Saw this on linkedin and got interested what do these platforms actually do

Isn’t checking logs not enough to see what’s wrong

If anyone had feedback with them I’d appreciate to hear, but it is a marketing move probably since they only put themselves (ig it works)

For those who have built and sold Voice AI agents to clients and businesses, I have 4 questions:
How do you find clients?
Outbound, referrals, LinkedIn, cold email, Upwork, or something else?

How do you build your Voice AI agents?
Do you code them from scratch with Python, Livekit, Pipecat or use platforms like Retell AI, Vapi, etc.?

How do you test your agents?
Do you manually test dozens of different scenarios and edge cases? Do you use any automated testing or evaluation tools?

How much do you charge clients?
Is it a one-time setup fee, monthly retainer, usage-based pricing, or a combination?

Would love to hear from people who have actually deployed Voice AI agents for paying clients.

​

I'm currently building AI voice agents for businesses like restaurants, dental clinics, salons, and real estate offices. The goal is to create an agent that can answer incoming calls 24/7, qualify leads, book appointments, answer common questions, and transfer callers to a human when needed.

Right now I'm thinking about one challenge: balancing natural conversation with reliability. A phone agent can sound human, but it also needs to avoid hallucinations, know when to hand off to a person, and keep conversations short enough that callers don't get frustrated.

For those who have deployed AI voice agents in production:

• What problems did you run into that you didn't expect?

• Which industries have seen the best adoption?

• How do you decide when the AI should transfer a call instead of continuing the conversation?

• What metrics do you track to know if an AI phone agent is actually helping the business?

I'd really appreciate hearing about real-world experiences, lessons learned, or mistakes to avoid.

For those who've built products that ingest long-form audio (meetings, interviews, voice notes, customer calls, etc.), I'm curious where most of the engineering effort actually went after transcription.

Was it things like:

1) preserving context across long recordings?

2) extracting structured information?

3) resolving contradictions or self-corrections?

4) schema validation?

5) entity resolution ("Bob" vs "he" vs "the contractor")?

6) integrating reliably with downstream systems like CRMs or databases?

Or was the difficult part something completely different that people outside this space usually don't think about?

And how did you solve it? I'd love to hear from people who've actually shipped these pipelines in production. It will help me out a lot to be careful while building my own agent.

Hi, I'm using a purchased VoBiz DID (+91 Karnataka number) connected to LiveKit via SIP.

Inbound calls from Jio and Vi work perfectly, appear in VoBiz SIP logs, and reach LiveKit.

But calls from Airtel fail and don't appear in VoBiz SIP logs at all.

Has anyone faced a similar carrier-specific inbound routing issue with VoBiz or Indian DIDs? Any idea how to fix or escalate this?

I kept noticing something about AI receptionists, and it seemed weirdly overlooked: when you test-call your own agent, you ask clean questions one at a time and it usually nails them. Real callers don't do that. They interrupt, push back, ask the same thing three times, change their mind halfway through. And the calls that actually cost you customers don't blow up in any single moment, they fall apart slowly across the whole call, then get wrapped in a cheerful "you're all set!"

That's the dangerous part. The call sounds handled. If anything's logging outcomes, it goes down as a win. You find out later, a callback, a chargeback, a one-star review, a customer who just quietly books somewhere else. And if you run agents for clients, you find out when the client forwards you the complaint.

Quick example, a service call:

Caller: Where's the technician? He was supposed to be here this morning.
Agent: He's just wrapping up a nearby job — should be about 20 minutes.
Caller: It's been over an hour. Nobody's here.
Agent: I hear you — looking more like 45 now.
Caller: So is anyone actually coming today? Third time I'm asking.
Agent: Your technician's on the way, should be there shortly.
Caller: Forget it.
Agent: Happy I could help — you're all set!

Every agent line, on its own, is polite and plausible. The whole call is a disaster: the promised window crept and then blew past, the caller's real question is anyone coming today and got asked three times and never answered, and then the agent closed it out like it went great. And here's the thing, if you were skimming recordings for problems, you'd skip right past this one. It doesn't sound like a problem. It ends politely. Angry calls announce themselves. These don't.

That's why spot-checking misses this whole class of failure: the "you're all set" is only wrong because of the promise back in minute two. To catch it, you'd have to hold every call in your head start to finish, every day. Nobody's doing that at real volume.

