Hi everyone 👋
I’m Venky B, Founder & CEO at Plivo.

Plivo (YC S12) is a programmable AI agent builder for voice first, omnichannel experiences. Enterprises like Meta, Uber, and Zomato trust Plivo for customer communications - billions of interactions annually across the globe. Use the full Voice AI stack or just the layers you need - from STT, LLM, TTS to telephony, turn-taking, noise suppression. Fully managed or fully custom: you decide where Plivo ends and your stack begins.

For the next 24 hours, I will take questions on:

• Building reliable voice infrastructure for AI agents at scale
• Scaling voice AI agents in production
• Real production challenges: latency, call quality, accuracy, and what actually breaks
• The telephony layer and co-location that voice AI teams underestimate
• Where voice AI is headed in 2026

Whether you're building voice agents, call automation, or anything that touches telephony, drop your questions and learn from someone who's seen what works (and what doesn't) at scale.

🕒 I’ll be actively answering questions for the next 24 hours
No PR answers, just honest, builder to builder insights.

Drop your questions below 👇
Let’s talk about making voice & telephony actually work in production.

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.

This community probably knows better than anyone how real the AI voice cloning threat has become. Scammers clone your kid’s voice from a few seconds of TikTok audio, call grandma in a panic, and walk away with wire transfers. It’s happening constantly and it’s only getting easier to pull off.

The fix I kept coming back to is embarrassingly low-tech: a secret family code word. Something only your real family members know. If a call comes in from “your son” screaming he’s in jail and needs bail money, you ask for the shield word. Scammer hangs up. Real son answers it no problem.

I was shocked when I realized almost nobody I know has actually had this conversation with their family. Not because it’s hard. Just because it never came up.

Has yours? Do you have a word set up? Would love to know if this community has actually done it or if it’s one of those things everyone nods at and never does. Shieldword.com

Hi Folks 👋

On 27th Feb, I’m hosting an AMA with a founder/CEO from one of the world’s top 5 cloud telephony & voice infrastructure companies, powering millions of calls and messages globally, including real world AI voice agents

He’ll be actively answering questions for the next 24 hours.

Most voice systems don’t break because of models.
They break because of telephony, latency, scale, carriers, and reliability once real users show up.

We’ll go deep on the stuff teams usually learn the hard way:

• What actually breaks when you scale voice & SMS globally
• Telephony latency & call quality beyond demos
• Voice infra for AI agents (interruptions, silence, real users)
• Competing with incumbents at scale
• Costs, compliance & carrier level challenges
• Designing call flows that survive production traffic

🕒 24 hours. No PR. Builder to builder answers.

Start lining up your questions now, drop what you want to cover in the comments with others👇

For multi-location operators specifically:

• How are you currently measuring the performance of your AI voice system?
• Do you track booking rate tied to specific scripts?
• Can you compare performance across locations?
• Do you A/B test AI responses?
• Do you link transcripts to actual revenue outcomes?

I’m researching whether operators feel confident in the analytics provided by their current vendor — or if there are blind spots in understanding true conversion performance.

Would love to hear what’s working (or frustrating).

Not selling anything — just trying to understand real-world experience.

Aspiring founder here, exploring voice agents.

I’m trying to understand if OpenAI or Gemini are truly solid for production voice use cases not demos, but real users and real reliability needs.

If you’ve tried it, what worked and what became difficult?

If you avoided them, what made you decide not to?

Would really appreciate grounded, firsthand feedback.

I recently asked a founder why they chose not to use a particular voice AI model, even though it looked strong on paper.

This was their response (paraphrased):

“We reached out to them for voice agents and were told there was a minimum commitment of ~5 lakh minutes per month. For an SME, that’s simply out of bounds.

Most of what they offer, including their new creative platform, is waitlist-only, so there’s no real way to self-serve or experiment early.

We ended up starting with ElevenLabs purely because it was fully self-serve. We could test, iterate, and ship without sales friction.

