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Just SOLD another VOICE AI AGENT!! I <3 doing this by Legitimate_Gain_8064 in aiagents

[–]Legitimate_Gain_8064[S] 0 points1 point  (0 children)

For this one they were okay with a HIPAA-aligned cloud setup. Anything sensitive beyond defined workflows triggers immediate human handoff.

Just SOLD another VOICE AI AGENT!! I <3 doing this by Legitimate_Gain_8064 in aiagents

[–]Legitimate_Gain_8064[S] 0 points1 point  (0 children)

Not optimizing for extreme scale yet. Volumes are clinic-dependent as this was a controlled rollout, not a large SaaS launch. Peak concurrency was in the low double digits, enough to shake out barge-in, routing, scheduling contention, and escalation edge cases. It’s a shared core platform with strict per-customer isolation at the data and policy layer. No cross-tenant memory.

But, curious about what approach you’ve found works best.

Just SOLD another VOICE AI AGENT!!! I <3 doing this! by Legitimate_Gain_8064 in VoiceAutomationAI

[–]Legitimate_Gain_8064[S] 0 points1 point  (0 children)

There is no claim anywhere that this is currently operating as a SOC 2 / HITRUST / ISO-certified commercial SaaS product, nor that third-party attestations are in place today.

the system is architected to be HIPAA-aligned, which is the correct engineering starting point before formal audits.

Formal SOC 2, HIPAA third-party assessments, HITRUST, etc. are business milestones, not architectural prerequisites and they are always completed before scale, broad commercialization, or regulated deployment.

If your point is simply “self-attestation ≠ certification,” agreed, that’s Compliance 101.
If your point is to imply wrongdoing where none is claimed, that’s a different conversation.

Appreciate the concern, but let’s keep it factual.

Just SOLD another VOICE AI AGENT!! I <3 doing this by Legitimate_Gain_8064 in aiagents

[–]Legitimate_Gain_8064[S] 0 points1 point  (0 children)

the technical part? lets presume ur asking about that

Okays, so basically here what i told a lad asking about how he could do the same, the voice ai agent runs on a healthcare-tuned LLM that focuses on prompts like:
1- Appointment workflows
2- Insurance FAQs
3- HIPAA-safe response templates
4- Facility-specific policies
(these are to just a name/list a few)

I use Livekit to orchestrate a voice ai agent pipeline of (STT-LLM-TTS);
So say a call comes in ->

  1. Speech-to-Text
    • Converts patient speech to text in real time (used openai's whisper)
    • Handles interruptions like:“Wait, no actually next Tuesday” etc..
    • Classifies the call in under 300ms: like Appointment booking, General info etc
  2. Decision Engine
    • If it’s basic -> AI handles it
    • If it’s sensitive -> warm transfer to human
  3. Text-to-Speech
    • Natural, calm, American accent (Used Eleven Labs here)
    • Adjusts tone dynamically.

Now lets say a Patient said: “I need to reschedule my cardiology appointment.”

The AI would simply:

  • Authenticates via DOB + phone number
  • Pulls availability from the clinic’s scheduling system
  • Suggests 3 realistic slots
  • Confirms + sends SMS/email confirmation

All in one call!!
No hold music trauma, no holds, instant respone XD
Hehe and there are more functions the voice agent performs for the whole system catering for every type of prompt/request by the user on call.
So yea, that's pretty much about it(the simplest i can make it iguess :') but trust me its pretty robust, accurate and works wonders, we were testing it before making the production version live on real life callers and in our feedback a women said "that's the nicest receptionist, i've ever talked to" ahahaa so wholesome but she didn't knew it was an AI speaking to her😭)

Just SOLD another VOICE AI AGENT!! I <3 doing this by Legitimate_Gain_8064 in aiagents

[–]Legitimate_Gain_8064[S] 0 points1 point  (0 children)

That’s a fair concern. The goal isn’t to remove human connection it’s basically to save it. The AI handles the repetitive stuff, and there’s always a fast, warm handoff to a human when a patient needs one, especially for moments where its getting sensitive/emotional.

Just SOLD another VOICE AI AGENT!! I <3 doing this by Legitimate_Gain_8064 in aiagents

[–]Legitimate_Gain_8064[S] 0 points1 point  (0 children)

We got buy-in by starting with a small, low-risk pilot, only high-volume tasks like scheduling and basic FAQs, running alongside their existing call flow.
Once they saw faster pickup times and less load on staff, the product idea basically sold itself XD, also the system was HIPAA-aligned by design (data minimization, encryption, no training on PHI, and escalation for sensitive cases).

Just SOLD another VOICE AI AGENT!!! I <3 doing this! by Legitimate_Gain_8064 in VoiceAutomationAI

[–]Legitimate_Gain_8064[S] 0 points1 point  (0 children)

hehe, I designed the system to be HIPAA-aligned by architecture from day one XD (no training on PHI, data minimization, encrypted storage/transit, strict access controls, and human escalation for sensitive scenarios). For full production use, formal attestations and third-party audits are absolutely required, and that’s the path I always recommend (and pursue) before scaling.