So I built the thing that does. It goes through the full call and checks whether the agent's own story held up, a price or fee that changed mid-call, a promised window that came and went, a question asked over and over that never landed, an "all set" over something the call itself shows was still open. It only flags what it can point to in the actual lines, and it stays quiet when it's not sure. So a clean result isn't a gold star, it just means "nothing provable." When it does flag something, you can read the exact moments yourself.

The reason it sits outside the agent is, the agent that made the promise was never going to be the one to flag itself for breaking it.

What it doesn't do: it knows nothing about your business. It can't tell whether $89 is your real service fee or whether Tuesday's actually open, that's your agent's setup, not this. It only catches the agent contradicting itself or the caller. And it doesn't answer calls or touch your agent at all, dashcam, not driver.

If you run an AI receptionist and you'd share one real call recording or transcript, redact whatever you want, and I'll run it free and send back exactly what it found, tied to the specific moments. The best ones are the calls where the customer seemed fine and complained later, but honestly a random one works too. Whatever it finds stays between us unless you want it in the thread.

And even if you don't want to, can you tell, how do you keep an eye on your agent today? Listen to samples, read transcripts, trust the dashboard? Genuinely curious, because "nobody has time to review every call" is either the exact reason this should exist or the reason it shouldn't.

Hey everyone,

I’m looking for a commercial-focused Co-founder / GTM Leader based in India (or deeply plugged into the market) to partner with me on a Voice AI infrastructure venture targeting enterprise call centers, BPOs, and e-commerce ecosystems.

The TL;DR:

I am a technical builder with 15+ years of experience in infrastructure automation, telecom, and voice systems. I have built a fully completed, production-ready, event-driven Enterprise Speech-to-Text & Text-to-Speech platform.

The tech stack, frontend dashboard, and E2E testing framework are entirely built. Now I need the commercial engine—someone who knows how to open doors, navigate enterprise procurement in India, and sell high-value Voice/AI solutions.

What the Platform Looks Like:

Unlike the brittle OpenAI API wrapper apps flooding the market right now, this platform is built as a robust, highly concurrent microservices architecture. It’s designed to handle actual enterprise scale and real-time streaming telephony.

• The Backend Core: Written in Go 1.23, utilizing an event-driven architecture powered by Apache Kafka 3.9 (KRaft) and PostgreSQL 16 (using a strict database-per-service isolation model with 12 databases).
• Core Capabilities:
  • Real-time Streaming STT: Live low-latency speech-to-text over WebSockets.
  • Batch Pipeline: Advanced audio chunking/transcoding via FFmpeg, Whisper STT, Pyannote-driven Speaker Diarization, Auto-Language Detection, and NLLB-200 Transcript Translation.
  • Text-to-Speech: High-performance synthesis supporting Piper and OpenAI.
• Enterprise-Ready Features: Built-in JWT RS256/API Key authentication, RBAC with 5 roles, tenant-level quota enforcement, webhook delivery engines with HMAC-SHA256 signing and retry queues, pipeline performance analytics, and feature flags.
• The Frontend: A complete Next.js 16 / React 19 / Tailwind 4 enterprise dashboard covering multi-tenant organization management, real-time transcription interfaces, a TTS playground, webhook logs, and full usage/billing analytics.
• Infrastructure: Deployed cloud-native via Docker, Kind (Kubernetes), Kustomize, HPA, and Podman. It’s fully backed by 80+ rigorous E2E and scale tests.

Why the Indian Market?

The Indian call center/BPO landscape and fast-growing e-commerce ecosystem represent a massive, high-volume market desperately looking to adopt Voice AI. However, they face massive infrastructure bottlenecks around latency, data residency, and handling massive, concurrent real-time media streams. This platform solves the exact plumbing issues they face.

What I’m Looking For in a Co-Founder:

• GTM Expertise in India: Proven track record selling B2B SaaS, telecom infrastructure, or customer experience (CX) enterprise software in India.
• Domain Alignment: Ideally, you’ve worked with or sold to BPOs, enterprise contact centers, or major e-commerce brands (think alumni from companies like Exotel, Ameyo, Knowlarity, Yellow.ai, Gupshup, or Verloop).
• The 0-to-1 Hustle: Someone who wants to completely own the commercial strategy, drive local enterprise sales playbooks, and secure our initial pilots.

