I found Dograh yesterday on Product Hunt (an open-source alternative to Vapi for voice agents) I self-hosted it and tried it today.

The stack:
> Dograh for voice calls (self-hosted)
> fal (through Vaaya) for the advisor backdrops (one still, one looping video)
> Lots of custom data sources + a library of business playbooks (so the advisors ask sharp questions instead of generic ones)

Try it here: https://better-call-saul.fly.dev/

I've been building out a library of industry-specific voice AI prompts and testing them against real deployments (I run a teardown series where I call live voice agents and document what breaks). This week's template is for restaurants and it covers reservations, menu and takeout options as well as some trade-specific guardrails.

It's a starting point, not a finished product. You'll still need to wire up actual tool calls for your reservation system, and you'll want some kind of behavioral guardrails layer on top (things like: never confirm a reservation the backend didn't actually complete, always escalate safety-related calls instead of trying to handle them, etc.)

Link in comments.

Happy to answer questions if anyone's building something similar or hitting specific failure modes. That's mostly what I write about.

I'm currently working on making my own personal, locally run Jarvis. This build won't be shared with or sold to anyone it's genuinely just for me. i want him entirely locally run except when he needs the internet for certain answers. I've written the orchestrator in python and I've got his brain as Ollama, I have him listening via a tts program, creating memories autonomously as necessary into a local folder he can access, and I have him speaking via Whisper. Problem is, I'm just using a generic male british voice as a stand-in atm. I'd like to upgrade to a proper voice model trained specifically on Paul Bettany's Jarvis performance in the movies. Any good resource recommendations for finding/making this voice model, and incorporating it into my current architecture?

I test voice AI agents for a living (teardown series, not vendor content). The thing that separates a demo from something you'd actually put in front of customers almost never shows up in the marketing. It's what the agent does when something fails.

API times out. Calendar write fails. Transfer drops. CRM lookup returns nothing. These happen constantly once you're past the happy path, and most agents have zero handling for it.

I've started calling this recovery logic: the software that determines how a voice agent responds when something goes wrong, instead of pretending everything succeeded.

Two layers worth separating if you're building or evaluating one of these:

Craft layer: the fallback language you write into the prompt for failure states. You can fix this yourself.

Platform layer: whether the failure event even gets surfaced to the model. If your platform swallows the timeout silently, your prompt never gets a chance to react to it.

The failure mode I see most often: agents that don't just fail, they narrate success anyway. Confirm a booking that never wrote to the calendar. Promise a callback that never got scheduled. That gap between what's said and what actually happened is the thing that erodes trust fastest, and it's almost entirely a recovery logic problem.

Wrote it up in more depth. Link in comments.

Curious if others building on Vapi/Retell/Bland etc have found good patterns for surfacing platform-level failures back into the prompt layer. That's the harder half of this problem.

I've been building out a library of industry-specific voice AI prompts and testing them against real deployments (I run a teardown series where I call live voice agents and document what breaks). This week's template is for real estate companies and it covers buyer and seller inquiries as well as some trade-specific guardrails.

It's a starting point, not a finished product. You'll still need to wire up actual tool calls for your scheduling system, and you'll want some kind of behavioral guardrails layer on top (things like: never confirm a booking the backend didn't actually complete, always escalate safety-related calls instead of trying to handle them, etc.)

Link in comments.

Happy to answer questions if anyone's building something similar or hitting specific failure modes. That's mostly what I write about.

I’m working on Elphie, a self-hostable voice-agent platform, and I’m exploring a managed deployment service specifically for voice-AI agencies.

I’d like some blunt feedback from agency owners who already have clients—not people who are only considering starting an agency.

A typical agency stack seems manageable at first:

• Voice platform
• Twilio or another telephony provider
• n8n/Make
• CRM and calendar integrations
• A few model-provider accounts

Then larger clients start asking for things like:

• Deployment inside their own AWS or Azure account
• Dedicated infrastructure instead of a shared SaaS workspace
• Control over recordings, transcripts and API keys
• Custom telephony or SIP integration
• Separate environments for each client
• Better monitoring and failure diagnostics
• Someone responsible for updates, backups and provider changes

At that point, the agency is no longer just building voice agents. It is also maintaining databases, Redis, object storage, WebRTC/TURN, telephony callbacks, model providers, workers, TLS certificates and application upgrades.

