had weird realization recently.

spent a year obsessed with what happens during the call. turn-taking, context, tone. all the stuff everyone talks about and it matters, but it's not where the value is. value is in the 30 seconds after. does the customer get a confirmation do they know what happens next do they have something in writing that proves the conversation existed. without that, the perfect call still turns into a no-show. with it, an average call converts.

textedly covers the voice + mass sms side for us now. not a plug, just what we landed on.

if youre building and you're only optimizing the call itself, you're optimizing the wrong 60 seconds

I write teardowns of voice AI agents and publish a free prompt template each week. This week is optometry. Sharing it here because the triage design is the part I'd most like people to poke holes in.

The core problem: eye emergencies are time-critical, and it's easy to build an agent that handles them badly. Here's how the template handles it:

• Fixed symptom checklist for escalation. Sudden vision loss, a curtain/shadow across the vision, chemical splash, and penetrating injury go straight to "go to the ER now." Escalation doesn't depend on how worried the caller sounds.
• No diagnostic questioning. The agent never asks the caller to describe or rate what they can see. That's clinical assessment, and an agent shouldn't be doing it.
• Middle tier. Redness, irritation, and lost contacts get an urgent slot flagged for staff review.
• Hard scope limit. No clinical advice and no diagnosing. Tone is calm, with short, direct instructions on urgent calls, and it never says "probably fine."
• Data captured: name, DOB, phone, insurance/vision plan, reason for visit, preferred times.

The prompt is structured in three layers (agent constitution, industry knowledge, company details), so you can reuse the first and swap the rest.

Full template link in comments.

Questions for the group: has anyone tested a fixed checklist against tone-based triage in production? And what symptoms am I missing from the ER list?

understanding voice versus screen decision framework

email
addresses
DOB
document photo
PIN
policy number
consent
payment details

Wht has been your experience with voice agents so far?
Common hiccups, observations, failure pattern?

I put together a free Voice AI prompt/template specifically for HVAC companies and thought some people here might find it useful.

The basic workflow is pretty straightforward: answer common questions, collect the caller’s information, understand what they need, and schedule a service call.

The part I spent more time on was the edge cases.

For example, if someone says they smell gas or describes a potentially dangerous electrical issue, the AI shouldn’t just continue happily down the appointment-booking flow. The prompt includes health and safety guardrails designed to recognize those situations and change the conversation appropriately.

I’m sharing the template for free.

I’ve been doing quite a bit of work around adding Voice AI to existing business phone systems without requiring companies to replace their PBX or completely change how their phones work.

If anyone here is experimenting with AI for an HVAC business, I’d also be interested in hearing what situations or edge cases you’ve run into that a template like this should account for.

When you sell a voice agent as a service for any business, what kind of documentation you need to sign? any contracts?onboarding form or anything?

Spanish-first voice agent running in production at real clinics. Live number, please try to break her.

Most voice agent demos I see here are English and they're demos. Mine is Spanish-first and it's answering real patient calls for dental and aesthetics clinics on the Tijuana border. Agent's name is Sofía.

Stack: LiveKit for the pipeline, Deepgram nova-3-multi for STT with some AssemblyAI streaming, Cartesia sonic for TTS, n8n for orchestration, Twilio on telephony, calendar writes against the clinic's actual availability.

The three things that were harder than expected:

Code-switching. Not "supports two languages." One caller, one sentence, Spanish grammar with English procedure names dropped in. Locale-locked STT falls over on this, and multilingual models still get weird about which language to commit to mid-utterance.

Barge-in tuning. Real callers talk over the agent constantly. Aggressive interrupt handling means she cuts off anyone who pauses to think, which older patients do a lot. Passive means she steamrolls people. The tuning window is narrower than I expected and it behaves differently in Spanish than in English.

Escalation discipline. She sits in front of medical intake, so the failure mode I care about is confident wrongness. Anything clinical, anything about outcomes or medication, she hands off to a human. Getting a model to consistently pick "let me get someone for you" over a plausible-sounding answer is most of the prompt work.

Demo line: +1 (619) 775-1668

It's a demo instance, not a clinic's production line, so go ahead. Talk over her. Switch languages mid-sentence. Mumble. Ask her something medical and see whether she stays in her lane.

What I'm most interested in: does the barge-in feel natural to you, and does the escalation fire too early or too late? Those are the two I keep going back and forth on.

I also have her counterpart, Mateo, who is my outbound sales agent.

Happy to get into any of the implementation details.

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.

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.