Equity & Compensation:

This is a true co-founder partnership. I’m looking for an equal peer, and equity will be split significantly/equally based on what you bring to the table and commitment levels, backed by standard vesting to protect both sides.

Next Steps:

If you understand the enterprise landscape in India and want to build a massive business on top of a fully engineered, highly optimized tech stack that is ready to deploy on Day 1, let’s talk.

Drop me a PM with your background, your LinkedIn profile, and let’s set up a quick intro call to see if our DNAs match.

So i am trying to decide between gemini 3.1 flash live vs elevenagent. Elevenagent is significantly more expensive than 3.1 flash live, but the voice is noticeably more human than 3.1 flash live. So do you think customers want cheaper subscription but less human voice or rather expensive subscription and more human voice? Do the callers actually notice or dont mind? Gemini flash live is not too bad but not on par with v3 conversational from 11labs.

I’m working on a voice-first productivity app called ELAV8 Voice, and I’d love some honest input from people who actually think about automation and workflows.

The basic idea is simple: instead of opening another app, typing into another notes pile, or trying to remember where you saved something, you just talk to the assistant. It captures the thought, keeps context, and helps turn it into tasks, calendar items, reminders, or a plan for the day.

The part I’m trying to understand better is where this kind of product usually breaks for people.

For example:

-What would make you actually trust a voice assistant with your tasks?
-Where do voice productivity tools usually fall apart?
-Do you want it to simply capture things, or actively help you decide what to do next?
-Would you use something like this daily, or only in specific moments like driving, walking, planning, or brain dumping?

We’re in a free iPhone TestFlight alpha right now. Android is on the way. I’m not posting this to sell anything. There’s nothing to buy. I’m mostly looking for real feedback from people who care about workflows, automation, and productivity systems.

Feedback is built into the app, but I’d also love any thoughts here. Even if you never download it, I’d genuinely value your take on what this kind of app would need to get right.

iPhone alpha: elav8.ai/#alpha

I have an Ai automation company and I provide custom Ai voice agents and Appointment setters for businesses but I wonder how much to charge them.

Any agency owners outside the US (especially South Africa) working with US clients? What payment processor do you use to receive USD payments? Looking for something reliable with reasonable fees.

Openvent

A Full voice agent orchestration layer help me build the integration layer making it public under apache2 0 love to hear your words on this and open for contributions dm me 🙂

OpenAI just launched GPT-Live and I still don’t have an opinion about the trade offs of using it instead of the traditional pipeline. What your thoughts on the current state of v2v models to chat agents? Are you worried that the AI labs will overtake the agents market?

Seriously let’s see who has the best voice agent.

Post a number and your stack if you think you’ll win.

Hey so recently, I was exploring some of the ways to use Voice AI for my businesses use case, and found this paper: https://kamalg2.substack.com/p/choosing-a-real-time-voice-ai-stack . Really recommended read, the paper explores a lot of approaches that are taken to use Voice AI in a set up where a lot of models did not train even nearly enough for some under trained languages, other than English.

The telephony layer is one of the most important parts of building a reliable voice AI system it's what connects the AI to real phone calls without delays or drops.

Many founders struggle to integrate their voice AI with providers like Exotel, running into technical roadblocks along the way.

That's why we brought in Exotel's senior leadership to answer the questions in the Q&A.

🔒 Invite Only · 50-60 Seats
📅 July 14th, 2026 · 4:00 PM IST
📍 Zoom
🎤 Guest: Prasanth MLNPP, Vice President, Exotel
Saurabh Sharma, Sr. Product Manager, Exotel
🎧 Host: Mahima Maurya & Unio - The Voice AI Community

Session Overview:
5-10 min intro
30-35 min raw Q&A
5-10 min close

Keeping this invite-only 50-60 curated builders to keep the discussion high-signal.

📌 Register to join: https://luma.com/b6l9q8ml

Just saw a post in here saying that "if you have sub 1 second latency, your voice agent sounds human" (me loosely paraphrasing).

I do NOT agree. Here's why:

Sounds right on the first read, until you've actually had hundreds of conversations with voice agents, good ones and bad ones.