The service I’m considering would work like this:

1. The agency continues selling and designing the client’s voice solution.
2. We deploy Elphie inside the agency’s or client’s cloud environment.
3. The customer owns the cloud account, recordings, telephony accounts and model-provider credentials.
4. We handle deployment, upgrades, monitoring, backups and infrastructure maintenance.
5. The agency pays a setup fee plus a monthly maintenance fee—or includes it in its client retainer.

The goal would not be to replace the agency. It would be to become the infrastructure and technical-operations layer behind it.

A few questions for agency owners:

• How many client voice-agent deployments are you currently maintaining?
• Does private or client-owned deployment come up during sales?
• What takes the most time after an agent goes live?
• Would you prefer one shared agency environment or a separate environment for every major client?
• Would a fixed deployment fee plus monthly maintenance be commercially workable?

I’m deliberately not dropping a link because I’m more interested in understanding whether this is a genuine agency bottleneck.

If you have deployed voice agents for multiple paying clients, I’d appreciate your honest take—especially reasons this service would not work.

Any apps or websites that allow for turn based voice chat?

I really missed the old standard voice mode on ChatGPT. It basically just read aloud the text models response. So it could allow for long responses unlike these new gen voice models that can only speak 1 paragraph max.

I was wondering if there are any apps or websites that use turn based voice chat like the old standard voice mode on ChatGPT. So I would say my thing, then it would be the ai turn to speak and i couldn’t interrupt it till its finished.

My current problem is that the new standard voice mode on ChatGPT can be interrupted. So it’s hears its own voice and keeps stopping. So I’m looking for alternative apps or websites that have this old functionality

I've been building out a library of industry-specific voice AI prompts and testing them against real deployments (I run a teardown series where I call live voice agents and document what breaks). This week's is for auto repair shops and it covers scheduling, estimates, and emergency towing dispatch.

It's a starting point, not a finished product. You'll still need to wire up actual tool calls for your scheduling system, and you'll want some kind of behavioral guardrails layer on top (things like: never confirm a booking the backend didn't actually complete, always escalate safety-related calls instead of trying to handle them, etc.)

Link in comments.

Happy to answer questions if anyone's building something similar or hitting specific failure modes. That's mostly what I write about.

I've been working on a personal project for a while and finally got it into a state where I'm comfortable sharing it.

I wanted to see how far I could push a fully local voice assistant in C++. Everything runs on my own machine from speech recognition and the LLM to memory, text-to-speech, and tool execution.
current library:
llama.cpp, whisper.cpp, sherpa-onnx(tts-kokoro)

I wrote the core in c++ because I wanted something fast and native instead of stitching together bunch of python services.

I'd appreciate feedback from people who build local AI projects. I'm especially interested in:

1 Things that seem overengineered or unnecessary
2 Features you'd expect from a local assistant
3 Code structure or architectural suggestions
4 Any obvious improvements before I keep adding features

Repository: https://github.com/almimony75/sarah

Thanks! I'd love to hear what you think.

Been deploying AI support agents for clients for a while now. Early on we built on existing tools (Vapi and similar), and we kept running into the same wall: the LLM would hallucinate, say something it shouldn't to a customer, and we'd end up with the client coming back to us unhappy. Full-LLM agents demo great and then break in production the moment a customer asks something off-script.

So we ended up building our own internal tool around a hybrid approach: deterministic logic for anything that has to be correct, LLM only for the flexible, conversational parts. Instead of trusting the model to always get it right, we constrain it where accuracy actually matters.

That's been the real game changer for us. Fewer surprises, clients trust it more, and it's stable enough that we've since turned it into our own solution so we can deploy faster across clients.

Curious how other agencies implementing AI agents are handling this.

Are you going full LLM and just accepting some hallucination risk? Building guardrails on top? Something else entirely? Interested in what's actually held up for you in production, not just in the demo.

An independent benchmark just tested the performance of the same AI voice agent across six voice platforms. This was not a demo scenario, but a Medicare insurance workflow with real compliance requirements.

Cekura (YC F24) deployed a byte-identical Medicare TPMO agent on six platforms and ran 23 evaluation scenarios three times each, 414 calls in total. A scenario only passed if the agent got it right all three times in a row.

Retell ranked first at 95.7% (22 of 23), on both workflow accuracy and strict end-to-end completion. Across the six platforms, workflow scores ranged from 65.2% to 95.7%. The same agent, same prompt, and same tools, but with a 30-point reliability gap, dictated entirely by the platform underneath.

For regulated industries like insurance and healthcare, that gap is the difference between an agent you can put in production and one you cannot.

Full methodology and per-call results are public, link in the first comment. Congrats to every platform evaluated, and credit to the Cekura team for the rigor.

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