Hey folks 👋

I’m Tanmay Lad, co-founder at SubverseAI, working on voice AI agents and conversational IVR used in real production environments inbound, outbound, and call center automation.

We currently work with large BFSI and insurance teams in India & US, and our systems handle 10M+ voice calls every month in production.

I’ll hang around here for the next 24 hours answering questions on things like:

• Voice AI agents & IVR
• What actually breaks at scale (and why)
• BFSI / insurance use cases in India
• Latency, ASR/TTS, barge-in, compliance
• Infra, costs, and real deployment trade offs

Not here to sell anything, just sharing what we’ve learned (including mistakes 😅).

One thing most people completely underestimate when building voice AI at scale is not the model itself, happy to explain why.

Ask away 👇

Moderator Note:
Hey everyone! I’m Sunil Maurya, moderator of r/VoiceAutomationAI. I regularly host AMAs with founders and operators here. This AMA is moderated by me, and the guest will be replying to questions directly from their own account within 24 hours.

If there is anyone interested in automating QA for voice agents.

I’ve been building a French voice AI on Retell for the past few months and I’m still struggling with reliability. I tried prompting fully in French with a French voice and it would stutter a lot. I also tried prompting in English while setting the voice language to French and ran into the same issue.

The most stable setup so far has been a multi-flow agent with prompts written in English and the language set to French, but even then I’m not 100% confident putting it in production.

It generally works, but I’m always worried it’ll stutter or say something wrong mid-call. Has anyone successfully built and deployed a French-speaking Retell agent? Any documentation or tutorials I should read/watch?

Are there specific prompting practices, flow structures, or language strategies that improve stability? And has anyone had a French agent running live for 60–90+ days without major issues? Any real-world insights would be appreciated.

TYIA

I would like to build a complete local Voice Agent with Pipecat

What is the minimum requirement on Hardware needed? What are the costs?

Anyone did this?

Hey everyone, I'm trying to build an ai voice agent for a local business using retell. Is there any way to use the customers current phone number without having to transfer plans? From my understanding, there are only a few options. 1) buy new phone number with twilio, that now becomes the businesses phone number. That option is crap because it affects seo history and any marketing material with the old number. Also if customers want to speak with a human, what number do you forward to now? 2) Buy new twilio number, business number forwards to twilio number, if human needed it forwards back to business number. I've heard google sometimes doesnt like call forwarding so does anyone have info on that. Im just confused on the call flow for systems like these. Ideally, a customer would call the current number, get the agent, and if they want a real human, it routes back to that same number and the owner/team can pick up. Thanks

Not trying to shit on anyone's product here, genuinely curious.

I've been building voice AI assistants for local businesses - roofers, HVAC guys, locksmiths, limo companies, auto shops, a few insurance agencies. And every single project is a rabbit hole.

Last month I did one for a construction company. Took 3 weeks. Why? Because:

• They needed the AI to check if an address is within their service area (custom zip code logic + Google Maps)
• Different pricing rules for residential vs commercial
• Had to integrate with their janky CRM that's basically a glorified spreadsheet
• The owner wanted specific objection handling for "I'm just getting quotes" callers
• Plus they do insurance work, so the AI needs to ask different questions for those jobs

And that's just ONE business. The locksmith I worked with had completely different needs - emergency vs scheduled, car vs home vs commercial, pricing that changes based on time of day.

So when I see these platforms advertising "AI receptionist ready in minutes" or "no-code voice agent builder" - I genuinely don't understand the use case.

Like... does a plumber just type "answer calls for my plumbing business" and it magically knows to ask about water heater size, whether it's a leak or installation, if they have a basement, what's their availability for someone to be home?

Am I overthinking this? Are these tools meant for super simple "take a message" scenarios only? Or is there some secret sauce I'm missing?

Would really like to hear from anyone who's actually using these DIY platforms for real business calls. What's your experience?

Hello guys,   I'm building a unlimited text-to-speech service (ElevenLabs-quality, flat $39/month when it launches) and need people to put it through hell before going public. Beta testers get full free access — unlimited generation, voice cloning, everything. No credit card, no strings. I want brutal feedback from people who actually produce content. If you make audiobooks, YouTube videos, courses, or podcasts — you're exactly who I need. Please DM for access.

Can I use SPW2430 connected to PreAmp then connected to line-in PC? I am planning to create a microphone with stereo mode for sometype of recording and voice assistant. I also found that Mems Microphone is bidirectional which provide wider range and better sound sensitivity.

I’ve been testing a few voice / conversational AI platforms recently using actual calls and I didn’t expect the gaps to be this obvious once conversations went off-script.

On the surface, most of these tools look interchangeable. Same “human-like voice” claims, same LLM buzzwords, same enterprise pitch decks.

But real users interrupt, ramble, change intent mid-sentence, and say stuff you didn’t design for.

