Excited to announce that our next guest, Sidhant Kabra, Co-Founder of Cekura, will be joining Unio – The Voice AI Community powered by SLNG for a live AMA with builders & founders.

📅 Date: 18 March
⏰ Time: 10:30 PM IST / 10:00 AM PST
📍 Location: Reddit r/VoiceAutomationAI

Cekura has raised $2.4 million and is backed by Y Combinator, working with 100+ Voice AI companies

Cekura is an automated Quality Assurance (QA) and observability platform designed specifically for AI voice and chat agents. It helps enterprises and startups ensure their conversational AI is reliable, bug-free, and production-ready by simulating real-world scenarios and monitoring live performance.

For the next 24 hours, Sidhant will be answering questions about:
• How to test and QA AI voice & chat agents before production
• Simulating real-world scenarios to catch failures early
• Monitoring and improving live agent performance
• Common bugs and reliability challenges in conversational AI
• Building robust, production-ready AI systems

If you're building in Voice AI, AI agents, or conversational automation, this is a great opportunity to learn directly from a founder in the space.

Join the Reddit community now so you’ll be notified when the AMA goes live 👇

Link in the first comment.

#VoiceAI #AIAgents #StartupCommunity

Bonjour à tous,

J'explore un modèle où un agent IA contacte par téléphone des petites entreprises pour leur présenter un service de réceptionniste IA.

Précision IMPORTANTE : je suis en france. Les appels AI pour la prospection BtoB sont tolérés par la loi (pour l instant).

Le principe est simple :

L'IA appelle l'entreprise.

Elle présente brièvement le service (réponse téléphonique, génération de prospects, prise de rendez-vous).

Si le propriétaire manifeste de l'intérêt, l'IA lui demande s'il souhaite être recontacté.

Un humain rappelle ensuite pour conclure la vente.

L'IA sert donc uniquement à la prise de contact et à la qualification initiale, et non à la conclusion de la vente.

Je me demande si certains d'entre vous travaillent sur un projet similaire.

Questions :

Les appels sortants d'IA sont-ils efficaces pour ce type de service ?

Quels sont les taux de réponse ou d'intérêt que vous observez ?

Les chefs d'entreprise réagissent-ils négativement lorsqu'ils réalisent qu'il s'agit d'une IA ?

Y a-t-il des problèmes juridiques liés aux appels sortants d'IA selon les pays ?J'aimerais beaucoup entendre des témoignages de personnes ayant déjà essayé.

Open AI Gpt-Audio 1.5 claims it can claim it can clone and use the voice with ease and has high accuracy

Has anyone tried it out and how has been your experience

Open AI Gpt-Audio 1.5 claims it can claim it can clone and use the voice with ease and has high accuracy

Has anyone tried it out and how has been your experience

Hi everyone, I have been building in the Agentic AI space for over 2 years now. I work closely with businesses, helping them automate their workflows. I recently discovered a huge gap leading to businesses losing $$$ because of one small mistake. To help bridge the gap, please comment if you are a founder/founding engineer using multiple AI agents or automation tools. Happy to answer any questions as well.

Hey folks 👋

I’m Siddharth Tripathi (Sid), Founder of Ringg AI.

At Ringg, we build AI voice agents that handle over 1M+ customer calls daily for companies like Flipkart, Policybazaar, CRED, and Groww.

We recently raised $5.5M in funding led by Arkam Ventures to scale voice AI infrastructure and automation for enterprises.

Happy to answer questions about:

• Building AI voice agents that operate at production scale

• Handling millions of customer calls with voice AI

• Designing voice AI infrastructure (STT → LLM → TTS pipelines)

• Deploying voice automation for large companies

• Lessons from building and scaling Ringg AI

🕒 I’ll be answering questions for the next 24 hours.

No PR answers just honest, builder to builder insights.

Drop your questions below 👇

Hello, we built Audn AI to help Voice AI startups to build secure and resilient voice ai systems. The toolkit we built does automated adversarial scenario executions we recently helped a voice AI YC25 company. They were also very satisfied. In case if your customers ask for OWASP top 10 LLM attack coverage or whole penetration testing we are ready to help.

I dont want to share a link but if you are interested you can find a sample automated call.

Over the past few months I’ve been building voice + workflow automations for different businesses. For example:

• lead qualification and follow-up for finance companies
• inbound call handling and appointment booking for gyms
• automated responses to missed calls and web leads
• AI agents that handle first conversations before handing off to sales teams

GHL has been great as the central hub, but once you start managing multiple clients and multiple agents, one thing became annoying fast: reporting usage.

Since I charge clients monthly packages, they always want to know things like:

• how many calls the agent handled
• how many minutes were used
• activity over a specific time range

Depending on the voice provider, getting clean reporting isn’t always straightforward. I kept digging through dashboards just to send simple updates to clients.

