Lyfe

I am Joseph, an AI Automation Specialist. Data Entry & Operations Specialist. Web3 Ambassador. I have rebuilt this page with one clear purpose; to document the systems I build, the problems I solve and the results that follow. 🧵👇

@Sungjinwoo70 Nothing for you🙏

Today’s the D-day. I’ve waited and also thought about whether to do it or not Finally taking a bold step today……..Pray for me guys🙏🏾

@Sungjinwoo70 Things I love to see😂🙏

I was friend-zoned💔💔

If you see engagement for your post today, you be big account…

@frontend__fairy I think say na me get bad luck sef😂

@bidhypergoat The future of this project looks great Bid the goat about to be the GOAT Can I get a dm?

@penguinxbt_ Gm gm Great project here Can I get a dm?

gm peng 🐧 we go our own way… solana:6anryGPu48Pa7trnfoxYum5uWmfBjyvcwUhcEDLYFioa 🐧🐧

@bidhypergoat Amazing project you have here Can I get a follow back?

@theuniqueuser8 @X @buildinpublic Hey! Im building FlyTradr — trading analytics that uses AI to help regular folks make smarter trades. No bloated charts, just clear signals. Been building for a while now, great to meet other tech folks here. Followed from my end, lets connect

Appreciate this comment. On the multi-intent question, it's a real challenge but the system handles it. Rather than routing to highest confidence, I solved it at the prompt level with a hardcoded Priority Order the model must follow whenever signals conflict: HR → Customer Service → Finance → Operations → Sales → Needs Human Review So for the support request that looks like a sales lead? The moment it contains words like error, login, dashboard, or account issue, it goes to Customer Service. Sales sits at the bottom of the priority chain intentionally. It only wins when the sender is provably a new prospect with zero existing relationship signals in the email. The other layer is that the model fetches the full email body, not just the subject or snippet, so it has complete context before classifying and Ambiguous emails that genuinely can't be resolved by the priority order get flagged to a Needs Review channel with the confidence score and classification reason attached so a human makes the call rather than the system guessing. Honestly the bigger fix was making Sales earn its classification rather than being the default catch-all. That alone eliminated most of the misrouting.

@BuildwithLyfe Clean stack n8n plus Groq is underrated for email workflows. Built fintech automations and classification gets messy when emails are ambiguous - support request that is also a sales lead. How do you handle multi-intent or just route to highest confidence

I built an AI-Powered Email Classification System using n8n + Groq AI + Gmail + Slack + Google Sheets ⚡ The workflow: • monitors emails • classifies intent with AI • generates unique ticket IDs and routing variables • routes requests automatically • labels emails • notifies departments • sends auto-replies • logs operational data Zero manual sorting. Zero missed emails. Runs 24/7. Here's exactly how it works 🧵👇 It starts the moment an email lands in Gmail. → Gmail Inbox Trigger fires the workflow → Prevent Reply Loop checks it's not an automated reply → Get Full Email pulls the complete content → Prepare Email Content formats it for AI processing The pipeline is already running before you've touched your phone. The email hits GroqAI; a fast AI classifier that reads the content and decides what type of email it is. → Customer Service? → Human Resources? → Finance? → Operations? → Sales? → Needs Human Review? While the Code Node bridges AI with automation. It: 🧩 parses AI output 🎫 generates unique ticket IDs 📌 structures records ⚡ prepares routing variables Before anything gets routed, an integrity check runs. If the AI classification fails, or returns something unexpected: → Admin gets an instant Slack alert → The email doesn't get lost → A human steps in only when actually needed Precision built into every step. The Smart Routing Engine takes the AI decision and fires it to the right team automatically. ✅ Customer Service → CS Department notified on Slack ✅ HR email → HR Department notified on Slack ✅ Finance → Finance team alerted ✅ Operations → Ops team notified ✅ Sales → Sales team on it instantly ✅ Needs Review → Escalation alert fired Every email lands exactly where it belongs. After routing, the system wraps up automatically: → Gmail Auto Reply sends acknowledgement to sender → Processing duration is logged → Row appended to Google Sheets for documentation → Email marked as Classified, and as Read Clean inbox. Full audit trail. No manual admin. What this replaces: ❌️ Someone manually reading every email ❌️ Forwarding to the right department ❌️ Copy-pasting into a tracker ❌️ Sending acknowledgement replies ❌️ Chasing emails that fell through the cracks. This is not just automation. This is operational intelligence. This is the standard of work I bring to every project. If your organisation is ready to eliminate manual operational workload and build intelligent systems that scale, I'm readily open to projects, consultations and strategic collaborations 📧 Inquiries: @BuildwithLyfe

