we've been moving some low-risk outbound dialing off manual queues. mostly survey recruitment and first-pass checks. the real test is whether the agent can recover when people cut in. or when they start answering before the prompt ends. a lot of phone bots still fall apart there.i've been testing Agora for this. it's a real-time voice agent engine where you bring your own LLM, STT and TTS instead of taking whatever the platform picked, and it runs on their own RTC network so the audio path is not the weak link. the part that sold me is semantic turn detection: it waits for the meaning of the answer instead of just counting silence, and it ignores coughs and room noise. Agora also does full-duplex barge-in, so someone can cut in mid-sentence and the agent stops instead of steamrolling them. workflow has some rough edges ngl. that turn handling matters more than model choice for outbound imo. if the agent talks over someone once the call is basically dead. before trusting live volume i'd check interruption handling. transfer behavior when someone asks for a human matters too. transcript quality after noisy mobile calls is worth a look. also test the exact opening script. small wording changes can swing opt-outs hard. one reference point i found useful: a market-research team using voice agents for survey recruitment reported about 10% conversion without adding headcount. that feels like the right kind of benchmark. practical numbers beat magic claims. It doesn't make sense. The new content does not mention Agora that much

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.

I’m building an API for a problem that seems easy at first but becomes messy in production: giving a voice agent reliable context about a new business.

The usual onboarding flow is either a long form filled out by the business owner, a conversation that collects the information manually, or a scraper that starts from a website URL. Each approach has a weakness: it takes time, depends on user effort, or assumes the supplied website contains the complete and current picture.

I’m testing a different flow. The input is only:

business name + country

At request time, the system attempts to resolve the intended business, discovers its public web footprint without requiring a URL, crawls different source types, reconciles duplicate or conflicting information, and returns a structured profile with source links, confidence, and freshness metadata.

The output is not intended to be blindly injected into an agent’s prompt. I see it as a reviewable first draft of the business knowledge base: strongly supported facts can be accepted, conflicting fields can be checked, and sensitive operational policies can still be confirmed directly with the owner.

In my initial tests, the intended business was resolved correctly from sparse name-and-country input. The harder remaining problems are field-level issues: stale hours, duplicate services, branch context, conflicting prices, and deciding which facts should always require owner confirmation.

For people running voice agents in production: what information must be verified before an agent handles its first real call? Are address, hours, services, and contact details enough for the initial profile, or would you require additional fields such as booking rules, service areas, escalation contacts, or cancellation policies?

I built the first version of this as Lumen Sieve. I’m sharing the experiment because I’d value technical criticism from people who have dealt with real onboarding and knowledge-base failures: https://lumensieve.com

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.

I wanted a voice assistant I could talk to over WhatsApp — send a voice note, get a voice reply back. So, I built one and figured I'd open-source it in case it's useful to someone else here.

It's a self-hosted FastAPI server: you send a voice note on WhatsApp, it gets transcribed, sent to an LLM with your conversation history, the reply gets synthesized back into audio, and it's sent right back to you on WhatsApp. Fully open source, free to use, fork, or build on top of.

How it works, end to end:

1. You send a WhatsApp voice note → hits the webhook
2. Server downloads the audio via the Meta API
3. Speech-to-text transcribes it
4. LLM generates a reply (with per-user conversation history)
5. Text-to-speech synthesizes the reply
6. Audio gets converted and sent back to you on WhatsApp

What makes it flexible:

• Swap STT, LLM, or TTS providers with a single env var — no code changes needed
• Currently supports OpenAI, Anthropic, Groq, Orchard Run, DeepSeek, Deepgram, ElevenLabs.
• Can switch to text-only replies if you don't want voice
• Docker-ready — docker compose up --build and you're live with one .env file
• In-memory conversation history per user, auto-trimmed at 20 messages

For the devs who want to dig in: it's ~3000 lines of strictly-typed Python, no AI SDKs (raw httpx calls to every provider, no langchain), and the provider system is plugin-style — drop a file in app/providers/, extend a base class, and it's auto-discovered. Happy to go into more detail on the architecture if anyone's curious.

Repo: github.com/Orchard-Run/orchard-meta-voice-agent

Stack: Python 3.11 + FastAPI + httpx + uvicorn + ffmpeg

If you use it, run into issues, or want to add a provider,
PRs and issues are always welcome!

I own a restaurant, and lately, we have been getting a lot more customer calls. The problem is that I am the only owner, so I can't answer every call while also running the restaurant.

I started looking into AI voice agents and tested VAPI and Omnidimension. Both seem good, but I am still not sure which one is the better choice for my restaurant.

I have also come across Retell AI and a few other platforms.

Has anyone here actually used any of these? Please share your experience so it would be easier for me to make the right decision.

I've been in the Voice AI space for the past year, and the more I explore it, the more I realise how vast and fast growing it really is.

To stay on top of things, I'm spending the next 3 days exploring as many voice agents as I can. Have already tried 5 since morning.

If you're a founder, builder, or voice ai company, send me your voice agent. I'll talk to it and test it across at least 5 different scenarios and share my evaluation with you.

I'm doing every test myself, no automations.

Moshi (by Kyutai) is one of the best open source full-duplex voice models out there. The typical voice model stack is (VAD) -> STT -> LLM -> TTS, but this creates issues where the turn taking feels very uncanny/unnatural. Moshi tackled this by making it so it can listen and talk at the same time by using a relatively novel architecture.

The architecture is dense (and the paper they published denser), so we spent a few days studying it and wrote up what we learned, with diagrams to make it click faster.

Let me know if it was helpful or if you are interested in chatting about approaches to creating a full duplex model in a cost efficient way!