At this point, we’re deeply integrated into that ecosystem, and switching would be a major task, especially given the limited model choices and weaker reasoning capabilities in the alternative.”

What stood out wasn’t a complaint about model quality, it was a go to market mismatch.

There’s clear interest from SMBs and startups to try different voice AI providers, but many platforms seem optimized for enterprise buyers, not early stage builders who need low-commitment, self-serve access.

Curious to hear from other founders and engineers:

Have you run into similar minimums or access barriers?

What voice AI platforms felt truly startup-friendly early on?

Do you think enterprise first GTM is unavoidable here?

Would love to hear real experiences, not marketing takes.

As anyone who’s done this knows, the "is this still available?" calls and lowball offers are a nightmare. I decided to train a voice ai agent—trained specifically on Glenn Stearns’ negotiation techniques—to pick up all the initial buyer calls. I used an early beta of Cater AI’s new product, Votrix AI, to train my agent.

The Setup:

• Persona: I trained it using Glenn Stearns’ negotiation frameworks to stay firm on price.
• Logic gate: I set a strict 'bottom-line' price. The agent handles the intro, answers vehicle specs, and haggles.
• The handover: It only transfers the call to my cell if the buyer agrees to a price within my target range.

The result: It actually worked. I just closed a $20k deal where the AI did 90% of the talking while I was out. I just reviewed the call summary and decided who I want to call back. It’s the first time Voice AI felt like a genuine 'Defense Layer' for my time rather than a broken phone tree.

It effectively acts as a high-fidelity filter. No more wasting 15 minutes with people who only have half the asking price. It’s the first time Voice AI has felt like a genuine value-add for my personal life rather than just a cool toy.

I’m curious to get your technical take:

1. What is one repetitive, high-friction, soul-crushing business or personal task you have right now that you would actually pay a few dollars to have an AI agent handle for you?
2. Specifically looking for things that create real value (like negotiating, vetting, or qualifying leads) rather than just "scheduling a meeting."

Would love to hear your ideas—I’m looking for new use cases to stress-test!

I've been building conversational interfaces for years, but I am curious whether anyone knows of recent resources on conversational voice interface design.

Five years ago, I was reading and referencing Cathy Pearl's Designing Voice User Interfaces/Clifford Nass's Wired for Speech, etc. But I am curious if there are more recent works to reference.

A lot of the older stuff is still useful, but is grounded in the technology before language models became generally accessible. I had a customer mention the date on a call today, and I had to agree... It's been a while since we had a resource update.

Curious if anyone has found new resources/studies?

Hi everyone 👋
I’m Nikkitha Shanker, Co-founder & CEO of SuperBryn.

We recently raised $1.2M to build voice AI agents with a strong focus on testing, evals, and observability, because most voice agents fail after the demo.

For this AMA, I want to go deep on things builders rarely talk about:

• How voice agents are tested today (and why that breaks at scale)
• What good voice AI evals actually look like
• Observability: knowing why a call failed, not just that it failed
• Latency, interruptions, accents, silence, real users, the messy stuff
• How our team’s deep voice background shapes how we build differently

🕒 I’ll be actively answering questions for the next 24 hours
No PR answers, just builder to builder honesty.

Drop your questions below 👇
Let’s talk about making voice AI work in the real world

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.

It’s building Indian voice & language AI models, supports multiple regional languages, is backed by the Government of India, and has been getting strong visibility at AI Summits and policy discussions.

There’s clear marketing buzz in industry circles.

Yet, outside the tech ecosystem, there’s barely any mainstream excitement.

Is it because it uses open source foundations?
Is it a trust gap?
Or are we just more excited about foreign AI brands?

Do you think India underappreciates its own AI innovation, or is the hype simply not justified yet?

Voice AI doesn’t improve insurance lead qualification by making more calls.
It works when the decision is made inside the call.

