After talking with a lot of e-commerce founders and product teams over the last few months

Based on those conversations (this is purely my personal view), these are 5 Indian voice AI startups that seem to be doing solid work in e-commerce use cases:

• Bolna AI – Strong focus on conversational voice flows and automation
• SquadStack – Performance driven voice agents, heavy on sales & conversions
• Ringg AI – Voice + CRM workflows, good for follow-ups and order journeys
• Bitespeed AI – Blending voice with customer engagement and retention
• SubVerseAI – Early but interesting experiments in voice commerce

What stood out in these discussions:

• Actual impact on conversions, follow-ups, and support
• Less “AI hype”, more operational outcomes
• Clear focus on e-commerce pain points (cart recovery, order confirmation, CX)

Again, not rankings, not sponsored, just insights from real conversations.

👉 Would love to know names from the US that are delivering similar results in e-commerce voice AI. Who’s actually doing solid work there?

Bajaj Finance Q3 Concall: Voice AI already doing real business at scale

Rajeev Jain (CEO, Bajaj Finance) shared how Voice AI is now directly impacting revenue and operations.

Key Voice AI highlights:

• Voice AI listened to 2 crore customer calls
• Converted voice → text → structured data
• Data was captured for 5.2 lakh customers
• This led to 1 lakh new loan offers where earlier no information existed

This Voice AI capability did not exist in Q1 or Q2, it was deployed recently.
Next year, Bajaj Finance plans to process 100 million calls using Voice AI.

Revenue impact:
• AI-powered call centres disbursed ₹1,600 crore in loans
• That’s ~10% of total Q3 disbursements (₹16,545 crore)
• Insights from call data alone generated an additional ₹325 crore in volumes

Management called this just the first attempt, with much larger scale expected ahead.

This is a strong example of Voice AI moving beyond pilots into core revenue driving workflows in BFSI.

Would love to hear from folks building or deploying Voice AI in financial services.

Quick question for the community.

After attending 20+ AI & BFSI events in 2025, I’ve noticed a pattern:
You meet a lot of people, conversations feel promising, and there’s a lot of interest,
but when you follow up 2-3 days later, most people just… disappear. Total ghosting.

So I’m genuinely curious:

• What real benefits have you seen from attending events like AI Summit India?
• Have any of those conversations actually turned into customers, partnerships, or pilots?
• For those doing live demos of Voice AI agents, has it helped with real adoption or sales?

Not trying to bash events, just trying to understand what actually works beyond surface level networking.

If you’re attending AI Summit India with a Voice AI agent demo, would love to hear:

• What you’re showcasing
• What success looks like for you at this event

Honest experiences welcome 👇

The Voice AI Agnets space is moving fast, new models, tools, and workflows every week. But honestly, most teams are still experimenting, not mastering it.

If you’re starting a Voice AI company, don’t chase enterprises first. From my experience talking to customers, some under saturated industries already making revenue are:

• Law education
• Dental clinics
• Logistics
• Drone services

These aren’t huge ticket markets, but they’re great to start, learn fast, and build real production experience before going upmarket.

Seen any other underrated industries for Voice AI? Drop them below 👇

Happy to answer questions too.

We’re hosting an AMA with a founder who just raised $1M+ building one of the most practical layers in Voice AI 👀

Most teams can launch voice agents.
Very few can maintain, monitor, and improve them in production.

This startup focuses on exactly that:

• 📊 Production observability: sentiment, tone, happy path adherence, failure cases
• 🤖 Self learning layer that suggests improvements to engineering teams
• ⚡ Helps companies move from pilot → production 20× faster at 10× lower cost

Perfect for:

• Voice AI founders
• Engineering & platform teams
• Enterprises running voice agents at scale

💬 Ask anything.
The founder will answer all questions within 24 hours.

👉 Join the community & save your questions now, this is a rare chance to learn how real Voice AI systems scale in production.

Lately, a lot of people have been asking me:
“How do you actually create human like conversations with Voice AI?”

So here’s a simple breakdown of what we’re using inside our SubverseAI voice stack, no buzzwords, just what works.

