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🚀 Project title - AI Lead Capture & Email Automation
💡 Short description - AI-powered lead automation that captures, qualifies, organizes, and routes new leads automatically. 🤖⚡
📝 Project description - Built an AI-powered lead processing workflow using n8n to automate the entire journey from form submission to lead notification. 🤖
How it works:
📝 Captures customer details and service requirements through a custom form
🔗 Sends submissions into an automated n8n workflow
🧠 Uses AI to analyze and qualify incoming leads
📊 Automatically stores lead data, summaries, status, and priority in Google Sheets
📧 Sends instant email notifications with the lead's priority, details, and AI-generated summary
Result: Less manual lead processing, faster response times, and a centralized system for managing incoming leads. ⚡
Tech: n8n • AI • Google Sheets • Email Automation • API Integration
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Really clean and polished app design! The booking flow looks intuitive and the overall UI feels modern and easy to use. 👏
good work
AI Assistant Using Your Business Knowledge Base — RAG on Your Documents
THE PROBLEM
Q&A bots break down when knowledge lives in documents: a 100-page manual has no "questions" to match, it can't fit into a prompt, and generic chatbots hallucinate instead of admitting what they don't know.
THE SOLUTION
A RAG (Retrieval-Augmented Generation) knowledge base: documents are split into meaningful chunks, embedded into a vector index, and the assistant answers from the right sections — by meaning, not keywords.
Any format as-is: PDF, DOCX, TXT, Markdown — 100+ pages is fine
Answers grounded in YOUR documents — it says "I don't have that information" rather than inventing
Source references — every answer shows which document and section it came from
Runs on your infrastructure — documents never leave your control
One command to re-index after updating documents — documented, no programmer needed
LIVE DEMO
Ask it about my products, pricing, deployment and support — it answers from 6 real documents with sources:
https://kb-assistant-demo.dvc-ai.workers.dev
The 'I don't have that information' line is the part most RAG builds skip, and it's the one that matters. How do you set the cutoff? On mine, a fixed similarity threshold broke once I filtered results by user role. Scores shifted and it refused questions it could answer.
Built an AI agent that handles access requests end-to-end inside Slack — reviewing, approving, and notifying automatically.
Manually tracking who requested access to what (and following up over email) was slow and easy to lose track of. I built a Make.com scenario that:
Watches Slack for new access request messages
Shows a typing indicator while the AI Agent processes the request
The AI Agent (Access Request Agent) evaluates the request and decides the next action
Depending on the decision, it logs the request in Google Sheets, sends an email via Gmail, or replies in Slack
Sends a confirmation message back to the requester in Slack
Result: Access requests are now reviewed and routed automatically — no manual follow-up, and every request is logged and answered consistently.
Tools: Slack, Make AI Agents, Google Sheets, Gmail (built on Make.com)