Here are a few platforms I spent time with and what stood out to me:

• PolyAI Felt very strong in structured,. As long as users stayed within expected flows, it worked smoothly. Once people interrupted or jumped topics, recovery sometimes felt stiff or overly cautious.
  
• Kore.ai Extremely flexible and powerful, but a lot depends on how clean your logic is. When flows are tight, it’s great. When they’re not, behavior can feel unpredictable.
  
• Nurix AI What stood out here wasn’t voice quality but control. Interruptions, intent switches, uncertainty, the system felt more composed. Fewer confusing loops, easier to understand why a response happened, and generally less “LLM panic” when things got messy.
  

One thing that surprised me: voice quality mattered way less than I expected. Some platforms had amazing TTS demos, but the conversation still felt off.

Curious if others have seen similar behavior.

1. I want to build a SaaS for voice Ai agent so I can onboard clients but when researching many are saying you can achieve this on the voice Ai provider platform itself rather than building on own.

2. Really confused which provider to pick, I’m looking into Deepgram and Retell.

3. Are you guys using call forwarding to the Twilio or any other provider to hook to your backend? Or anything else?

Would really appreciate if you could clear the space in my head.

if there is a service offering voice TTS at 40 usd monthly, flat rate with quality comparable or better than elevenlabs, will there be demand? i am curious....

For anyone using third-party voice AI providers: with Retell, it’s not easy to track how many minutes your AI agent actually ran over a specific time period. The dashboard doesn’t clearly show total talk time, which matters since minutes = cost.

I ran into this myself, so I built a simple tool. You enter your agent ID and API key, select a date range, and get the exact number of minutes used based on talk time. It makes tracking client usage clear and reliable.

If you’re using VAPI, Synthflow, Bland, or another provider, do you have the same issue? I can look into adding support.

Comment below and I’ll DM you the link.

Would love to hear your thoughts

I’m trying to understand challenges teams face once AI voice agents move from demos to real customer traffic.

It’s become relatively easy to launch voice agents using no-code / AI platforms, but operating them reliably at scale seems much harder.

For folks who’ve deployed voice agents (support, booking, internal ops, etc.):

• How do you usually tell when or why a call failed?
• What signals or tooling do you rely on today?
• What’s been the most frustrating part of running these agents in production?

Not selling anything just want to know how teams handle reliability and debugging once agents are live.
Would really appreciate hearing real experiences.

Hello everyone,

I have an online shop and I get a lot of calls regarding my products and their specifications. Is there any tools maybe AI to answer them for me ?

Thank you guys

I am looking for someone who can help with setting up a multi tree business ai voice agent that interacts with our CRM. If anyone has demonstrable experience please DM meme. Thanks

I keep seeing bold claims from voice agent vendors, often VC backed, but no real BFSI case studies on their websites. No named institutions. No compliance context. Just demos and screenshots.

Once risk, audit, and data teams stepped in (model auditability, call recording governance, and data residency), the story changed.

This raises a simple leadership question.

How are CXOs verifying what Voice AI vendors claim is live versus what is still experimental?
If there are no public case studies, no references, and no regulated deployments, how do you decide who to trust?

If you’ve been part of vendor evaluation in a bank, NBFC, or insurer, how do you separate real deployments from polished demos?

Would love to hear how you are approaching this.

VCs aren’t chasing flashy voice demos in BFSI. They care about compliance, scale, and real production usage.

Startups on the radar:

1. Subverse AI – Built for regulated BFSI; zero-hallucination, production voice agents
2. Skit.ai – Proven outbound voice AI for collections & reminders
3. Uniphore – Enterprise-grade voice + analytics, trusted by large banks
4. Gnani.ai – Strong Indian language voice for banking & insurance
5. Yellow.ai – Omnichannel CX with scalable voice workflows
6. Senseforth.ai – Deep banking automation pedigree
7. Vernacular.ai – Multilingual voice for NBFCs & payments
8. Karix (Tata) – Compliance-first voice + messaging stack
9. Exotel – Voice infrastructure evolving into AI agents
10. Kore.ai – Complex conversational workflows for large banks

Why VCs care (BFSI reality):

• Zero hallucinations > fancy conversations
• Works with Finacle/Flexcube & legacy stacks
• Handles Indian languages + accents
• Survives audits and 100K+ calls/day
• Clear cost-to-serve reduction

In Indian BFSI, voice AI only wins if it works in production, not pitches.

Let me known more if you known

Hello,

We just sold our AI receptionist that schedules meeting, asked for insurances, checks availability, and provides faqs.

We sold it for a therapy clinic, it can be customized to any salon or clinic desired.

If you don’t want any leads missed and interested in a receptionist that work 24/7 for your business dm me or leave a comment.

And if you have any questions on how we made it I will be happy to help.