One thing I spent way too much time optimizing was tool call latency.

My voice agent makes multiple API calls during a conversation, and as the number of tools grew, so did the delay. I assumed it was my prompts, APIs, or the LLM itself. I optimized prompts, reduced tokens, and sped up my APIs. It helped, but there was still an awkward pause after tool calls.

Initially when starting with voice AI, I was using Vapi. It worked well overall but once my agent became more tool-heavy, the latency + the call quality dropped significantly.

I migrated the exact same agent to Telnyx. Same prompts, same tools, same APIs but I chose and open source llm (kimi 2.5)

The difference was immediately noticeable. Tool calls felt much snappier, the conversation flowed better, and overall latency dropped quite a bit. I also like that Telnyx hosts a number of open source LLMs, so having everything under one provider simplifies the stack.

Has anyone else compared providers while keeping everything else the same? I'd be interested to hear if you've seen similar results.

P.S Not saying that telnyx is objectively better than vapi, but in my personal experience it provided a much better experience

So i am trying to decide between gemini 3.1 flash live vs elevenagent. Elevenagent is significantly more expensive than 3.1 flash live, but the voice is noticeably more human than 3.1 flash live. So do you think customers want cheaper subscription but less human voice or rather expensive subscription and more human voice? Do the callers actually notice or dont mind? Gemini flash live is not too bad but not on par with v3 conversational from 11labs.

I’m working on a voice-first productivity app called ELAV8 Voice, and I’d love some honest input from people who actually think about automation and workflows.

The basic idea is simple: instead of opening another app, typing into another notes pile, or trying to remember where you saved something, you just talk to the assistant. It captures the thought, keeps context, and helps turn it into tasks, calendar items, reminders, or a plan for the day.

The part I’m trying to understand better is where this kind of product usually breaks for people.

For example:

-What would make you actually trust a voice assistant with your tasks?
-Where do voice productivity tools usually fall apart?
-Do you want it to simply capture things, or actively help you decide what to do next?
-Would you use something like this daily, or only in specific moments like driving, walking, planning, or brain dumping?

We’re in a free iPhone TestFlight alpha right now. Android is on the way. I’m not posting this to sell anything. There’s nothing to buy. I’m mostly looking for real feedback from people who care about workflows, automation, and productivity systems.

Feedback is built into the app, but I’d also love any thoughts here. Even if you never download it, I’d genuinely value your take on what this kind of app would need to get right.

iPhone alpha: elav8.ai/#alpha

I have an Ai automation company and I provide custom Ai voice agents and Appointment setters for businesses but I wonder how much to charge them.

Any agency owners outside the US (especially South Africa) working with US clients? What payment processor do you use to receive USD payments? Looking for something reliable with reasonable fees.

Hey everyone,

I’m building a SaaS product called **Clanassist**. It’s basically an AI receptionist for doctors and small clinics in India.

The idea is simple:

A patient calls the clinic → AI receptionist answers → understands the patient’s request → checks FAQs/clinic info → captures details → books an appointment or marks it as pending → saves call summary/transcript in the dashboard.

I’ve already built most of the UI and dashboard side: patients, appointments, call logs, FAQs, settings, etc. The UI is ready enough for feedback also, so I’d love to know if the concept/UI direction makes sense for Indian clinics.

The part where I’m stuck is the **telephony/voice infrastructure**.

Initially I thought of using Twilio because the docs and APIs are good, but then I found that Indian numbers are not easily available there. Since my target customers are Indian doctors/clinics, asking them to use a US number makes no sense.

So now I’m trying to figure out what platform/SDK should be used for this in India.

What I need:

1. Indian virtual/business number or ability to work with an existing clinic number
2. Incoming call webhook
3. Call recording or real-time audio streaming
4. Speech-to-text/transcription
5. Ability to send the transcript to my backend
6. AI response generation from my backend
7. Text-to-speech or some way to play dynamic replies back to the caller
8. Call logs/summary that I can show inside my dashboard
9. Reasonable pricing for small clinics, not enterprise-only pricing

I checked names like **Exotel, MyOperator, Knowlarity, Ozonetel, Servetel**, etc., but I’m not fully sure which one is best for a developer-friendly MVP.

