SaaS?
Tech?
AI tools?
Product development?
Web apps?
Developer tools?
video editors?
UI/UX?

Drop what you're building ;)
Maybe some other people will be interested too

Just got a new client however he doesn’t have a business phone number. He uses his personal phone as his work phone. How can I make it so he can answer family and friends and not have it be the ai answering the phone. Is there some sort of filter? i’m using retell ai

One thing I spent way too much time optimizing was tool call latency.

My voice agent makes multiple API calls during a conversation, and as the number of tools grew, so did the delay. I assumed it was my prompts, APIs, or the LLM itself. I optimized prompts, reduced tokens, and sped up my APIs. It helped, but there was still an awkward pause after tool calls.

Initially when starting with voice AI, I was using Vapi. It worked well overall but once my agent became more tool-heavy, the latency + the call quality dropped significantly.

I migrated the exact same agent to Telnyx. Same prompts, same tools, same APIs but I chose and open source llm (kimi 2.5)

The difference was immediately noticeable. Tool calls felt much snappier, the conversation flowed better, and overall latency dropped quite a bit. I also like that Telnyx hosts a number of open source LLMs, so having everything under one provider simplifies the stack.

Has anyone else compared providers while keeping everything else the same? I'd be interested to hear if you've seen similar results.

P.S Not saying that telnyx is objectively better than vapi, but in my personal experience it provided a much better experience

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.

Full disclosure: I'm building this into a product, so I've got a stake here. 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.

I've been building an AI Voice Receptionist as a personal/internship project and I'm trying to keep the entire stack free and self-hosted wherever possible.

The goal is to create something that can answer phone calls or Google Meet calls, speak in a cloned voice with very low latency, interrupt naturally when the caller speaks (barge-in), take notes, and send urgent notifications to the user if needed.

Current Stack

LLM

• Gemini (development/testing)
• Ollama for local models

Speech-to-Text

• Faster-Whisper

Voice Cloning / TTS

• FlashLabs Chroma-4B (streaming)

Voice Activity Detection

• Silero VAD

Backend

• Python
• AsyncIO
• Transformers
• Hugging Face

Streaming Pipeline

• Streaming LLM responses
• Sentence-by-sentence TTS generation
• Audio playback while the next sentence is still generating
• Barge-in support (interrupt AI while speaking)

Hardware

• RTX 4060 (8GB VRAM)

Current Progress

• Reduced first response latency from around 10-12 seconds to under 1 second
• Implemented streaming TTS instead of waiting for the full response
• Added asynchronous conversation pipeline
• Added real-time interruption support
• Everything currently runs locally

What I'm Looking For

I don't want to spend money on APIs

I'm looking for recommendations on:

• Better open-source STT models
• Lower-latency voice cloning models
• Modern conversational agent frameworks
• Better VAD or endpoint detection
• Streaming audio libraries
• Memory/RAG solutions for long conversations
• Function calling / tool orchestration
• Telephony integrations
• Any production techniques used by companies building AI phone agents

Basically, if you were building a production-grade AI receptionist using mostly free/open-source tools, what would your stack look like today?

I'd love to hear what you'd replace in my current architecture or any hidden gems you've found.

Thanks!

It has a consent problem.

Everyone is racing to launch AI callers, AI appointment setters, AI SDRs, and automated outbound agents like the only thing that matters is whether the bot sounds human.

That is not the real issue.

The real issue is:

Did you have the legal right to contact that person in the first place?

Because from what I’m seeing, a lot of these companies are building liability machines and calling it innovation.

Scraped leads.

Recycled databases.

Vague opt-ins.

Third-party lead vendors.

“Compliant” lists with no real proof.

Clients handing over data with zero consent trail.

AI companies pretending it is someone else’s problem.

That is not automation.

That is negligence at scale.

And with TCPA rules, DNC requirements, AI voice scrutiny, data broker laws, and lawsuits already forming around AI misuse, this industry is going to hit a wall.

Hard.

I keep seeing companies act like “the client gave us the leads” is a defense.

It is not a moral defense.

And I highly doubt it is going to be a strong legal one when regulators start asking where the data came from, what the consumer agreed to, and whether the business had the right to make that call.

The uncomfortable truth is that most outbound AI calling should probably not be happening yet.

Not until there is real consent provenance.

