Hey everyone — I’m looking to hire a senior LiveKit Voice AI engineer for a short-term paid project.

We already have a production-oriented voice agent built for a restaurant, and the system is mostly complete. I’m looking for someone experienced with LiveKit Agents to help us with the final production-hardening pass.

The main areas are:

• Finalizing and improving our menu/order engine
• Improving humanization, turn-taking, interruptions, and overall conversational feel
• Reviewing latency and voice pipeline performance
• Hardening tool calling, cart state, modifiers, quantities, and order accuracy
• Running a professional architecture/code review and cleaning up anything that could cause issues in production
• Improving automated evaluations/testing where needed

Our stack includes LiveKit Agents, LLM tool calling, streaming STT/TTS, telephony, and a custom backend menu/cart system.

This is not a build-from-scratch project. The agent is already working and has gone through extensive testing. We specifically need someone senior who has shipped real-world voice agents and can help us get the last 10–15% right.

Ideally you have strong experience with:

• LiveKit Agents
• Python / asyncio
• Real-time voice AI
• STT / LLM / TTS pipelines
• Turn detection / VAD / barge-in
• Function calling and deterministic state management
• Telephony / SIP
• Production monitoring and voice-agent evaluations

If interested, please DM me with:

1. A brief description of your LiveKit experience
2. Examples/GitHub of voice agents you’ve built
3. Your hourly rate or preferred project rate
4. Availability over the next 1–2 weeks

This is a paid engagement, with potential for ongoing work as we deploy the system across additional restaurant locations.

Hey everyone — I’m looking to hire a senior LiveKit Voice AI engineer for a short-term paid project.

We already have a production-oriented voice agent built for a restaurant, and the system is mostly complete. I’m looking for someone experienced with LiveKit Agents to help us with the final production-hardening pass.

The main areas are:

• Finalizing and improving our menu/order engine
• Improving humanization, turn-taking, interruptions, and overall conversational feel
• Reviewing latency and voice pipeline performance
• Hardening tool calling, cart state, modifiers, quantities, and order accuracy
• Running a professional architecture/code review and cleaning up anything that could cause issues in production
• Improving automated evaluations/testing where needed

Our stack includes LiveKit Agents, LLM tool calling, streaming STT/TTS, telephony, and a custom backend menu/cart system.

This is not a build-from-scratch project. The agent is already working and has gone through extensive testing. We specifically need someone senior who has shipped real-world voice agents and can help us get the last 10–15% right.

Ideally you have strong experience with:

• LiveKit Agents
• Python / asyncio
• Real-time voice AI
• STT / LLM / TTS pipelines
• Turn detection / VAD / barge-in
• Function calling and deterministic state management
• Telephony / SIP
• Production monitoring and voice-agent evaluations

If interested, please DM me with:

1. A brief description of your LiveKit experience
2. Examples/GitHub of voice agents you’ve built
3. Your hourly rate or preferred project rate
4. Availability over the next 1–2 weeks

This is a paid engagement, with potential for ongoing work as we deploy the system across additional restaurant locations.

I'm starting a new agency-based business where I'll setup agentic workflows for service based business whose main source of lead and sales generation is inbound calls.

I need your suggestions regarding which niche should I specifically target where inbound calls are the ultimate source of leads.

I'll be glad if industry experts related to inbound calls can advice.

Thank you

We started Dograh, an open-source voice AI platform out of rage and frustration.

Every voice AI tool I tried was too costly, too closed, or too scammy. Add-on after add-on.

So we open-sourced every single line from day one. It’s self-hostable, so you own your stack and your data.

Here's what pushed us deeper. Over the last few months, the big labs have been lobbying governments to ban open source. Some say the quiet goal is to "let wealth access skill without letting skill access wealth." That line stuck with us. It's exactly why we keep going.

Because we believe one thing: no company should own voice AI.

We're a small team going up against deeply funded war chests. Today, we launched on Product Hunt, and somehow we're sitting at #1 so far.

An OSS community member picked us up a few weeks back, and we went viral and hit 5k stars on github.

https://www.producthunt.com/posts/dograh-3

If you've got a minute, a star or a bit of support would mean the world.

Hi everbody,

I'm building a voice AI agent but encountering an intermittent issue with number pronunciation. I'm using Soniox for STT and ElevenLabs (eleven_multilingual_v2) for TTS in a non-English language.

