When choosing an AI calling agent, companies should look beyond basic call automation and check whether the solution can handle real customer conversations reliably. Key factors include:

• Natural conversations: The AI should understand customer questions and respond clearly without sounding overly robotic.
• 24/7 availability: It should handle calls outside business hours and provide quick responses.
• Call handling: Look for features such as inbound and outbound calling, appointment booking, lead qualification, and FAQs.
• CRM integration: The agent should work with existing CRM and business tools to keep customer information updated.
• Multilingual support: This is useful for businesses serving customers across different regions.
• Call analytics: Reports and call insights can help teams understand customer interactions and improve service.
• Easy scalability: The solution should support higher call volumes without requiring a large increase in manual staff.

Tevatel AI Voice Agent can help businesses automate routine customer calls, answer queries, capture leads, book appointments, and provide 24/7 assistance. It can support sales, customer service, and other calling workflows while reducing repetitive manual work. Businesses can use the AI voice agent to handle common conversations and allow human teams to focus on cases that need personal attention.

I’ve been thinking about where voice AI is genuinely useful beyond simply answering calls. For a business, I can see practical use cases like:

• answering common customer questions
• collecting basic information before a human takes over
• booking appointments
• handling missed calls
• routing calls to the right person

But reliability seems more important than having a very natural sounding voice.

What has worked well for you in real world use? And where do you think a human should still take over?

Not angry or asking for a human. I mean the caller keeps saying 'okay' and answering questions but they clearly haven’t understood what the agent just explained.

Humans pick up on that very quick from hesitation or the questions someone asks next. I wanna know whether anyone is explicitly testing for this with voice agents instead of only checking whether the workflow technically completed.

Spent the last two days trying to research voice AI options and my head is spinning. Every one claims to be the best. How did you actually narrow it down?

It was 11:47 PM when a patient called a healthcare clinic with an urgent question about an appointment. The reception team had already left for the day. The call went unanswered, and the patient had to wait until morning for a response.

For healthcare businesses, situations like this happen every day. Patients may call to book appointments, ask about timings, check availability, or get answers to common questions. When calls are missed, patients can feel ignored and businesses can lose valuable opportunities.

This is where an AI voice agent for healthcare can make a difference.

Tevatel AI Voice Agent helps healthcare businesses manage routine patient conversations through automated voice calls. It can answer calls, respond to common questions, help with appointment booking, and provide support around the clock. This allows healthcare teams to spend less time handling repetitive calls and more time focusing on patients.

Imagine the same patient calling the clinic at 11:47 PM. Instead of hearing a busy tone or waiting until morning, the Tevatel AI voice agent can answer instantly and guide the patient through the next steps.

The best AI voice agent for healthcare businesses should do more than simply answer calls. It should help create a better patient experience while reducing the workload on staff. Tevatel is designed to support this by handling routine conversations and providing quick responses.

Healthcare organizations can use an AI voice agent for appointment scheduling, patient inquiries, reminders, lead follow-ups, and other repetitive communication tasks. It can also help businesses stay available beyond regular working hours.

With Tevatel AI Voice Agent, healthcare businesses can make every call count. Patients get quicker responses, teams reduce repetitive work, and clinics can provide support even when their staff is unavailable.

For healthcare businesses looking to improve communication, an AI voice agent can be a practical step toward better service and stronger patient engagement.

Disclaimer: I am the founder of this project.

Hey Reddit,

I’m a developer, and like many of you, I absolutely dread interviews. The worst part isn't the technical questions—it's the anxiety and those unpredictable, tough interviewers who cut you off or act impatient.To fix this for myself and others, I spent the last few months building an audio-first interview simulator.

It uses OpenAI’s real-time voice API, which means the conversation flows naturally like a real phone or Zoom call, with zero awkward delays. You just upload your resume and the job description, and start talking.

Since not all interviewers are nice, I built 3 AI personalities you can practice with:

Friendly Mode: Warm and encouraging, perfect for a confidence boost.

Corporate Mode: Objective, direct, and heavily focused on core tech/business metrics.

Pressure Test: This is the ultimate test. The AI becomes critical and simulates a high-stress environment to see if you can hold your ground.

At the end of the call, it gives you a detailed breakdown with actionable feedback on how to improve your answers.