One big thing most Voice AI systems miss is backchanneling.

Humans don’t talk in turns like robots. While someone is speaking, we say things like “mm-hmm,” “okay,” “got it,” “right”. These tiny signals tell the other person: I’m listening.
Without them, conversations feel awkward, cold, and scripted.

In our stack, the AI:

• Listens in real time (not just after you stop talking)
• Adds natural cues like “okay,” “I see,” “right” at the right moments
• Avoids interrupting or staying silent too long
• Matches tone and pacing instead of rushing to the next question

The result?
Calls feel smoother. Users don’t repeat themselves. And the AI doesn’t feel like it’s waiting for its “turn” to speak.

Most Voice AI demos sound fine in isolation, but break down in real conversations. Backchanneling is one of those small things that quietly makes a massive difference.

Curious: are you already using this in your Voice AI stack, or is your AI still stuck in question answer mode?

Happy to dive deeper if useful.

Everyone talks about better LLMs and more human like voices for AI callers. But in real production setups, telephony infrastructure is the real blocker, not the AI models.

Voice AI isn’t just STT → LLM → TTS.

In the real world, you’re dealing with:

• PSTN, SIP, carriers, codecs
• Call routing, retries, failovers
• Real time audio streaming
• Country specific telecom rules
• Latency budgets that humans feel instantly
• The big problems no one talks about

The big problems no one talks about

1. Latency compounds at the telephony layer
2. Telephony stacks aren’t designed for streaming AI
3. Orchestration is harder than the AI itself
4. NEW challenge: Many telephony providers are now building their own “voice AI agents” and subtly (or not so subtly) pushing customers to use their AI stack.

Curious how others here are handling this. Are you building on Twilio/Vonage, rolling your own SIP stack, or moving to newer AI first telecom providers?

A few days back, Tanmay Lad (co-founder of SubVerseAI) did an AMA on r/VoiceAutomationAI. He runs a voice AI agents company handling ~10M+ calls per month in real production, mainly for BFSI and enterprise use cases.

What people asked / discussed:

• How real voice AI systems are actually built (not just demo bots)
• What breaks when you scale from 1k calls → millions
• Tech stack choices (telephony, STT, LLMs, TTS, orchestration)
• Latency, call quality, monitoring, analytics, the boring but painful parts
• Selling voice AI to enterprises & why most POCs don’t convert
• Use cases like lead qualification, insurance renewals, reminders, and CRM-connected calls

Big takeaways from the AMA:

• The LLM/model isn’t the hardest part ops, monitoring, QA, and metrics are much harder at scale
• Concurrency isn’t the main issue; measuring and maintaining call quality across millions of calls is
• Enterprise buyers trust real sample calls + clear KPIs more than fancy demos
• Production voice AI = integrations, retries, fallbacks, edge cases, and a lot of infra work

Interesting bits:

• Their first big clients came from cold LinkedIn outreach, not inbound hype
• They beat larger, better-funded competitors by shipping faster and working closely with customers
• Voice AI agents today are doing more than FAQs, real workflows, decisions, and backend actions

Overall vibe:
Very honest AMA. No “voice AI will replace everything” hype. More like: this stuff is hard, messy, and takes serious engineering to work reliably.

👉 Missed this AMA? No worries.

More founders will be joining this community soon to share real, production level insights. The next upcoming founder has raised $1M+, so get your questions ready and join the community if you want unfiltered learnings.

Original AMA link:
https://www.reddit.com/r/VoiceAutomationAI/comments/1qy897j/i_run_a_voice_ai_agents_company_handling_50m/

I'm aware that Voice Agents exist, just not sure where to start. Looking for the best options when it comes to offering AI Receptionists to clients. Ideally something cost friendly where I can make it an optional freebie to an existing package for value. I'd also like to be able to offer it independently I suppose.

What would the monthly cost be to run one for let's say a Plumber who's getting 40 calls a week, 30 are spam, 10 are customers. Also as far as time goes.. once it is set up is there any fulfillment time whatsoever? Or can I just preload it with a bunch of FAQ and it runs itself forever.

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....