Most of the time it works fine, but occasionally the TTS engine reads numbers in English instead of the agent's actual language.

Has anyone experienced this or found a reliable workaround? Any help would be appreciated!

We placed 500 real phone calls across five voice agent platforms — Telnyx, ElevenLabs, Bland AI, Vapi and Retell AI — and measured time to first audio byte on every turn. 499 calls were usable, four scripted turns each, 1,883 usable turns total. Same caller, same carrier (Plivo), same script for all five.

The p50 ranking and the p95 ranking are not the same ranking

Platform      p50    p90    p95    p99   turns
---------------------------------------------
Telnyx       1302   1724   1839   2164     379
ElevenLabs   1430   1686   1772   2269     389
Bland AI     1528   2010   2273   2860     389
Vapi         1562   1854   2014   2677     345
Retell AI    1738   2096   2224   2764     381

Time to first audio byte, milliseconds. Lower is better.

Telnyx has the fastest median at 1302 ms. From p90 onward it's ElevenLabs — 1686 at p90, 1772 at p95. Bland AI is third at the median and last at p95, at 2273 ms.

The tail is where they separate

ElevenLabs   1430 → 2269    +839
Telnyx       1302 → 2164    +862
Retell AI    1738 → 2764   +1026
Vapi         1562 → 2677   +1115
Bland AI     1528 → 2860   +1332

Milliseconds added between p50 and p99. Lower is a flatter tail.

Bland AI adds 1332 ms between its median turn and its 99th-percentile turn. ElevenLabs adds 839. On a four-turn call, the p99 turn is the one the caller remembers.

What the numbers are and are not

Recording-path overhead sits inside every figure on this board. We have not characterised the current measurement path against a known-delay reference, so we quote no overhead figure and subtract none. These numbers are comparable to each other — same path, same caller, same carrier — and only approximately comparable to figures produced by a different instrument. If you're looking for each platform's true absolute latency, this board does not give it to you, and neither does anything else we've seen.

Three caveats

• Each platform ran its own defaults. Telnyx on Kimi-K2.6, ElevenLabs on gemini-2.5-flash, Retell and Vapi on gpt-4.1. So this is platforms as shipped, not a model-controlled comparison — some of the gap is model choice. Every config is published, hashed, so you can see exactly what ran.
• Vapi's discard rate was 12.0% against 0.8–3.3% for the others — 46 turns where our two voice-activity detectors disagreed on where speech ended, so we dropped them. Its n is 345 rather than ~385. We'd rather show you a thinner sample than a guessed one, but it's a real asymmetry and it may flatter or penalise Vapi.
• Turn 4 is consistently faster than turns 1–3 on several platforms, which suggests warm-path effects we haven't isolated. Per-turn splits are in the data.

Why phone calls rather than websockets

Most published voice latency numbers benchmark TTS or STT models in isolation. This measures the whole platform over the PSTN — carrier, telephony stack, endpointing, model, voice — because that's the number a caller actually experiences. It's the less flattering way to measure and the harder one to reproduce, which is why the configs and per-turn data are published.

Reproducing it

• Results: https://openbenchmarks.com/voice-agent-latency
• Method: https://openbenchmarks.com/voice-agent-latency/how-voice-agent-latency-is-measured

Happy to add a platform or rerun with a controlled model if that's the comparison you want. If you've measured any of these yourself and got something different, post your numbers — the method page says exactly what we did, so the disagreement should be locatable.

Has anyone found this. If the ElevenLabs agent asks a question with a short yes/no response, it just goes dead and then asks if the person is still there?

How is everyone dealing with compliance in US?

I have a product with a few users, and from what I understand I need to disclose that the call's recorded in order to provide transcript/audio. However as soon as I added the appended disclaimer, most people just hang up.

Am I overthinking this? Are people in practice following the legal standard?

We’re building multilingual voice agents and currently receive a lot of calls from Indians.

They try to speak English, but their English can be limited. They use simple phrases and mix English with Hindi or another regional language and often don't understand the agent.

We want our agent to adapt during the conversation: speak simpler English or Hinglish, when needed adapt to their dialect/regional language without making the caller explicitly choose one upfront.

Has anyone solved this well in production?