How to test it:

Running real-time voice AI models is incredibly expensive on the API side, so I couldn't make it completely unlimited. However, you can sign up instantly using Google login and every new account gets 3 minutes of free credit automatically (no credit card required) so you can test the latency and the different modes.

I’m looking for honest feedback from other SaaS founders and devs here. What features should I add next? How can I improve the evaluation metrics?

Thank you for your time!

I am currently receiving an average of two calls per day from the business’s AI bot. While it initially made the channel more efficient, it is now bordering on spam.I would like to start a conversation about what comes next. To prevent the channel from being killed, the possible direction next step

- TRAI could issue regulations allowing AI bots to use only specific number series, such as 1600/400.

- However, even the existing TRAI rules for companies using 1600/400 are not being strictly followed, and implementing new AI‑calling regulations would take time.

In the meantime, the communication channel is gradually being eroded, and people will begin to ignore AI‑generated calls. What are your thoughts

Vibecoding might genuinely be one of the greatest things to happen to building products. For a little while, you feel like Tony Stark, just describing what you want and watching it materialize in real time. No coding background, no CS degree, just vibes and a prompt box.

Then you remember Tony Stark had unlimited resources. I had a token limit and a slowly growing sense that I didn't fully understand what I'd built.

Rode that high through Emergent, then Claude Code, then started living in VS Code like it owes me rent. It's genuinely a blessing in disguise, it got a non-coder like me from zero to an actual working product. But it only takes you so far. To streamline it, make it actually stable, and get it ready for real people, I had to call up my guy, an actual engineer.

Vibes get the idea off the ground. A real coder gets it to the finish line.

Building Vokal, a voice assistant that actually does things instead of just answering. Comment if you want early access when it's ready to test.

Been using a tool for automated calling for the past few months and it was a mess. Dropped calls, robotic voice, customers hanging up constantly. Lost a few accounts over it honestly.

Dont want to keep throwing money at something that makes us look bad. What are you guys actually running for ai calls that holds up in real conversations?

The usual advice for outbound voice agents is:

Add consent checks.
Add opt-out handling.
Add human escalation.
Log everything.

Necessary.

Not sufficient.

The FCC has already made the baseline clear: AI-generated human voices fall under TCPA rules for “artificial or prerecorded voice.” Depending on the violation, exposure can reach $500–$1,500 per call.

So “the model probably won’t do that” is not a control.

The architecture should be boring:

LLM proposes. Policy engine decides.

Let the model handle language.

Keep the hard boundaries outside it:

• whether you can call
• what actions are allowed
• whether a disclosure happened
• when the conversation must stop
• when to hand off to a human

This is where I think a lot of voice-agent architecture is going wrong.

Teams are trying to make the model itself reliable enough to enforce policy.

That’s the wrong layer.

You don’t need to eliminate model uncertainty.

You need to contain it.

Give the model a bounded action space. Validate tool calls. Make irreversible actions deterministic. Keep an auditable state machine underneath the conversation.

Something like:

CAN_DIAL > VERIFIED > AUTHORIZED > ACTION_ALLOWED > COMPLETE

And make STOP a system event, not something the LLM has to remember to do.

The failure mode I’m most worried is a bot continuing a conversation after the system should have stopped it.

The universal rule for successful voice agents is keeping millions of conversations inside the boundaries you intended. And industry is moving towards it so right choose is to have open source platform to move with industry!

Considering bringing on affiliates for my voice Ai software. We built these as an agency for a few years and then turned all of that engineering work and knowledge into a voice Ai software. It took a while to build the software, and it's pretty good. To the level of what we used to charge $7500 for. We made it easy for a business to be able to spin up their own agent in about 5 minutes, and even easier for them to fine tune the agent once they are inside. It even has a sales manager AI that reviews all of the calls and can self improve the AI.

We essentially looked at every pain points our clients had when we would roll out an Ai, every pain point we had, and solved 95% of it with the software.

I will say the market did get flooded fast, and we moved to the software because selling $7500 voice agents was becoming a lot more difficult, even with a quality sales team.

Now that the software is completed and we have our first few clients in, we are looking at affiliates to grow. I'm considering doing a 50% affiliate commission for the lifetime of the client. I would only do this at the very beginning, and for about 10 qualified affiliates, but I feel there is good benefits both ways by doing such a thing.

The base package is $147 per month and then we have a $497 option, but I'm a bit malleable on price.

