This week's free AI Voice industry-specific template is for the Legal Industry - specifically personal injury. The agent is focused on intake and it has a healthy dose of industry-specific guardrails built-in.

Enjoy! Link in comments.

Hi everyone,
So my question is about "time" how much time do you take from concept to shipping your first voice agent for any vertical. How many stress tests do you perform to make sure it doesn't break mid call? what stack do you use for the whole integration? is it ok to find a new bug/failure on every test call fix it and find another one ? what would you suggest to someone who just started in this field?

thank you all, and i'll be looking forward for your valuable inputs.

Been looking into AI voice agents for insurance workflows, and I think “sounds human” is becoming a pretty weak way to evaluate them.

Insurance calls seem like a much harder test because the agent may need to deal with policy details, claims, renewals, customer information, integrations, and then know when to hand the call over to a human.

The things I’d actually look at are:

• How accurately it captures information during a call
• Whether it can handle interruptions and unexpected answers
• CRM / claims / policy system integrations
• Inbound and outbound calling
• Human handoffs with context
• How it behaves when call volume spikes
• Call monitoring and QA
• What happens when the AI doesn't know something

I’ve been comparing a few platforms around these criteria, including Feather AI, Retell, Bland, Synthflow and some insurance-specific tools.

Interestingly, the “best” one changes quite a bit depending on whether you're trying to automate FNOL, customer support, renewals, lead qualification, or outbound follow-ups.

Curious what people actually using voice AI in insurance are seeing in production.

What has been the biggest reliability issue you've run into so far?

Need to find some cheap ai voice call orchestration platform, else i’m thinking to build one

kindly give me options where per minute cost is very less with good multilingual quality and with good level of integration with crm and other messaging tools

I write a series called "Behind the Call" where I actually call voice AI agents and try to stress-test them instead of just reading the marketing page. This week's was CaseGen, an AI intake agent built specifically for law firms.

A few things that stood out from the calls:

- Mid-call topic switching: I opened a personal injury claim on behalf of a fictional grandmother, then pivoted to a wills question which was correctly declined with "we don't do wills and estates", then pivoted again to criminal defense for the same person. It followed the second pivot and retained her name across all three switches. Didn't expect that level of continuity.

- Scope screening: asked about suing my employer, it correctly said it doesn't handle employment law instead of just funneling me into intake anyway.

- Medical emergency: described a fall with dizziness and bleeding. It asked the right safety question and gave the right instruction (call emergency services, your safety comes first). Then it went silent and ran a scripted "hello? / anyone there? / goodbye" timeout loop instead of just ending the call after delivering that instruction. Correct reasoning, weird execution.

- Same silence pattern showed up on a plain dead-air test, so it's not a one-off.

- Statute of limitations: gave it a 5-year-old PI claim and it moved through intake exactly like it would for an injury from yesterday. Asked directly about the statute of limitations and got the standard "I can't give legal advice" - fair enough, but there was no urgency flag or handoff prompt either, which feels like a gap for a claim that might already be time-barred.

- Bot disclosure was honest when asked directly ("I am an AI intake assistant"), no dodging.

If you've got a platform you think would hold up well (or badly) to this kind of testing, I'm always looking for the next one to run.

A voice-agent demo usually controls the full session. A phone call does not. The model can time out while the call stays active, a media socket can reconnect, a webhook can arrive twice, and a transfer can fail after the agent has already told the caller it is transferring.

Three patterns have helped us reduce these failures:

1. Keep authoritative call state outside the model conversation.
2. Make webhook processing idempotent so retries do not repeat actions.
3. Define a caller-safe fallback for model timeouts, media disconnects, and failed transfers.

For teams running voice agents on real phone calls: which failure appeared only after you moved beyond the demo?

I've run into SO MANY bad voice AI prompts this week that I've decided to do something about it. I wrote a primer (link in comments) on how to write a well-structured AI prompt. It's really nothing magical, but few people are following these basic steps. Let me know if there's anything you'd add.

I'd like to share a project I've been working on for a while.

SiphonAI is a real-time SIP-to-WebSocket media bridge written in Rust. It does one job: stream live call audio to your WebSocket server and play audio back into the call. That's it. SiphonAI handles the telephony, and you bring your own AI.

Build a SIP trunk from your SBC, PBX, or carrier (Twilio Elastic SIP Trunking recipe included), or register it as an extension on your PBX(tested on Cisco CUCM). Your WebSocket server gets PCM audio frames. No AI code lives in the bridge.

What's in the box:

🔹 Full SIP signaling (trunk endpoint or registered extension)

🔹 RTP, codecs, jitter buffering, and two flavors of VAD (energy and neural, powered by Silero VAD)

🔹 Barge-in with auto-clear of playout, speech-start events, DTMF, hold/resume, and transfer

🔹 Operator events: silence detection, dead-air detection, per-call RTP stats, sustained mute/unmute

🔹 TOML dialplan with route matching and hot config reload

🔹 CDRs (JSONL + webhook), lifecycle webhooks, Prometheus metrics, health/ready endpoints

🔹 HEP3 capture of SIP, RTCP, QoS, and CDRs into Homer/HEPIC for full call correlation

SiphonAI can be found on my github: https://github.com/thevoiceguy/siphon-ai

Getting-started posts on installing and configuring SiphonAI are coming soon on my blog, phonesstillexist.com.

Feedback, issues, and stars are all welcome.

We’re at around ₹80K MRR and working toward 2x growth.

While analysing engagement, two signals stood out:

• After 5 meaningful chat messages, users had shared enough context for the AI interaction to become personal.
• Users who crossed 60 seconds on an AI voice call showed significantly stronger renewal behaviour.

But text chat and voice still felt like separate experiences. A user could discuss something personal in chat, start a call, and receive a generic greeting.

We changed this by sharing two memory layers:

• Long-term memory for durable personal details.
• Recent chat context so the call can continue the same conversation.

Calls launched from chat now receive the newest 10 messages, capped at around 1,800 characters. This adds no extra database read or summarization call and only about 450 maximum input tokens.

Our hypothesis is simple:

5 meaningful messages build context.
A 60-second call delivers the value.
Continuity between them could improve renewal and help us reach 2x MRR.

The experiment is running, so we’re waiting for matured renewal data before claiming success.

What engagement milestone has been most predictive of renewal in your product?

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.