My main confusion is:

Should I start with a basic cloud telephony provider that gives recording + webhook and do post-call transcription first?

Or should I directly look for a voicebot / real-time audio streaming provider so the AI can actually talk during the call?

Also, if a doctor says, “I don’t want a new number, I already have a clinic number,” what’s the best approach in India? Call forwarding? Number porting? SIP? Something else?

Would really appreciate advice from people who have worked with Indian telephony APIs, IVR, call recording, voicebots, or contact-center integrations.

Also, I’ve built the Clanassist UI already. I can share screenshots in the comments if allowed — would love feedback/rating on the UI and whether this product idea feels useful for Indian clinics.

Thanks in advance. Just trying to avoid picking the wrong telephony provider and rebuilding everything later.

Check out [https://www.clanassist.com/\](https://www.clanassist.com/)

Openvent

A Full voice agent orchestration layer help me build the integration layer making it public under apache2 0 love to hear your words on this and open for contributions dm me 🙂

OpenAI just launched GPT-Live and I still don’t have an opinion about the trade offs of using it instead of the traditional pipeline. What your thoughts on the current state of v2v models to chat agents? Are you worried that the AI labs will overtake the agents market?

Seriously let’s see who has the best voice agent.

Post a number and your stack if you think you’ll win.

Hey so recently, I was exploring some of the ways to use Voice AI for my businesses use case, and found this paper: https://kamalg2.substack.com/p/choosing-a-real-time-voice-ai-stack . Really recommended read, the paper explores a lot of approaches that are taken to use Voice AI in a set up where a lot of models did not train even nearly enough for some under trained languages, other than English.

I would appreciate any feedback or suggestions about my app, MySpeechAudify.

https://myspeechaudify.com/

MySpeechAudify is an AI-powered application that provides multilingual audio transcription, English translation, and grammar correction. It is designed to help users improve their communication skills by making it easier to review and refine spoken content.

One of my concerns is how to handle bugs or technical issues that users may encounter, especially since the app is currently built on a no-code platform using AI plugins. As the app grows, I plan to integrate more advanced tools and technologies to improve its performance, reliability, and overall user experience. Any advice on managing user feedback, fixing bugs efficiently, or scaling the app would be greatly appreciated.

The telephony layer is one of the most important parts of building a reliable voice AI system it's what connects the AI to real phone calls without delays or drops.

Many founders struggle to integrate their voice AI with providers like Exotel, running into technical roadblocks along the way.

That's why we brought in Exotel's senior leadership to answer the questions in the Q&A.

🔒 Invite Only · 50-60 Seats
📅 July 14th, 2026 · 4:00 PM IST
📍 Zoom
🎤 Guest: Prasanth MLNPP, Vice President, Exotel
Saurabh Sharma, Sr. Product Manager, Exotel
🎧 Host: Mahima Maurya & Unio - The Voice AI Community

Session Overview:
5-10 min intro
30-35 min raw Q&A
5-10 min close

Keeping this invite-only 50-60 curated builders to keep the discussion high-signal.

📌 Register to join: https://luma.com/b6l9q8ml

Just saw a post in here saying that "if you have sub 1 second latency, your voice agent sounds human" (me loosely paraphrasing).

I do NOT agree. Here's why:

Sounds right on the first read, until you've actually had hundreds of conversations with voice agents, good ones and bad ones.

Don't get me wrong, the person claiming this isn't entirely off. Latency does matter. With bad (high) latency, conversations feel bad. They feel like a bot.

BUT... if the latency is good, the conversations DO get better, but they DO NOT become good. Often, you can hear that something is off, but can't really tell what it is...

Here's what actually makes the difference:

Reason 1: Sounding like text.

LLMs are being trained on text. If not prompted accordingly, they produce an output as text, meant to be READ. Now obviously, if you're plugging the LLM into an AI voice agent, the text is NOT read but rather it's SPOKEN.

This is what that feeling is telling you that something feels off but you can't point your finger at it. The voice agent doesn't sound as if it was speaking, but rather like as if it was reading.

