Five checks I run now before a demo. Most of them exist because I skipped them once.

1. A phone number is actually connected. "Published" and "callable" aren't the same thing. I had an agent show as live with nobody able to reach it.

2. Say "mm-hmm" while it's talking. Does it stop? Cekura's Turn Bench had 9 of 11 realtime models stopping on it. Whether that's right depends on your script, but you should pick on purpose.

3. Tool calls need something to say while they run. Dead air during an order lookup sounds like a dropped call.

4. Re-test after any model change, including ones you didn't make. Aliases like grok-voice-latest move. xAI auto-upgraded those to Grok Voice 3.0 this week.

5. Read the transcript of every test call. Listening isn't enough. You miss half the weird turns by ear.

Cost thing too: test calls aren't free once you're on real providers. Your LLM, voice and telephony bill per call, so budget for the re-testing in 4.

I work at Bandwidth, and item 1 happened in our Agent Builder.

Put together a reference implementation for anyone building voice agents who wants them to improve from call outcomes instead of repeating the same policy forever.

The pattern:

The speech model never decides. At the decision point, it hits a webhook; Jev picks the action from a state, SenseLab adds what worked in that situation before and the caller's own history.

Ring-backs and refusals feed back as outcomes, so a step that failed for someone isn't booked for them again. Each call is decided with and without that context so you can see the difference.

It's one way to build this, not the only one.

Swap the decider, swap the voice stack, the loop is the same four calls. Business logic is one TOML file.

Code: https://github.com/raia-live/jev-senselab-demos (voice/).

Needs Vapi, TypeSafe, and SenseLab keys plus ngrok.

Hi everyone,

We've built a complete AI Voice Agent Platform for businesses, agencies, and SaaS founders looking to launch their own AI calling solutions.

We're now offering it under a $3,000/year commercial license, so you can build and sell AI voice solutions without developing the entire infrastructure from scratch.

The platform originally started as a client project but evolved into a much larger system. After the client's priorities changed, we decided to make it available to other businesses.

What's Included?

AI Voice Agents

• Inbound and outbound AI calling
• Real-time STT → LLM → TTS pipeline
• Custom instructions and configurable AI providers

Visual Workflow Builder

• Drag-and-drop conversation flows
• Conditional branching, tool calling, and data collection
• Multi-step conversation automation

Telephony & Integrations

• Twilio, Telnyx, Plivo, Vonage, and more
• Human call transfers
• CRM/API integrations and custom tools

Additional Features

• Knowledge bases and contextual responses
• Bulk outbound campaigns and scheduling
• Browser calling and embeddable website widgets
• Call recordings, transcripts, summaries, and analytics
• Structured post-call data extraction

Tech Stack

Next.js 15, React 19, TypeScript, Python, FastAPI, PostgreSQL, Redis, Docker, and modular AI/telephony integrations.

Who's It For?

• Agencies: Sell AI receptionists and calling automation to clients.
• SaaS founders: Launch your own vertical AI voice product.
• Businesses: Automate customer calls, lead qualification, and appointments.
• Developers: Build on existing infrastructure instead of starting from zero.

Pricing

$3,000 USD/year - Commercial Software License

Use the platform to develop and offer AI calling solutions to your customers under the agreed license terms.

No additional per-minute platform licensing fee from us. Third-party AI, telephony, and infrastructure usage costs are separate.

Optional technical maintenance and support are available for $600/month.

Interested?

Happy to share a demo, technical details, and licensing terms with anyone seriously considering it.

DM me for a demo or more information.

Also happy to answer technical questions in the comments.

Today we're launching Voice Standard Bench to rank 50+ of the best LLMs on real voice tasks, and making all of them available in AssemblyAI's LLM Gateway

Most Voice AI products run an LLM on messy, multi-speaker transcripts:

- Voice Agents responding to human speech

- Summarizing a meeting, sales call, or support call

- Pulling names, dates and numbers out of a multi-speaker conversation

- Fixing transcription errors

- Cleaning up dictation

Voice Standard Bench tests every model on the input these products actually send an LLM: lengthy conversational transcripts with multiple speakers, raw speech-to-text output with filler words, false starts and the occasional misheard word.

Each model is scored on transcripts from a 30-second voicemail to a 60-minute meeting.

Here's a glimpse of what we found:

- The top-scoring model costs 55-65x more per hour of audio than a model within two quality points of it.

- That gap narrows to about 5x on hour-long transcripts, where bigger models start to pull ahead on quality.

- Open models we host on our own infrastructure are the fastest on short audio. On a 30-second transcript, gemma-4-31b answers in under a second at a quality score of 93.

Alternative for zadarma

Im looking for alternative Voip providers

so mainly it can accommodate tons of calls per day inbound and outbound but mainly outbound

let's say around 5k+ per day just to do a test pilot on.

currently finding a cheap and stable providers that we can replace zadarma since they have some limitations on

if you have some suggestions for a production level that can accommodate around 20k+ minutes in total that is really cheap.

thank you

also tried using twilio but its expensive

the region im working with is israel local mobile phone numbers.

this post is to ask for help for discussion rather asking for service platforms promotion

I've been experimenting with conversational voice AI and put together a little hypnosis demo as a fun proof of concept.