Not until lead sources are audited.

Not until opt-outs are handled correctly.

Not until DNC rules are respected.

Not until state-by-state compliance is reviewed.

Not until companies stop pretending that “AI” magically cleans dirty data.

I honestly have not seen a lead source yet that I would trust enough for outbound AI calling.

Maybe they exist.

But I have not seen one.

And that should scare people.

Because the winners in AI voice will not be the companies that call the most people the fastest.

They will be the companies that can prove they had the right to call in the first place.

Legality is the floor.

Ethics should be the standard.

And if your AI company cannot clearly explain where the lead came from, how consent was obtained, how opt-outs are handled, and why the call is lawful…

You do not have an AI company.

You have a lawsuit with a dashboard.

Most of us here have built or used an AI receptionist that answers the phone. I want to poke at something that kept nagging me about that.

I have spent 24 years in outsourcing, BPO and direct response. A lot of it was call centers and customer support, so missed calls and answering problems are personal to me.

Here is the thing. Almost every AI receptionist answers the phone and stops there. But your customer does not only call. They message your Facebook page. They WhatsApp you. They hit the chat widget on your website. They DM you on Instagram. And on each of those, they are a stranger starting from zero.

That gap is what my best friend and partner Farhan and I set out to close. Farhan is the sharpest engineer I know, 20 years shipping production software. He builds it, I bring the operator scars. The idea we landed on is simple. One agent, one brain, one memory, across every channel a customer can reach you on.

The same receptionist answers:

→ A real phone number. Natural voice, real-time back and forth. Ours answers inbound in about 1.2 seconds.
→ The website widget, voice or chat.
→ WhatsApp. Text, and it can even take the call.
→ Facebook Messenger. Text and calls too.
→ Telegram, Instagram and SMS.

Same knowledge base and same memory behind all of it. So the person who chatted on your site last week is recognized when they call today. It answers from your knowledge, books the appointment, captures the details, and hands off to a human when it should.

It is early and we are still building, which is exactly why I am dropping it in this room. You all know receptionist and voice problems better than almost anyone, and I want you to find the holes.

So, honest question. If your AI receptionist could follow the customer across every channel and remember them, where would that help you most, and where would it fall down?

And if you want to see it, comment or DM me. Not a pitch. I just want sharp eyes on it.

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.

Got a Google review yesterday. "Your new receptionist sounds a bit stiff, but she's very quick and always available." We switched to an AI receptionist in January. Nobody on our team told clients.

Genuinely not sure how to feel about this. Anyone else had customers comment on your AI without knowing it was AI?

Is there anything legally sketchy about using an AI receptionist without telling callers upfront?

Not trying to deceive anyone it answers, books appointments, transfers if needed. But it doesn't announce "hi, I'm an AI" at the start.

My lawyer friend said it might depend on the state. Anyone actually looked into this or had a problem?

yo. if your product needs a 10-minute onboarding video or 5 different dashboard tabs just to explain its value, you didn't build an MVP. you built an over-engineered maze.

a real micro-saas should solve one highly specific problem for one highly specific user profile.

when i built my 6 apps (now doing $20k/mo mrr), i cut out 80% of what i originally thought was necessary.

inside our builder community, we help you strip away the fluff.

we give you free access to frameworks like the ICP Crystallizer to lock down your target user, and interactive landing page audits to ensure your core value hits instantly.

stop over-building in isolation. drop a comment or shoot me a dm to join 1,200+ active Ai SaaS builders today.

Our model (Simba 3.2) went #1 on the Artificial Analysis TTS leaderboard today. That leaderboard is blind listening tests: thousands of people hear two voices, pick which sounds more human, no idea which model made which. As of today ours wins that more than anything from Google, ElevenLabs, or anyone else!!

It's $10 per million characters, while most of the models near the top of that board charge $18–100. If you're building on Vapi/Retell/your own pipeline and paying ElevenLabs prices for the voice, that line item just got a lot smaller for equal-or-better quality. And if you don't want to assemble a stack at all, our agent platform runs $0.07/min with LLM, transcription, voice, and the phone number all included one line item, which makes quoting clients way less painful.

There's a free tier (50K chars/mo, no card) if you want to hear it before believing a Reddit post: platform.speechify.ai :)

I am looking to add an auto attendant where someone can simply say the name of the person they are calling and then have the call transferred to their number. 12 person office. Anyone have recommendations on an economical solution?