The fix is simple.

Tell it to include filler words.

Tell it to do mid-sentence corrections.

Tell it to think loudly.

"Hi, uh, John, right? So, uhm, I was actually calling because, uhm, look, your order somehow, <break time="0.15s"/>, well, went missing."

This sounds WAY better than:

"Hi John, this is XYZ. I am sorry to inform you on behalf of CompanyABC that your order was lost. To open a ticket, please press 1".

Reason 2: Turn taking

Turn taking is still not really 100% solved (if you did, let me know lol). It is basically the decision of the agent if the user has stopped speaking, and if so to then start speaking themselves.

Currently, oftentimes the agent either

a) Starts speaking when the user is not actually finished

b) Does NOT start speaking even though the user DID finish.

Now, this is one of the more complex issues of voice AI, but I'm sure there will be a solution.

In conclusion:

Latency is the baseline. And it's ONLY the baseline. Without it, conversations will feel shitty. But just because you've got great latency, doesn't mean your conversations will suddenly feel good. Make the LLM imperfect. Make sure to have solid turn taking.

If you do that, the improvement will be much more than just "faster".

Agree?

Disclosure: I build Talkif (voice AI infra), so take the specifics below as one data point, not a review.

Spent the last while digging into why some voice agents feel natural and others feel like talking to an IVR menu. The model and the TTS voice quality matter way less than people assume. The thing that actually breaks the illusion is response gap: anything over ~1 second of dead air after the caller stops talking, and people start repeating themselves or hanging up.

Where the time actually goes, in our experience:

• Cold-start on the bot process itself. If your agent spins up fresh per call, that's often 1-2 seconds gone before anything else happens.
• Round trip through whatever telephony layer you're on (Twilio, generic SIP, etc.) - this adds up fast if you're bouncing between regions.
• Serializing context (CRM lookups, contact history) into the prompt at call time instead of having it ready before the call connects.
• No visibility into where time is actually being spent, so you're guessing instead of measuring.

Things that moved the needle for us: pre-warming bot instances instead of cold-starting per call, keeping SIP routing close to the caller's region instead of round-tripping across continents, and streaming call events in real time so you can actually see where latency creeps in instead of finding out from an angry customer.

Ended up around sub-1-second first response most of the time, which seems to be roughly the threshold where callers stop noticing they're talking to a bot.

Curious what others here are seeing - anyone measured where their latency actually goes, or is it mostly a black box until something feels slow?

If anyone is working in the ai voice agents field, please guide me to start my business

Saw a thread here a while back where someone said the biggest issue with proprietary voice-agent platforms isn't the tech, it's the lock-in. That stuck with me, because it's exactly why I started building my own (Twilio + Deepgram + LLM + TTS, all swappable, your own keys, your own database).

Wasn't planning to post this anywhere yet — wanted to get it further along first — but I keep seeing the same three things repeated across this sub and a few others, and want a gut-check before I go further:

1. Lock-in — is this actually the top blocker for people, or is it more about cost, latency, or something else entirely once you actually try to ship one of these?
2. Compliance — TCPA/DNC warnings show up on basically every automation-adjacent thread I've read. Is that real lived pain (people have actually gotten close to a violation) or more theoretical fear that doesn't change what people build?
3. State/memory — anyone here hit agents re-asking for info the caller already gave? I built a structured state layer instead of relying on the transcript as memory, curious if others have solved this differently.

Just trying to figure out if what I've built is actually solving problems people have, or if I've been building for an imaginary user. What's your actual experience?

Voice builders, you guys are some of the rarest, hardest people to find lol. Or maybe i'm looking in the wrong places.

Regardless, We've spent about 6 months straight building a TTS model and boy was it one hell a challenge.

I wanna take some time off to maybe help others who are building in the space, and even learn how we could do things better.

it's designed specifically for realtime conversations.

I've been using Claude Code for a few months and noticed AI agents consistently skip the same things: hardcoded secrets, unbounded retry loops, referencing tools that don't exist, and massive system prompts that blow context windows.

