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24 contributions to Wifi Life
AI Cold-Calling Voice Agent — Handling Rejection (Episode 3)
This is Episode 3 of my AI Cold-Calling Voice Agent series. In this demo, the AI agent handles “not interested” responses cleanly — something most human callers struggle with. The agent: - Respects boundaries - Ends the call professionally - Keeps the brand intact Built using VAPI + AI automation workflows. If you’re building AI agents for sales, outreach, or service businesses, happy to break down the logic behind this behavior.
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AI Cold-Calling Voice Agent — Handling Rejection (Episode 3)
How I built an "AI-Research Assistant" that saves 10+ hours a week (and tracks every competitor)
Let’s be real—manually tracking YouTube competitors is a massive time-sink. I see so many creators and agency owners spending hours every week jumping between tabs, checking view counts, and trying to "guess" what content is actually trending. That’s a waste of high-level talent. I decided to solve this with a 2-part n8n automation engine. Now, the data comes to me. Here’s the breakdown of the system: 1️⃣ The Data Harvester: It monitors a list of target channels in Google Sheets. Every time a new video drops, it automatically pulls the views, likes, comments, and tags. No more manual data entry. 2️⃣ The Outlier Detector: This is where the ROI happens. The system calculates the average performance of a channel and only alerts me when a video is an "outlier" (performing way above average). 3️⃣ The Intel Report: It generates a professional HTML newsletter and drops it in my inbox. I can see the "viral signals" while I’m drinking my morning coffee. ☕ The ROI? Time: 10+ hours/week saved (that's 40+ hours a month to focus on high-ticket sales). Money: Replaces the need for a $1,500/month research assistant. Strategy: You stop guessing and start creating content that is backed by real-time competitor data. I’m curious—how many of you are still tracking your market research manually? If you want to see a walkthrough of how the nodes are set up or want this built for your business, drop a "TRENDS" in the comments below! 👇
How I built an "AI-Research Assistant" that saves 10+ hours a week (and tracks every competitor)
1 like • 7d
The 'Outlier Detector' logic is brilliant. Most people just pull raw data, but setting up a baseline average to filter for viral signals is where the real leverage is. Are you using a specific n8n function to calculate those averages over a set period (like the last 30 days), or is it pulling from a historical database in the Sheet?
$2,227 in 28 Days with YouTube Automation 🚀💰
From $0 to $2,227 in just 28 days this is how YouTube automation really works. One viral push, smart monetization, and the right audience can change everything overnight. No face, no talking, juststrategy and systems doing the heavy lifting. If you want results like this 👇 Check under the comments and join 👇 our telegram channel OR DM 📥 me directly on WhatsApp
$2,227 in 28 Days with YouTube Automation 🚀💰
0 likes • 7d
Solid results for a sub-30 day window! 📈 Are you leaning more into high-RPM niches like Finance/SaaS to hit those numbers that fast, or is this a high-volume play in a broader entertainment niche? Would love to know if this was driven more by search or the browse features.
This channel wasn’t built on hype — it was built with strategy, consistency, and automation.
This channel wasn’t built on hype — it was built with strategy, consistency, and automation. In the last 28 days: 737K+ views, 360K+ watch hours, +3.4K subscribers, and around $10K in estimated revenue — all from a faceless YouTube automation channel. No filming. No personal brand. Just smart niche selection, high-retention content, and a system designed to scale. If you’ve been thinking about starting but keep waiting, this is your sign. YouTube is rewarding creators who take action and build the right systems. 📩 Message me now if you’re ready to start your own profitable YouTube automation channel.
This channel wasn’t built on hype — it was built with strategy, consistency, and automation.
0 likes • 7d
These numbers are incredible for 28 days! 🚀 It’s a perfect case study in why systems win over luck. People underestimate how much 'high-retention' depends on a repeatable workflow rather than just a one-off viral hit. The $10k revenue is proof that the strategy is dialed in.
AI Cold-Calling Voice Agent — Handling Receptionists (Episode 2)
This is Episode 2 of my AI Cold-Calling Voice Agent series. In this demo, the AI agent is trained specifically to handle receptionists and gatekeepers, which is usually where most cold calls fail. The agent: - Stays calm and professional - Avoids sounding salesy - Qualifies the right contact Built using VAPI + AI automation workflows. If you’re building AI agents for sales, outreach, or service businesses, happy to break down how this is set up.
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AI Cold-Calling Voice Agent — Handling Receptionists (Episode 2)
1-10 of 24
Abdul Raffay
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@abdul-raffay-9656
Raffay

Active 1d ago
Joined Aug 20, 2025
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