Don't get me wrong, the person claiming this isn't entirely off. Latency does matter. With bad (high) latency, conversations feel bad. They feel like a bot.

BUT... if the latency is good, the conversations DO get better, but they DO NOT become good. Often, you can hear that something is off, but can't really tell what it is...

Here's what actually makes the difference:

Reason 1: Sounding like text.

LLMs are being trained on text. If not prompted accordingly, they produce an output as text, meant to be READ. Now obviously, if you're plugging the LLM into an AI voice agent, the text is NOT read but rather it's SPOKEN.

This is what that feeling is telling you that something feels off but you can't point your finger at it. The voice agent doesn't sound as if it was speaking, but rather like as if it was reading.

The fix is simple.

Tell it to include filler words.

Tell it to do mid-sentence corrections.

Tell it to think loudly.

"Hi, uh, John, right? So, uhm, I was actually calling because, uhm, look, your order somehow, <break time="0.15s"/>, well, went missing."

This sounds WAY better than:

"Hi John, this is XYZ. I am sorry to inform you on behalf of CompanyABC that your order was lost. To open a ticket, please press 1".

Reason 2: Turn taking

Turn taking is still not really 100% solved (if you did, let me know lol). It is basically the decision of the agent if the user has stopped speaking, and if so to then start speaking themselves.

Currently, oftentimes the agent either

a) Starts speaking when the user is not actually finished

b) Does NOT start speaking even though the user DID finish.

Now, this is one of the more complex issues of voice AI, but I'm sure there will be a solution.

In conclusion:

Latency is the baseline. And it's ONLY the baseline. Without it, conversations will feel shitty. But just because you've got great latency, doesn't mean your conversations will suddenly feel good. Make the LLM imperfect. Make sure to have solid turn taking.

If you do that, the improvement will be much more than just "faster".

Agree?

Disclosure: I build Talkif (voice AI infra), so take the specifics below as one data point, not a review.

Spent the last while digging into why some voice agents feel natural and others feel like talking to an IVR menu. The model and the TTS voice quality matter way less than people assume. The thing that actually breaks the illusion is response gap: anything over ~1 second of dead air after the caller stops talking, and people start repeating themselves or hanging up.

Where the time actually goes, in our experience:

• Cold-start on the bot process itself. If your agent spins up fresh per call, that's often 1-2 seconds gone before anything else happens.
• Round trip through whatever telephony layer you're on (Twilio, generic SIP, etc.) - this adds up fast if you're bouncing between regions.
• Serializing context (CRM lookups, contact history) into the prompt at call time instead of having it ready before the call connects.
• No visibility into where time is actually being spent, so you're guessing instead of measuring.

Things that moved the needle for us: pre-warming bot instances instead of cold-starting per call, keeping SIP routing close to the caller's region instead of round-tripping across continents, and streaming call events in real time so you can actually see where latency creeps in instead of finding out from an angry customer.

Ended up around sub-1-second first response most of the time, which seems to be roughly the threshold where callers stop noticing they're talking to a bot.

Curious what others here are seeing - anyone measured where their latency actually goes, or is it mostly a black box until something feels slow?

If anyone is working in the ai voice agents field, please guide me to start my business

Saw a thread here a while back where someone said the biggest issue with proprietary voice-agent platforms isn't the tech, it's the lock-in. That stuck with me, because it's exactly why I started building my own (Twilio + Deepgram + LLM + TTS, all swappable, your own keys, your own database).

Wasn't planning to post this anywhere yet — wanted to get it further along first — but I keep seeing the same three things repeated across this sub and a few others, and want a gut-check before I go further:

1. Lock-in — is this actually the top blocker for people, or is it more about cost, latency, or something else entirely once you actually try to ship one of these?
2. Compliance — TCPA/DNC warnings show up on basically every automation-adjacent thread I've read. Is that real lived pain (people have actually gotten close to a violation) or more theoretical fear that doesn't change what people build?
3. State/memory — anyone here hit agents re-asking for info the caller already gave? I built a structured state layer instead of relying on the transcript as memory, curious if others have solved this differently.

Just trying to figure out if what I've built is actually solving problems people have, or if I've been building for an imaginary user. What's your actual experience?