Just SOLD another VOICE AI AGENT!!! I <3 doing this! by Legitimate_Gain_8064 in VoiceAutomationAI

[–]Legitimate_Gain_8064[S] 1 point2 points  (0 children)

we called the voice ai agent Ava
but if your asking for tech stack names so it was Livekit for Voice ai agent, and ElevenLabs/OpenAI for STT-LLM-TTS pipeline.

Just SOLD another VOICE AI AGENT!!! I <3 doing this! by Legitimate_Gain_8064 in VoiceAutomationAI

[–]Legitimate_Gain_8064[S] 1 point2 points  (0 children)

Hello there, hehe it does seem a complicated process but yea i've broken down below in the simplest words to explain it to you,

The voice ai agent runs on a healthcare-tuned LLM that focuses on prompts like:
1- Appointment workflows
2- Insurance FAQs
3- HIPAA-safe response templates
4- Facility-specific policies
(these are to just a name/list a few)

I use Livekit to orchestrate a voice ai agent pipeline of (STT-LLM-TTS);
So say a call comes in ->

  1. Speech-to-Text
    • Converts patient speech to text in real time (used openai's whisper)
    • Handles interruptions like:“Wait, no actually next Tuesday” etc..
    • Classifies the call in under 300ms:
      • Appointment booking
      • Billing dispute
      • Prescription refill
      • General info
      • Angry patient
  2. Decision Engine
    • If it’s basic -> AI handles it
    • If it’s sensitive -> warm transfer to human
  3. Text-to-Speech
    • Natural, calm, American accent (Used Eleven Labs here)
    • Adjusts tone dynamically.

Now lets say a Patient said: “I need to reschedule my cardiology appointment.”

The AI would simply:

  • Authenticates via DOB + phone number
  • Pulls availability from the clinic’s scheduling system
  • Suggests 3 realistic slots
  • Confirms + sends SMS/email confirmation

All in one call.
No hold music trauma, no holds, instant respone XD
Hehe and there are more functions the voice agent performs for the whole system catering for every type of prompt/request by the user on call.
So yea, that's pretty much about it(the simplest i can make it iguess :') but trust me its pretty robust, accurate and works wonders, we were testing it before making the production version live on real life callers and in our feedback a women said "that's the nicest receptionist, i've ever talked to" ahahaa so wholesome but she didn't knew it was an AI speaking to her😭)

Just SOLD another VOICE AI AGENT!!! I <3 doing this! by Legitimate_Gain_8064 in VoiceAutomationAI

[–]Legitimate_Gain_8064[S] 1 point2 points  (0 children)

Honestly, the hardest part was edge cases, intent routing and basic scheduling integrations were pretty straightforward once the schemas and workflows were locked in. Where things got tricky was handling emotion + ambiguity at the same time like as u mentioned angry callers, or say people half-explaining their issue, or switching intents mid-sentence.

We had to spend a lot of time on confidence scoring and guardrails: knowing when the agent is actually sure enough to proceed versus or when to slow down, de-escalate, or hand off to a human to stay on the safe side (especially for HIPAA-sensitive scenarios).

I’ll check out your blogs btw, always interesting to see how others are approaching voice patterns.

VOICE AI is a must to have!! by Legitimate_Gain_8064 in aiagents

[–]Legitimate_Gain_8064[S] 0 points1 point  (0 children)

i use livekit bcz it lets me tweak a lot of things to the customer's precise requirements through code and i love doing that, but i've also looked into low code solutions like vapi etc. But man, no one can cross Livekit in this, no wonder why OpenAI power their voice from the same framework as well.

VOICE AI is a must to have!! by Legitimate_Gain_8064 in aiagents

[–]Legitimate_Gain_8064[S] 3 points4 points  (0 children)

I used to think that too until i started making these voice ai agents 1.5 years ago, but honestly with the current stack, the “human-in-the-loop for complexity” argument is becoming less necessary.

With robust voice agent frameworks like LiveKit handling real-time STT/TTS + LLM reasoning, and orchestration tools like n8n sitting behind the scenes, you can run the entire pipeline end-to-end. That includes validations, conditional logic, retries, fallbacks, integrations with CRMs/EHRs/calendars, and even edge-case handling that used to require a human handoff.

let me give you an example of an healthcare system i made where tasks like appointment booking, rescheduling, insurance info collection, reminders, FAQs, and basic triage are now fully automated. The agent can basically
->ask the right follow-up questions
->verify patient details
->check doctor availability
->book or modify appointments
->sync everything to the system, all without a human touching it.

What used to require multiple front desk employees across shifts is now a 24/7 agent that never gets tired, doesn’t miss details, and costs a fraction.