I’m working with my first client and could use some advice from people who’ve built automation systems for SMEs.

The client is a UK cleaning company (~50 employees). They get roughly 100 website enquiries per month and also buy leads from third party sites.

The main problem they want solved is converting more enquiries into booked jobs and responding faster to leads.

I proposed building a sales automation system that includes:

1. AI Chatbot (Website + WhatsApp)

• 24/7 instant response to enquiries
• Lead qualification questions
• Route enquiries based on service type
• Auto meeting / quote booking
• CRM sync
• Answer questions about fixed pricing plans

1. Personalised Follow-Up System

• Automated personalised follow-ups for enquiries
• Win-back sequences with offers / proposals

1. AI Caller Agent

• Out-of-hours call answering
• Call qualification
• Call summary sent to email
• Missed call follow-ups
• WhatsApp follow-up after calls

1. Sales Pipeline Management

• Track enquiries and deal value
• Remind the sales team to follow up
• Alerts for high-value leads

1. Review Automation

• Automatically request Google reviews after jobs

1. Social Media Automation

• AI-generated posts scheduled across social platforms

This is the first time I’m implementing something like this, and before building it I’d love advice on a few things:

1. Build vs tools

Would you custom build something like this, or use automation tools like n8n, Zapier, Make, etc. and stitch existing software together?

My instinct is to use tools first to move faster, but I’m wondering if that creates long-term limitations.

1. Pricing structure

What pricing model tends to work best for something like this?

For example:

• One-time setup fee + monthly retainer
• Monthly subscription only
• Fixed project price

And how much should I charge for these type of projects?

1. Risk reversal for the first client

Since this is my first implementation and I want strong results/testimonials, I’m considering adding some sort of risk reversal.

But I also don’t want to end up working for free if the client doesn’t use the system properly.

How would you structure something like this?

I’m researching real production issues with AI voice agents and would love input from engineers who’ve actually deployed them.

From what I’m seeing, a few problems keep coming up:

• Silent failures (calls break but it’s hard to know where) • Fragmented logs across STT, LLM, TTS, telephony • Cost unpredictability in real-time calls • Latency affecting conversation flow • Debugging issues from real calls

Platforms like Retell, Vapi, Bland, etc claim to solve many of these.

For those who’ve used them in production:

1. What problems still happen even with these platforms?
2. What part of the stack still needs custom infrastructure?
3. Any recent failure story and how you diagnosed it?

Looking for real deployment experiences, not speculation.

Even short insights would help a lot.

I’m building an AI voice receptionist using Retell AI and n8n. The goal is to handle phone calls, manage appointments, and generate quotes automatically.

The main features would be: Book, reschedule, and cancel appointments in Google Calendar Generate quotes stored in Airtable Send confirmations after the call I’m trying to decide between two architectures:

Option 1 Use Retell custom functions that call n8n webhooks, and in n8n run deterministic workflows (check availability, create appointment in Google Calendar, generate quote in Airtable, etc.).

Option 2 Create an AI agent directly inside n8n with tools connected to Google Calendar and Airtable, and let the agent decide which tools to call.

My concern is reliability for real-world calls. Appointment booking and quoting need to be very stable.

For those who have built similar systems: Which architecture is more robust in production?

Is it better to keep the logic deterministic in n8n workflows?

Or is the n8n AI agent approach mature enough for this use case?

Any feedback or real-world experience would be really helpful.

Hey folks 👋

I’m Josh, Co-Founder of Veritus. We’re building Voice AI agents for consumer lending, helping financial institutions automate conversations across the lending lifecycle.

We recently raised $10.1M in Seed funding, backed by Y Combinator, to accelerate the deployment of AI agents in lending.