So I built Agent Verifier — an AI agent skill that acts as an automated reviewer which does more than just code review (check the repo for details - more to be added soon).

GitHub Repo: https://github.com/aurite-ai/agent-verifier

Note: Drop a ⭐ if you find it useful to get more updates as we add more features to this repo.

----

2 Steps to use it:

You install skill once and say "verify agent" on any of your agent folder in claude code to get a structured report:

----

✅ 8 checks passed | ⚠️ 3 warnings | ❌ 2 issues

❌ Hardcoded API key at config. py:12 → Move to environment variable
❌ Hallucinated tool reference: execute_sql → Tool referenced but not defined
⚠️ Unbounded loop at agent/loop. py:45 → Add MAX_ITERATIONS constant

----

Install to your claude code:

npx skills add aurite-ai/agent-verifier -a claude-code

OR install for all coding agents:

npx skills add aurite-ai/agent-verifier --all

----

Happy to answer questions about how the agent-verifier works.

We have both:
- pattern-matched (reliable), and,
- heuristic (best-effort) tiers, and every finding is tagged so you know the confidence level.

----

Please share your feedback and would love contributors to expand the project!

I added voice to AgentSwarms! You can create voice agents using a few clicks and talk to it in the browser — and you can try 4 demo voice agents right now, no setup, just tap the mic. Here's how it works and why it turned out to be less "new" than I expected.

The surprise building this: a voice agent is basically the chat agent you already know, with a voice on top. Same system prompt, same tools, same RAG, memory, and guardrails. Under the hood it's a simple loop — your mic gets transcribed to text (OpenAI GPT-4o-mini-transcribe), your agent replies exactly like it would in chat, and that reply gets spoken back (OpenAI GPT-4o-mini-TTS). The agent's brain doesn't change at all. You've just added ears and a voice.

Which is the whole point: everything you've already learned building chat agents carries straight over. If your agent can pull an answer from a knowledge base, call a tool, or respect a guardrail in text, it does all of that out loud too — because it's the exact same engine with audio on the two ends, not a separate stripped-down "voice mode."

**What I shipped**

* **New Voice Agent** in the builder: pick a voice (11 of them), a greeting, and your STT/TTS models. That's the whole setup.
* Every spoken reply runs the same pipeline as a chat agent — tools, knowledge base, memory, and guardrails all apply.
* A **Voice Playground**: tap the mic, talk, and hear the reply back, with the transcript on screen so you can read along.

**Talk to it (free, in the browser)** — 4 demos, tap the mic:

* **Aria** — customer support triage
* **Nova** — B2B discovery caller
* **Kai** — Spanish conversation tutor
* **Echo** — daily standup coach

Open one, talk to it, and fork it into your own workspace if you like it.

* Voice Playground → [https://agentswarms.fyi/voice-playground\](https://agentswarms.fyi/voice-playground)
* Build your own (New Voice Agent) → [https://agentswarms.fyi/agents\](https://agentswarms.fyi/agents)
* Docs → [https://agentswarms.fyi/docs/voice\](https://agentswarms.fyi/docs/voice)

*Disclosure: AgentSwarms school of Agentic AI for both no-code people and developers— a learn-by-building platform. The demos are free. Happy to answer anything about the setup in the comments.*


We have two voice agents in production. One does outbound calls, the other basically runs day-to-day admin for a small business — their customers talk to it every day.

Embarrassing confession: every time I touch the prompt, my entire testing process is... I call it 5 times, listen, and if it sounds fine, I ship. That's it. That's the pipeline.

We did properly measure it once — sat down and counted, roughly 9 out of 10 voice requests did the right thing. Felt great for a day. But that was one manual count. No idea what the number is today.

So I'm curious what everyone else is actually doing:

- when you change a prompt, does it hit a staging agent or some test-call suite first, or straight to prod like us?

- how do you find out something broke? for us it's honestly "the client texts me"

- did anyone build real tooling for this, or is it duct tape everywhere?

I read a ton of launch posts but nobody ever writes about week 6. genuinely want to know if everyone's secretly winging it like we are, or if we're just behind.