u/here_vii

As a agency owner i have been using so many platforms(retell,vapi, sarvam, ultravox etc) and tools for building a voice agents to my clients. Every tools has good and bad views according to me. Some has good understanding, some are good at latency, some are easy to connect with other telephony and api's.

I have been delivering voice agents according to clients needs, region, telephony supports so yeah varies with pricing.

As I'm at scaling period, i trying to reduce a still. i want to know more about what are the platforms are you guys using and per minute cost of each. if you guys reduced it, how did you do that and what is the cost of per minute?

Cause providing the maximum value with a affordable cost is our first thing - Take ownership.

As a agency owner i have been using so many platforms(retell,vapi, sarvam, ultravox etc) and tools for building a voice agents to my clients. Every tools has good and bad views according to me. Some has good understanding, some are good at latency, some are easy to connect with other telephony and api's.

I have been delivering voice agents according to clients needs, region, telephony supports so yeah varies with pricing.

As I'm at scaling period, i trying to reduce a still. i want to know more about what are the platforms are you guys using and per minute cost of each. if you guys reduced it, how did you do that and what is the cost of per minute?

Cause providing the maximum value with a affordable cost is our first thing - Take ownership.

When I first started building my chatbot, I was obsessed with getting the first version working. Every new feature felt exciting. I finally got the responses working, connected all the APIs, built the workflow, and watched it answer questions in real time. It honestly felt amazing because after spending days learning everything from scratch, I had something that actually worked. I remember thinking, "Now it's basically finished." Looking back, that was probably the biggest misconception I had. The first version wasn't the finish line—it was the beginning of a much bigger job.

After people started using it, I realized the chatbot was only as good as the conversations it had yesterday. Every day I found something new to improve. Some users asked questions I never expected. Others gave incomplete information and still expected the bot to understand. A few users skipped straight to complicated requests without any context. Then there were conversations where the chatbot technically gave the correct answer, but it didn't feel like the answer a real person would give. Those were the hardest ones to spot because nothing was actually "broken," but the experience still felt off.

I also noticed that I spent far more time reviewing conversations than writing prompts. Every failed interaction became another note on my list. Sometimes the problem wasn't the AI at all—it was my workflow. Other times it was bad instructions, missing context, or assumptions I didn't even realize I had made while building it. The more logs I read, the more I understood that chatbots aren't really built once. They're continuously improved through thousands of tiny adjustments that users never notice.

One thing that really changed my mindset was stopping the search for the "perfect prompt." I used to think there was one magical prompt that would solve everything. Now I think reliable chatbots come from good system design—clear rules, validation, fallback paths, human handoffs, and constantly learning from real conversations. The AI is only one part of the system.

I'm still improving mine every week, and honestly I don't think it'll ever be "finished." Every conversation teaches me something new. For anyone who's built a chatbot or AI assistant, did you have a moment where you realized the real work only started after people began using it? I'm curious what changed your approach the most.

A few months ago I decided to build a chatbot. At first I thought the hardest part would be the technical side of things. Learning the tools, connecting APIs, writing prompts, handling memory, creating workflows, and making everything work together. I spent hours watching tutorials, reading documentation, testing different models, and trying to make the conversations feel natural. Eventually I got to a point where I felt pretty confident about it. Every test conversation worked exactly as expected. The bot answered correctly, followed instructions, stayed on topic, and handled the scenarios I had prepared for. Looking at it, I genuinely thought I was almost done.

Then real people started using it.

Within a few hours I realized how wrong I was. One person asked three questions in the same message. Another person gave half an answer and expected the bot to understand the rest. Someone completely changed the topic in the middle of the conversation. Another user used slang I had never seen before. One person replied with a single word. Another replied with a paragraph. Some people ignored the chatbot's questions entirely and started talking about something else. What looked like a solid system during testing suddenly felt fragile. Every conversation exposed a new edge case I had never considered. It wasn't because the AI was bad. It was because humans are unpredictable.

The funny thing is that the technical problems turned out to be easier than the human ones. If an API breaks, you can fix it. If a workflow fails, you can debug it. If a node throws an error, you can usually find the cause. But how do you handle a user who asks a vague question? How do you know when someone is joking, being sarcastic, frustrated, confused, or simply typing too fast? How do you build a system that works for people who communicate in completely different ways? Those problems don't have a simple fix. The more conversations I reviewed, the more I realized that building a chatbot isn't really about AI. It's about understanding how people think, how they communicate, and how messy real conversations actually are.

What surprised me the most is that every "failure" taught me something useful. The conversations that broke the bot were often more valuable than the conversations that worked perfectly. They showed me where assumptions existed, where instructions weren't clear enough, and where the experience felt unnatural. Instead of trying to build the perfect chatbot from day one, I started treating every weird conversation as feedback. Slowly the bot improved. Not because the AI got smarter, but because I learned more about the people using it.

Now when I see chatbot demos online, I look at them differently. Making a chatbot answer a few prepared questions is easy. Making it survive thousands of unpredictable conversations with real people is a completely different challenge. I'm still learning every day, but one thing I've realized is that the biggest lessons don't come from building the chatbot. They come from watching how real humans interact with it. For those who have built chatbots or AI agents before, what was the biggest thing real users taught you that testing never did?

I've been experimenting with Instagram content automation recently.
workflow/CHbcTEYwwzuYPYKZ

The idea sounded simple:

Create content → Schedule posts → Stay consistent → Get more reach.

But after looking at the results, I'm not sure consistency is the real bottleneck.

Some accounts post every day and barely get engagement.

Others post less often but seem to generate far more conversations and leads.

It made me wonder:

Are businesses struggling because they're not posting enough?

Or because they're posting the wrong content?