Most people are using AI as a chatbot. The ones ahead are using it as an employee. ❌ Chatbot- you ask, it answers, you do the work ✅ AI Agent- it monitors, decides, executes & reports back A well-built AI agent: → Scrapes & structures data automatically → Triggers actions based on real-time inputs → Manages workflows across multiple platforms → Operates 24/7 with zero supervision This isn't the future. It's already being deployed. Are you building with it or falling behind it?#AIAutomation #AIAgents #WorkflowAutomation #BuildInPublic #NoCode #LowCode #OperationsStrategy

@Hopeville27 This is a nice workflow Currently working on something like this as well

Candidate submits an application through a recruitment portal AI reviews the application AI assigns a score and hiring recommendation HR receives a summary The candidate receives a confirmation email Everything is stored in a recruitment database No manual data entry.

2. Text Classifier This node completely changed how I build chatbots. Before: Customer: “Can I see a picture of the hoodie?” AI Agent: “The hoodie is a stylish…” The AI understood the request. But it answered the wrong thing. The customer wanted an image. Not a paragraph. Now I use a Text Classifier first. Image request? → Send Image Node Complaint? → Escalation Route Order Status? → Database Lookup General Question? → AI Agent The best AI systems don’t make the AI do everything. They make it do only what requires intelligence.

Most businesses are still missing calls. Not because they don’t care. But because humans can’t always respond instantly. So I built an AI Voice Agent for a GYM. 🏋️🤖 A customer can call in and: • ask about membership plans, • inquire about pricing, • check available programs, • ask opening hours, • and have their details stored automatically. Built in n8n. Tech breakdown for the builders: → MCP Server Trigger Acts as the bridge between the voice interface and workflow execution. → LiveKit Hosts the real-time voice conversation layer through the MCP endpoint. → Google Sheet Tool #1 (GYM Knowledge Base) The AI agent retrieves structured gym information directly from sheets: membership plans, schedules, pricing, trainers, and policies. → Google Sheet Tool #2 (Customer Details) Stores caller information automatically for lead tracking and follow-up. What makes this interesting isn’t just the AI speaking. It’s the business impact. Business owners can now focus on the things that actually move the money needle: closing deals, improving operations, growing revenue… while a reliable AI system handles incoming inquiries, responds instantly, and ensures potential clients don’t slip away because someone missed a call. That’s the shift AI automation is creating. Not replacing businesses. Making them more responsive, scalable, and available 24/7. And technically, this was another reminder that: AI agents become significantly more reliable when connected to structured retrieval systems instead of relying purely on model memory. Imagine this adapted for: • restaurants, • hospitals, • hotels, • salons, • real estate, • customer support teams. Businesses are slowly moving from: “Please hold while we attend to your call…” to: “The system already handled it.” 🚀 #AI #AIAutomation #VoiceAI #n8n #BuildInPublic #AIEngineering #Automation #MCP #LiveKit

@introvertdevs26 Let's build together mate 🚀

Building a team as we speak. Anyone who wants to work and help i can send telegram admin. Vetting will be probably if you hold over 2%. $ID

So today, I built an AI-powered recruitment system to help businesses and startups save hours from manually: • Reviewing applications • Sending candidate emails • Organizing applicant data • Deciding who to shortlist Here's how it works: 👇

Been looking for the next gem…rugs after rugs When the next $WHITEWHALE??