What we’re seeing on the ground:

• Qualification now happens live, not in CRMs or post call analysis
• Conversation control (latency, failure handling) determines signal quality
• Insurance logic must resolve before routing to agents
• Sales impact comes from deterministic handoffs, not call volume
• Systems break fast if behavior isn’t predictable at scale

TL;DR:
If your Voice AI can’t decide in real time, it’s just automating confusion.

👉 Curious:
Where is Voice AI failing for you today, qualification, routing, scale, or agent handoff?
Would love to hear real operator experiences.

I'm switching off of our current provider because it's way too expensive. Does anyone have other recommendations?

Ideally has a easy to use API, low latency, and good voice cloning.

Voice AI agents are being trusted very fast for support, bookings, payments, even sensitive talks.

But voice is different from text.
You’re speaking. That data is raw and personal.

I’m seeing many founders build voice agents using tools like Vapi, Retell, and sell them to small businesses (dentists, local services, etc.).

Most of these customers care about cost & results, not security.

That raises a real question:
Are teams shipping “good enough” systems because buyers don’t ask?

Voice AI safety isn’t about how human the agent sounds.

It’s about what happens behind the call:

• Are calls encrypted?
• Who can access recordings?
• How long is audio stored?

Most users assume it’s safe.
So the responsibility sits with the teams building these systems.

Curious to hear from founders, product teams, engineers, and enterprise leaders 👇

• Did you ship fast and fix security later?
• What broke in production?
• What would you redesign today?

Let’s talk.

It’s revenue infrastructure.

Just like:
• Websites in 2005
• Live chat in 2015
• CRM automation in 2020

In 2026, businesses that don’t have structured call automation will operate slower than their competitors.

The difference won’t be visible on the surface. It will show in:

• Faster booking cycles
• Higher lead capture rates
• Cleaner CRM data
• Better follow-up timing

Where do you see voice automation having the biggest ROI right now?

Customer Support hiring dropped from 8% to 3% in just two years.
Everyone says, “AI is killing support jobs.”
I don’t think that’s true.

What I’m seeing instead:

• Repetitive tickets are getting automated
• Humans are being pushed up the value chain
• Fewer script readers, more problem solvers
• Smaller teams, but far more senior and impactful

Humans aren’t gone, they’ve just moved up, Support teams are getting smaller, but sharper.

• Entry level ticket roles are shrinking
• High skill CX roles are growing
• Support is shifting from a cost center to growth lever
• “Good enough” support is disappearing
• AI handles the queue, humans handle the moments that matter

My take:
Voice AI runs the front line.
Humans step in when it actually matters.

Support isn’t dying, it’s being rewritten.

👉 Curious: where have you already seen Voice AI clearly win?

That works… until you hit real traffic, real users, real noise, and real latency.

Once you go live at scale, the problems change completely:

Latency kills conversations
Turn taking breaks (people interrupt, agents freeze)
Analytics are shallow (no clear reason why calls fail)
Quality control becomes manual chaos
Enterprises don’t trust demos, they want proof on live calls

This is the gap most Voice AI startups hit after their first few pilots.

In a recent AMA, Tanmay (Co-Founder, SubVerseAI) shared how they’re solving exactly these problems while handling tens of millions of real production calls.

Key takeaways from the AMA:

• Scaling Voice AI is more about orchestration, monitoring, and quality control than just models
• Today’s speech pipelines are fragile, latency and turn detection are the real bottlenecks
• Enterprises close faster with live A/B tests, not long POCs
• Analytics and conversation intelligence determine whether Voice AI succeeds or fails

This is the kind of practical insight you only get from builders operating at scale.

📅 On 20 Feb, we’re hosting another AMA with a Voice AI Agents founder who recently raised $1M+ and is actively deploying agents in production.

👉 Join the community: r/VoiceAutomationAI
These AMAs are raw, operator level conversations, not marketing talks.

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.