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I built a simple but powerful lead automation system using n8n.
When a new lead comes in:
-Captures the data (name, email, budget, message)
-AI classifies the lead (HOT or COLD)
-Adds it to Google Sheets automatically
-Sends instant email alerts for HOT leads
-Sends an automated follow-up email to the lead
-Schedules additional follow-ups based on response (or no response)
No more checking forms, chasing leads, or forgetting to follow up. Everything runs automatically.
This system can be used for:
-Lead generation & qualification
-Client onboarding
-Contact form automation
-Sales pipeline management
-Booking and inquiry handling
-Customer support triage
-Automated follow-ups & nurturing
For this example:
A lead with a $500 budget and interest in automation was marked as HOT, logged, and contacted immediately.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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Automated Document Generation using Carbone.io
I just completed the second phase of the order automation system I shared a few days ago.
Here’s how this part of the workflow works:
Once an order is manually approved in Airtable, the system automatically sends the order data to Carbone.io, where a PDF document is generated with all the order and fulfillment details.
After the document is generated, an automated email is sent with the PDF attached to confirm the order and notify the retailer.
The generated documents are then automatically stored in a dedicated Google Drive folder for record keeping and easy access.
I’ll be sharing a full video walkthrough of the system very soon.
#Automation #Airtable #Makecom #NoCode #WorkflowAutomation #DocumentAutomation #CarboneIO
AI demos are easy.
Production AI is where engineering begins.
Anyone can connect an LLM API and make something that looks impressive in a demo.
But real businesses need systems that survive messy users, bad inputs, edge cases, and scale.
Production AI means:
✅ Grounded retrieval (RAG)
✅ Reliable tool/API execution
✅ Structured outputs
✅ Agent workflows with business logic
✅ Monitoring, evals, and feedback loops
✅ Infrastructure that scales in production
The difference between an AI demo and an AI product?
Architecture.
That’s where most “AI startups” break.
I build AI systems that go beyond prompt wrappers — production-ready LLM agents, RAG pipelines, and full-stack AI products.
If you're building something real, build it like software.
#AIEngineering #LLMAgents #RAG #GenerativeAI #LangChain #LangGraph #OpenAI #ClaudeAI #Python #FastAPI #AWS #VectorDatabase #AIProduct #SaaS #ArtificialIntelligence #Contra #FreelanceDeveloper #AIConsultant
This is amazing
I built a machine that turns you into a literal GOAT 🐐
Introducing The GOAT-i-fyer, my official submission for the #ElevenCreative Challenge!
Drop in any selfie, and this multi-node template will show you how you'd look as a fabulous goat - PLUS it'll build a fully voiced, lip-synced version of your new alter-ego.
What makes this a true G.O.A.T. workflow:
⭐ It scans your photo and maps your unique real-world traits (like your hair color, expression, or accessories) directly onto a high-fidelity goat avatar
⭐ It doesn't stop there - it also scripts a punchy, spoken monologue that syncs perfectly to the goat's mouth movements
Try out the template, and PLEASE show me your results in the comments!
& if it makes you laugh, smash that UP-GOAT button (if not now, when?) 👇
This is so cool and fun!
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