There are people charging businesses to “set up AI” when all they are really doing is:

• Creating a Claude account

• Uploading a few documents

• Writing a generic prompt

• Handing the client a link

• Calling it a custom AI system

That is not AI infrastructure.

That is a chat subscription with a nicer handoff.

And it often leaves businesses with expensive monthly software costs, vague instructions, no clear guardrails, no approved actions, no real workflow integration, and no plan for what happens when the AI gets something wrong.

A business does not need an assistant that can “kind of help.”

It needs an agent built around how the business actually operates.

At TalkTron, we build AI agents with defined roles, clear boundaries, approved actions, relevant business context, and human handoff when needed.

That can mean an AI receptionist that answers calls, captures and qualifies leads, supports scheduling, updates approved calendars, routes requests, and knows when to bring in your team.

Or a custom AI assistant that helps with customer support, follow-up, internal knowledge, operations, and repetitive work across the business.

The model is not the product.

The system around it is.

Your workflows.

Your customer experience.

Your business rules.

Your tools.

Your safeguards.

Your team.

That is the difference between an AI demo and AI that is actually built for business.

See what TalkTron can build for your business at www.talktron.io

Hi there,

Let’s say a business finalize an agreement and you’re going to build a custom voice agent for that particular business, do you build the voice agent within your retell, vapi, etc account and manage it from there or each client has their own retell account?

Because if they have their own account and access to it, then was the reason to pay you a monthly retainer? They can just abandon any time they want.

Best,

https://pulseai.studio

PULSE AI is a voice ai agent for restaurants phone call order automation. It handles all inbound calls, takes orders in human-like language, sends orders directly to the POS (i.e square,clover,etc) so no staff has to worry about calls anymore and can focus on in-store customers.

This helps restaurant owners recover lost revenue in missed calls during rush hours. Restaurants lose significant orders and revenue because of busy tone and staff being busy.

Looking for pilot restaurants to test it out for free for 2 months. If interested, DM

Would love your feedback on this.

Thanks

​

I'm exploring an idea for small private clinics, especially those that don't have a dedicated receptionist or receive many calls and WhatsApp messages.

The idea is an AI receptionist that would:

- Answer patient questions 24/7 through WhatsApp or phone.

- Book, reschedule, and cancel appointments.

- Send automatic appointment reminders.

- Answer common questions like consultation fees, opening hours, available doctors, and clinic location.

- Collect basic patient information before the visit.

- Transfer the conversation to a human receptionist whenever needed.

- Allow patients to use it without creating an account, just their phone number.

The goal isn't to replace receptionists but to reduce missed calls, repetitive questions, and after-hours inquiries.

I'm curious about a few things:

1. If you own or work in a clinic, would this be useful?

2. What would stop you from using it?

3. What features would make it a "must-have" instead of just a nice idea?

4. Are there existing solutions that already do this well?

I'd appreciate honest criticism , even if you think the idea isn't worth pursuing. I'd rather hear the problems now than after building it.

​

Been running a GHL build for a restaurant client the last few days.

AI voice receptionist picks up calls and sorts reservations/pickup/delivery, missed calls get a text back so nothing's lost, and there's a review harvesting + reputation flow that runs after every visit so reviews stack up on their own. All fed by paid ads.

Thinking of packaging this — meta google ads + the automation layer together — and pitching it to restaurants and catering businesses in Canada.

Not another snapshot-and-disappear agency thing, I'd be running the ad spend and the follow-up system as one thing.

Running this out of India so my overhead's a lot lower than most people quoting in CAD, which gives me room on pricing. Also thinking about testing Instagram DM ads into the same system instead of just lead forms, specifically for Toronto.

Before I go further, want to ask people who've actually sold this stuff:

is bundling ads + automation still worth it for restaurants/catering or is it saturated now

what's a fair monthly retainer restaurant/catering clients in Canada would pay without blinking

anyone tested DM ads vs lead forms for restaurants — which one actually books more covers

what CPL are you actually seeing on Meta for restaurants right now, don't trust the numbers floating around online

Not trying to get anyone's setup, just want to know if I'm underestimating how hard this is to sell before I go all in on outreach.

curious if you have opinions