For those managing Instagram accounts:

What's made the biggest difference for you?

• Better content?
• Faster replies?
• More consistency?
• DM automation?
• Better offers?

Curious to hear what has actually moved the needle for people.

Everyone is selling "AI Voice Agents" right now. But if you are a business owner who has actually tried to deploy one to handle your customer calls, you probably realized the reality doesn’t match the hype.

A few months ago, I was auditing AI voice setups for businesses and kept hearing the same operational nightmares: The AI took 3 seconds to reply, it hallucinated, it couldn't reliably book an appointment to save its life, and the per-minute billing was completely unpredictable.

I decided to build a platform from the ground up specifically to solve these operational bottlenecks for mid-to-large-sized businesses.

Here are the biggest reasons AI voice fails in the real world, and exactly how we engineered a solution:

1. The "Awkward Pause" (Fixed the Latency) The Problem: Most AI agents take 2 to 3 seconds to process speech and reply. It leads to callers talking over the bot, getting frustrated, and hanging up. The Fix: We built over 10 custom internal optimization tools specifically to process audio faster. Our system responds in roughly 400 ms. It is so fast and fluid that callers rarely realize they aren't speaking to a human.

2. The "Dumb Bot" Syndrome (Flawless Tool Calling) The Problem: Most AI agents are just ChatGPT on a phone line. They can talk, but when a customer says "Can I book a slot for Tuesday?" the bot breaks, double-books, or fails to trigger the calendar. The Fix: We built rock-solid backend tool-calling. If a customer wants to book, the AI instantly checks your live calendar, secures the appointment, and triggers an immediate SMS and Email confirmation to the customer before they even hang up the phone.

3. Scattered Data & Ghost Leads (Built-in Custom CRM) The Problem: Business owners were having to log into three different platforms just to figure out what the AI said to a customer. The Fix: We built a dedicated, custom dashboard for every single client (I attached a screenshot of it below). It acts as your Voice CRM. The second a call ends, the dashboard updates with:

• The full audio recording.
• A complete text transcription.
• AI Sentiment Analysis: It automatically tags calls (e.g., "Frustrated", "Ready to Buy", "Needs Follow-up") so your human sales team knows exactly who to prioritize calling back.

4. Unpredictable, Skyrocketing Bills The Problem: Other platforms charge you a markup for every minute the AI is on the phone. If you have a busy month, your software bill skyrockets unpredictably. The Fix: We use a BYOK (Bring Your Own Key) model. You just plug in your own Twilio account to handle the raw phone lines at wholesale cost, and we provide the entire AI brain, CRM, and dashboard for a single, predictable flat monthly fee. No surprises, no matter how much you scale.

I see too many business owners giving up on AI automation because they were sold a cheap, unreliable setup. When built correctly, having an agent that answers every inbound call instantly, books appointments flawlessly, and logs everything into a CRM is a superpower.

Business owners who have tried AI calling—what was the biggest failure point or frustration you ran into?

I’ve been experimenting with AI workflows in n8n for handling customer support messages.

The setup works well for repetitive questions, but random user inputs still sometimes produce weird or inaccurate replies.

I’m curious how others are solving this problem inside n8n.

Are you mostly:

• using strict prompts
• adding moderation layers
• relying more on rule-based flows
• or keeping humans in the loop?

Still testing different approaches and wanted to learn how others here are building reliable AI automations.
https://n8n.io/workflows/3664-build-an-ai-powered-customer-support-chatbot/?utm_source=chatgpt.com

Hey everyone,

I’ve been working closely with local moving and storage businesses lately, and I noticed a massive problem with how software is priced for your industry.

If you try to use a major CRM like ClickUp, HubSpot, or Salesforce, they charge you per user, per month. If you have a dispatcher, 2 office admins, and 10 contractors/movers on the road, suddenly you are paying $300 to $500+ every single month just for software.

To avoid the fees, I see owners sharing a single login password across the whole team—which completely ruins your ability to track who did what, and is a massive security risk.

I’m a software developer, and I realized this pricing model is broken for physical service businesses with high employee counts.

So, I built MoveAll CRM.

It’s an all-in-one dashboard built specifically for moving and storage, featuring:

• A clean Job Schedule where contractors only see the jobs assigned to them.
• A Storage Masterlist to track client items and billing.
• Automated Lead-to-Contact syncing.
• Automated customer updates via SMS/Email (so they stop calling you for ETAs).

The catch? There are no per-user monthly fees. I am pricing this as a **, Monthly one-time payment.**and you can add as many staff members and contractors as you want without your bill ever going up.

Hey everyone,

I’ve been working closely with local moving and storage businesses lately, and I noticed a massive problem with how software is priced for your industry.

If you try to use a major CRM like ClickUp, HubSpot, or Salesforce, they charge you per user, per month. If you have a dispatcher, 2 office admins, and 10 contractors/movers on the road, suddenly you are paying $300 to $500+ every single month just for software.

To avoid the fees, I see owners sharing a single login password across the whole team—which completely ruins your ability to track who did what, and is a massive security risk.

I’m a software developer, and I realized this pricing model is broken for physical service businesses with high employee counts.

So, I built MoveAll CRM.

It’s an all-in-one dashboard built specifically for moving and storage, featuring:

• A clean Job Schedule where contractors only see the jobs assigned to them.
• A Storage Masterlist to track client items and billing.
• Automated Lead-to-Contact syncing.
• Automated customer updates via SMS/Email (so they stop calling you for ETAs).

The catch? There are no per-user monthly fees. I am pricing this as a single, one-time payment. You buy it, you own it, and you can add as many staff members and contractors as you want without your bill ever going up.

I am looking for a handful of business owners to test it out before I officially launch.