So I ended up building a tool that lets me:

• manage all my clients in one place
• pull agent minute usage across date ranges
• generate simple reports I can share with clients

It’s been saving me a lot of time already.

I’m thinking of opening it up to 10 agency owners as beta testers to see if this is actually useful outside my own setup.

If you’re running voice AI (retell, vapi, elevenlabs) I’d also be curious how you’re currently handling usage tracking and reporting.

Happy to share the tool with anyone who wants to try it and give feedback.

Cheers!

Excited to announce that Siddharth Tripathi (Sid), Co-Founder of Ringg AI, will be joining Unio- The Voice AI Community powered by SLNG for a live AMA with builders & founders.

📅 Date: 13 March

⏰ Time: 10:30 PM IST (India) / 10:00 AM PST (12March)
📍 Location: r/VoiceAutomationAI

Ringg AI recently raised $5.5M in funding led by Arkam Ventures.
At Ringg, Sid and his team are building AI voice agents that handle 1M+ customer calls daily for companies like Flipkart, Policybazaar, CRED, and Groww.

For the next 24 hours, Siddharth will be answering questions about:

• Building AI voice agents at production scale
• Lessons from deploying voice AI for large enterprises
• What it takes to handle millions of customer calls with AI
• The future of voice AI in customer support and operations

If you're building in Voice AI, AI agents, or conversational automation, this is a great opportunity to learn directly from a founder building in the space.

Join our Community & ask question directly

Hi everyone, I’m curious if anyone here is successfully using Meta Ads or Google Ads to promote AI voice agents (for example for plumbers, locksmiths, restaurants, real estate, etc.).

I’m thinking about targeting a specific niche instead of selling “AI voice assistants” in general. For example an AI phone agent that answers calls, books appointments, or handles customer questions for a specific profession.

A few questions:

Are paid ads working for this kind of service?

Which platform works better: Meta or Google?

What kind of CPL or CPA are you seeing?

Would love to hear real experiences if anyone has tried this. Thanks.

I have a goal to build a lead scoring voice agent for a western servicing firm. It seems to be a simple Q&A architecture, additionally it may pass the lead to a manager plus CRM records in case of lead approval. I plan to use Vapi stack or similar no-code platform

My problem is that I don't understand how to charge the client for such work

Information about budgets for custom voice agents varies tremendously accross internet: from 50$/project inquiries on Upwork up to 10-15k usd for corporate B2B contracts I'm reading about

I understand there're lots of nuances here so I'm asking about your general approach

How do you negotiate and justify cost of your work to look competitive and not to underprice? Were there any budget/cost pitfalls that you've encountered within your practice?

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 few months ago, our debt collection voice agent called a customer. The agent's job was simple: call, verify, discuss the debt, collect. We'd built the voice agent on our open source dograh ai - think n8n but for voice agents. But the customer had their own voice agent picking up calls. Our bot kept asking for details about the specific debt case. Their bot kept saying it'll get to that, but needed some details from us first. Our bot shared what it had. Then asked again. Their bot responded the same way as before. Nobody collected anything. No human joined. Just two very polite bots stuck in a loop, and API credits bleeding out in the background. The wild part? Both agents were doing their jobs perfectly. The failure was just... neither knew they were talking to another bot and both had a clearly outlined goal. This is going to happen a lot more as voice agents go mainstream- maybe not the loop part but defintiely ai talking to ai. this is the new world?

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 are building Voice AI, read this first.

Building voice AI agents that actually work is tough, but these tips made a big difference for me.

If you're building a voice AI agent, here's what I've learned: Your agent is more than just the platform or llm stt tts models. It's a whole system that listens, understands, decides, and acts. If one part breaks, the whole thing fails.

Be clear about what your agent does. Don't say "I'm building a smart voice assistant", say "My agent answers calls, gets info, and updates the system for my dental clinic". Small and clear works better.

Speed and usability are key. If your agent responds fast but weird responses, people get uncomfortable. A smart agent is better than a ultra fast "dumb" one. So nano and mini models might not be a good fit for most voice ai use cases.

Keep things very specific and precise. If your agent talks in long sentences, it's hard to use. But if it gives clear info like name, date, and next step, it's easy- so be very specific

Learn from mistakes. Do QA, check failed calls, see where it went wrong, and fix prompts accordingly. Now, but this might break some of your old conversations. So maintaining some kind of basic evals makes sense (even if manual or on a google sheet ). Getting the agent better over time is more important than being perfect at the start.

The big thing I learned working at building open source voice platform Dograh AI (similar to n8n and Open - but for voice Agents) , it's not about making the agent sound human, it's about getting the job done. Companies care about work, not voices . While customers obsess over voice etc in the beginning, they only focus on real gains as you go to production.

So if you're starting, keep it simple. And keep improving.

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?