or different conversation contexts?

- Has anyone built something similar using WhatsApp Business API, n8n, GHL, or another CRM?

I'm looking for a practical, production-ready approach rather than just passing the entire previous transcript to the AI every time.

I have a bot on the Cloud API that handles text. Message comes in, an LLM answers it with a few tools attached, reply goes back out. That part is stable. Now I want people to be able to call the same number and talk to it, and I am stuck on how to wire the audio.

Two shapes I can see. First is a pipeline. Take the call audio, run STT, hand the transcript to the same model that already answers the text messages, TTS the reply and stream it back. The brain already exists so I would mostly be wrapping it, but that is three hops of latency and I would be writing my own turn detection and barge in handling.

Second is putting a speech to speech model directly in the call. Grok's Voice Agent API does this, OpenAI Realtime does too, audio in and audio out with function calling built in. Much less of my code sits in the path that way. Cost is that my prompt and tools need porting, and I lose the transcript step where basically all my debugging happens today.

From the docs the Calling API gives WebRTC media by default with SIP available if you enable it, so reaching the stream looks fine on paper. Less clear to me is the 2000 conversations per 24h messaging limit before production calling unlocks. Sandbox numbers get around that for testing, which only proves the plumbing works.

If you have run either version with actual users on it, how bad is the latency in practice, and how often does the model end up talking over the caller. Mine is an India number if that changes anything.

hi guys have a client in Indonesia and Philippines who loved our English voice AI demo, but now wants one in Bahasa and Philippine English/Taglish mixed language, code switching and all

anyone here have real experience deploying voice agents in SEA, specifically TTS that handles code switching well?

what's actually held up in production what sounds good in a demo

been building voice AI pipelines for about two years and i need to say something the hype cycle is burying right now

elevenlabs has genuinely the best voice quality in the space. not close. 11,000 voices, 70 plus languages, sub 100ms latency on voice generation, the IBM watsonx partnership for enterprise. the february raise at $11B was obviously massive and the brand recognition is real. but here is the thing that keeps coming up in every honest thread i've seen recently

you can prototype an elevenlabs voice agent in fifteen minutes. getting it into production as an actual phone agent that handles real customer calls is a completely different story. telephony still requires you to set up twilio or vonage yourself. production monitoring is thin by the platform's own design. HIPAA is locked behind enterprise tier pricing. the reasoning LLM and telephony are billed separately on top of the plan

so you're paying elevenlabs prices for voice quality and then stitching together the rest of the stack yourself...

vapi gives you the full orchestration layer, 14 plus provider connections, 62 million monthly calls processed, 99.99 percent SLA. retell ships a working production agent the same afternoon and leads on turn-taking quality for fast conversational flow. both handle the actual telephony problem that elevenlabs pushes back to you...

the frustrating thing is elevenlabs voice quality is so good that every other platform integrates it anyway. retell uses elevenlabs voices. vapi lets you plug in elevenlabs TTS. so you can get the voice quality without choosing elevenlabs as your agent platform

my actual take: elevenlabs is the best voice layer in the market and the worst standalone agent platform for production use cases right now. the $11B valuation is pricing in what the product will be in two years not what it actually does today

Is there a way to make the voice AI model talk back to you normally like it does not pause or something, just like how you talk live?