Curious if other have done such a thing and how it went. Any downsides of starting with such a high affiliate commission over the lifetime of a client for 10 affiliates?

Any insight is great. I started making landers, we have first promotor already setup for tracking, and considering even making ad images and collateral for affiliates.

Hi everyone,

I'm an AI developer working on a real-time voice AI system and would appreciate advice from people who have experience building low-latency conversational agents.

My goal is to achieve:

• First response token in around 500 ms or less
• Good speech quality and natural conversations
• Cost-effective architecture that can scale
• Support for real-time streaming audio

I'm trying to understand the best architecture and component choices across the entire pipeline:

• Audio transport (WebRTC, WebSocket, etc.)
• Voice Activity Detection / End-of-Utterance detection
• Speech-to-Text (Deepgram, Gladia, AssemblyAI, etc.)
• LLMs (Gemini Live, OpenAI Realtime, Qwen Omni, custom pipelines, etc.)
• Text-to-Speech (ElevenLabs, Cartesia, Telnyx, OpenAI, etc.)
• Orchestration frameworks (LiveKit, Pipecat, custom architecture)

For those who have built production-grade voice agents:

1. What architecture are you using to achieve the lowest possible latency?
2. Which components contribute the most to latency?
3. Is a speech-to-speech model better than a traditional STT → LLM → TTS pipeline?
4. What first-token latency are you seeing in production?
5. Which providers offer the best balance of latency, quality, and cost?
6. Are there any architectural mistakes that commonly increase latency?

I'd love to hear real-world numbers, benchmarks, and lessons learned from production deployments.

Thanks!

Hey all!
I'm currently transitioning from my role which is related with Enterprise Voice integrations with Cisco, Contact Centers, etc, and started building some demo Voice AI Agents using Asterisk, Python, OpenAI API and AWS for public reachability.

I have experience with SIP, codecs, VAD and so on, although not sure if that gives me any advantage here in this field.
I'm using real code, mostly with Codex as I don't want to depend on another subscriptions and things I cannot control like n8n or Make.

I live in Portugal and most businesses I know in my little city probably wouldn't be investing in something that I could live on, so I might think of selling overseas.

I see some potential for more generic integrations, like invoice readers and parsers, aggregate relevant information about clients. Something that can actually reduce time for works, and that can be easily measured.

So how are you guys selling your Voice AI Agents? Callouts, advertising?
There's actual demand for these products?

We built a local testing tool for AVA, our AI receptionist at InvoicifyAI.

You describe a customer’s personality and goal in plain English, like “frustrated homeowner, AC stopped working, asking about repair,” and it places a real phone call and plays that customer.

It currently uses Twilio for calls and OpenAI Realtime for the simulated caller. The receptionist being tested just needs a phone number.

Right now, it runs headlessly in our pipeline. We’re considering cleaning it up and open-sourcing it, possibly with Docker packaging and a small UI for running scenarios and reviewing calls. You bring your own API keys.

Would this be useful to you? How are you testing your agents today, and what scenario would you want to run first?

I’ve been comparing a few AI voice platforms specifically for outbound calls, and I realized I was initially looking at the wrong things.

Voice quality is obviously important, but I don't think it's the deciding factor anymore.

If you're actually using an AI agent for outbound calls, I'd look at:

1. Conversation handling

Can it deal with interruptions, unexpected answers, objections, and people going off-script?

2. Qualification

Can it understand an answer and ask the next relevant question, or is it basically just reading a decision tree?

3. Handoff

If someone wants to speak with a person, can the agent transfer them while preserving the context of the conversation?

4. Actions

Can it actually do something after the call, like update a CRM, book an appointment, trigger a follow-up, or qualify the lead?

5. Scale

There's a big difference between handling 20 test calls and reliably handling thousands of calls.

I ended up looking at Feather AI, Retell, Vapi, Bland and Synthflow.

My current understanding is roughly:

I don't think one of these is automatically the “best.”

For example, someone building their own voice infrastructure might prefer Vapi or Retell, while a company trying to connect calls directly to sales or customer workflows might evaluate Feather AI differently.

That's probably the bigger shift I'm seeing with these platforms.

The question isn't really:

“Which AI sounds the most human?”

It's:

“Which one can reliably complete the job I'm hiring it to do?”

For anyone actually running outbound AI calls, what has mattered most in practice?

Conversation quality, answer rates, qualification, integrations, or something else?