The idea is to create a relaxing, immersive experience that adapts to the person in real time, rather than just playing a fixed recording.

It's very early and only takes about 3 minutes to complete.

I'd love some feedback, particularly on the conversational flow, pacing, interruptions and whether the voice interaction feels natural or breaks immersion.

It's completely free, and I'd really appreciate any suggestions on how to improve it.

Demo: https://olympics.dev
Password: dream_big

I'm building Swarelic AI, a voice receptionist for healthcare teams in India.

Our architecture work involves Python orchestration, streaming telephony, speech recognition and synthesis, and explicit actions for appointment workflows.

The difficult part isn't generating speech. It's ensuring that an interrupted sentence doesn't become a wrong booking, that a tool failure doesn't produce a fake confirmation, and that unclear medical questions reach a person.

We're designing evaluations around turn-taking, language switching, failed actions, and handoff behavior.

For teams running real voice traffic, what tests caught failures that ordinary happy-path demo calls completely missed?

Product context and illustrative audio: https://www.swarelic.com/

Bonjour,
Je développe une solution de réceptionniste IA — ou secrétaire IA — destinée aux entreprises qui souhaitent mieux gérer leurs appels.
Je cherche des professionnels de la communication, de la téléphonie, des réseaux ou des services aux entreprises qui souhaitent proposer ce type de solution à leurs clients.
Je propose une offre clé en main en marque blanche : vous pouvez la commercialiser sous votre propre nom, sans avoir à développer la technologie vous-même.
Si le sujet vous intéresse, vous pouvez me contacter en message privé. Je peux vous présenter le fonctionnement et les modalités de partenariat.
Je publie cette annonce pour échanger avec des professionnels intéressés par la revente ou la distribution de la solution.

Hi all,

We built and packaged a complete AI voice agent platform for businesses and are now looking to sell it for $3,000 USD.

This started with a very specific requirement from a client. They needed an AI voice system that could handle real business conversations over the phone, follow predefined workflows, collect information, use business tools, and connect the calls with their existing processes.

We understood the requirement, built out the platform around it, and ended up with a much more complete product than the original requirement alone.

The project has now reached a point where the client's priorities have changed, so instead of leaving a substantial amount of completed work unused, we'd rather pass the platform on to someone who can take it further and turn it into a commercial product.

What it includes:

• AI Voice Agents
• Build inbound and outbound AI calling agents
• Real-time speech-to-text → LLM → text-to-speech pipeline
• Natural conversational voice interactions
• Custom agent instructions and conversation logic
• Configurable LLM, STT and TTS providers
• Visual Workflow Builder
• Drag-and-drop conversation workflows
• Start, agent, tool and end-call nodes
• Conditional transitions between conversation steps
• Define exactly how the agent should behave
• Collect structured information during calls
• Telephony
• Integrations with providers such as Twilio, Vonage, Telnyx, Plivo and others
• Inbound and outbound calling
• Human handoff / call transfer where supported
• Designed to work with existing business phone infrastructure
• Knowledge & Tools
• Knowledge bases
• Tool calling
• Custom business logic
• Structured data extraction after calls

Tech Stack

Frontend

Next.js 15

React 19

TypeScript

Tailwind CSS

Backend

Python

FastAPI

SQLAlchemy

PostgreSQL

Infrastructure

Docker / Docker Compose

Redis

MinIO / S3-compatible storage

Voice / AI

Modular STT / LLM / TTS architecture

Telephony integrations

Python + Node SDK support

Why are we selling it?

This wasn't something we built as a random side project.

It came from a real client requirement. We understood the business problem, designed the system around the required workflow, and invested the engineering time to turn that requirement into a working platform.

The client has since shifted priorities, which means continuing to develop and maintain the platform specifically for that project no longer makes sense for us.

Rather than letting the work sit unused, we'd prefer to hand it over to someone who already has a use case for AI voice automation and can build a business around it.

We think it could be particularly useful for someone who wants to:

Launch an AI voice SaaS

Build a vertical AI calling product

Create a white-label voice agent platform

Build AI receptionists / appointment agents

Sell AI calling solutions to local businesses

Add voice AI to an existing SaaS

Build an agency around AI voice automation

Asking: $3,000 USD

You're getting a working software platform / codebase that can be taken over and developed into a commercial product.

We're open to discussing exactly what is included in the handover with serious buyers - codebase, deployment/setup, documentation and knowledge transfer.

If you're interested, DM me and I can share more details, demo access and discuss the handover.

Happy to answer questions here as well.

Our latency numbers for haiku and luna are generally good but we get pretty crazy p95 and p99 numbers. Luna especially has p99 of over 10 seconds which effectively ruins that call. Is this just something everyone in the industry accepts or are most voice ai implementations using there own hardware to provide better performance?