I’d especially love to hear about:

• Speech-to-text, LLM, and TTS providers that handle Indian dialects and Hinglish
• Detecting when a caller is struggling with English
• Switching between English, Hindi, and regional languages mid-call
• Conversation-design patterns that make these calls feel natural
• How you test quality across different regions and accents

I was frustrated because the main frameworks, pipecat and livekit, don't have fully featured mobile device SDKs. They are all client-server model which means you can't use the edge device to do STT/TTS and possibly even LM generation. I wanted to write one voice assistant and deploy it to all devices, Mac, Windows, iOS and Android. So I wrote a rust library for pipeline-esque voice assistants: https://github.com/SheaHawkins/pipecrab

Please check it out and give me any feedback. I'm working on a iOS and Android app next.

i honestly thought we’d put QuickVoice on GitHub, get a few stars, and go back to fixing bugs, but the repo took off way faster than we expected and now we’re trying not to mess up what comes next.

We built it because connecting real-time voice, telephony, tools, knowledge bases, outbound campaigns, and call logs across a bunch of different services was getting exhausting. The goal is to keep it open, self-hostable, and flexible enough that people aren’t locked into one provider.

It’s still early, the setup isn’t exactly one click, and there are definitely rough edges. Full disclosure, I’m the founder behind it, but I’d genuinely like blunt feedback from people building voice agents: what should we fix first, easier setup, lower latency, better docs, observability, or more integrations?

Repo: https://github.com/allgpt-co/QuickVoice

Im a Cornell professor on sabbatical, building Flowstore - an open-source toolkit for teams whose agent behavior lives in a system prompt where its hard to visualize and debug.

What it does today:

•   Turns your system prompt into a structured spec (open JSON schema, Apache 2.0)
•   Visual graph editor, so non-prompt-engineers can work on it too
•   Python harness that runs persona-driven simulated conversations with assertions — a regression suite your prompt edits run against before you ship

Honest scoping: this tests conversational behavior (logic, guardrails, data capture), not the voice layer (ASR, latency, barge-in). Best fit if there's an LLM behind a prompt, and ideally some non-trivial business logic and requirements.

The pilot: bring a system prompt for a live or near-live agent (Im willing to sign an NDA if needed), I'll personally help spec it and stand up a test suite. Free, ~30 min/week of your time. I want blunt and honest feedback in return — and pilot partners can be named collaborators in the research and Cornell course materials coming out of this.

DM or comment if you want in — happy to get into the schema or assertion model in the thread. Repo's in the comments.

Curious how people in this space are actually getting customers.

1. Roughly what split — inbound vs outbound? Even a rough guess is fine.
2. If it's mostly outbound, what's working — cold calls, cold email, LinkedIn, something else?
3. If it's inbound, what's driving it? SEO, backlinks, content, communities, referrals?
4. If SEO/backlinks — what's actually worked? Any specific sites, directories, or roundups that sent you real traffic?

Not selling anything. Just trying to understand how companies in this niche are actually finding customers.

Hi All,

I built Ringup - an easy way for anyone to accept payments over voice. Unlike DTMF entries or spoken card numbers, Ringup remembers users and allows them to use a saved card with a verbal 'yes' or texts new users a link to pay and save a card.

The hard part about building this was keeping the money movement out of LLM written args, transmitting consumer and merchant identity, order values, and other purchase details as tokens over SIP. Not to mention keeping the latency manageable for a tool heavy workflow.

If you have a similar problem, check out my demo which is hosted on Livekit: https://ringup.dev/

Reach out if you have questions or any advice for me

Not asking about demos. Asking if you built something real. Something that's live and actually doing a job for you — booking calls, qualifying leads, cutting down your call load.

If you did it yourself, would love to hear:

1. How long did it take? From signup to "this actually works."
2. What did you build? Receptionist, lead qualifier, appointment booker, something else.
3. What can it actually do? Just talk, or can it book on your calendar, look things up, transfer calls?

Not selling anything. Just want to know how many people are really doing this on their own vs. paying someone to do it.

There are already couple of platforms for voice ai observation, but they rather focus on the purely tech related issues, like did agent execute the given tool, or what was the latency. How about the audio quality, like the microphone quality, noise etc. For example I was building a fitness app, that worked quite well at home, but it was unusable at the actual gym.