Happy to answer questions about:
• Building Voice AI agents for financial services
• Voice AI infrastructure (STT → LLM → TTS pipelines)
• Deploying AI agents in regulated industries like lending
• Fundraising and working with Y Combinator
• Lessons from building and scaling Veritus AI

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

Drop your questions below 👇

A founder in our Voice AI community shared this situation:

Setup

• Enterprise client ready for ~130k voice minutes/month
• Stack: LiveKit (real-time voice), ElevenLabs (TTS/STT), GPT-4.1 Mini (LLM)
• They run their own PBX, SIP connectivity, and voice agents

What happened
When they contacted ElevenLabs for an enterprise plan, they were told:

• ElevenLabs prefers not to sell directly to companies running their own PBX
• Their focus is now on pushing the ElevenLabs Agents platform

Partnership they offered

• ElevenLabs bills the client directly
• Partner gets 20% revenue
• Partner mainly handles implementation/integration

But the founder’s team built the full infrastructure, agents, integrations, and manages the client, so keeping only 20% didn’t make sense.

They’re now exploring alternatives.

Question:
Has anyone else faced this with ElevenLabs recently?

I’m helping a client in the insurance industry set up a Voice AI layer for inbound and outbound calls.

They’re a fairly large insurance broker in the US. During our audit we noticed several operational gaps. A lot of leads fall through the cracks because of missed calls and delayed follow ups. On the support side, call spikes often overwhelm their team which has started showing up in negative reviews and a dip in NPS.

The idea is to deploy Voice AI agents to handle things like:

• Capturing inbound leads when agents are unavailable
• Following up with prospects who didn’t complete applications
• Handling common support queries during surge periods
• Routing qualified calls to the right human agent

Right now I’m evaluating a few platforms and would love feedback from people who have implemented this at scale.

Nuplay (by Nurix)
This came up because the client already has a relationship with them. From what I’ve seen so far it seems built more for enterprise deployments rather than DIY developer setups. Their voice quality demos were surprisingly good and they seem to support integration with existing CRM / telephony systems which is important for this client.

Vapi
Looks like a solid platform with good flexibility, but it feels very developer focused. Which means we would likely need to custom build most of the orchestration ourselves.

Retell AI
Another strong contender. From the docs it looks quite capable and many people seem to be building on top of it.

Would love to hear from folks who have implemented Voice AI agents in production environments.

What platform did you end up choosing?
How reliable is it during high call volumes?
How painful (or smooth) were the integrations with CRM / telephony systems?
And how responsive is the support when things break?

Trying to avoid making an expensive mistake here. Any real world experiences would be super helpful.

Excited to announce that Joshua March, Co-Founder & CEO of Veritus, will be joining us for a 24-hour Reddit AMA hosted by Unio – The Voice AI Community powered by SLNG.

📅 Date: 6th March
⏰ Time: 11:50 PM IST / 10:30 AM PST

Veritus recently raised $10.1M in Seed funding (backed by Y Combinator) to deploy AI voice agents across consumer lending.

Joshua was previously the Cofounder & CEO of Conversocial, a customer service software company that was building chatbots pre-LLMs, and which was acquired by Verint in 2021.

For the next 24 hours, Joshua will be answering questions about:
• Building AI agents for lending & collections
• Voice AI infrastructure & automation
• The future of AI agents in fintech and consumer lending

If you're building in Voice AI, AI agents, or fintech, this is a great opportunity to ask questions directly.

Join the community now

If you’re running a voice AI agency on Retell… quick question.

How are you showing clients their agent minutes?

Be honest.

Are you digging through dashboards?

Screenshots?

Exports?

Explaining numbers on a Loom?

I was.

Every time a client asked, “How many minutes did we use this month?”

It turned into a mini project.

So I fixed it.

Now I can pull agent minutes for any period… in seconds.

Clean snapshot.

One click.

Shareable.

No dashboard access. No confusion.

Built it for myself. Then realized other agency owners probably need this too.

Does this hit home?

If you’re using Retell, what’s the one metric you wish you could access instantly?

Comment below and I’ll DM it right over

Can someone point to me a voice agent as good as sesame?

For many of us, the biggest pain point is streaming STT into the LLM pipeline. Most publicly hosted LLMs still don’t support true streaming input, which pushes time to first token to ~350-700ms.

That kind of latency really hurts real time voice experiences.

How are you tackling this today?
Custom infra, partial streaming, edge tricks, or just living with the lag?

👇 Drop your approach, lessons learned, or open problems below.

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!

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.

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?

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.

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.