I automated lead follow-up and management for Miale to reduce the manual work involved in reviewing enquiries, categorizing leads, responding to prospects, and keeping the team informed.
The problem:
When leads come in, manually reviewing each response, deciding how to handle it, sending emails, and notifying the team can take time, and makes it easier for follow-ups to slip through the cracks.
The solution:
I built an AI-powered workflow in Make that automatically processes new lead submissions.
Here’s how it works:
Google Form → Google Sheets → AI Classification → Router → Email + Slack → Lead Tracking
When a prospect submits the form, AI analyzes their collaboration interest and categorizes the lead as Collaboration Lead, Needs Follow-up, or Not Interested.
The workflow then automatically:
- Routes the lead based on their response.
- Sends a relevant email to the prospect.
- Notifies the team in Slack when a new lead needs attention.
- Updates the lead record for easier tracking.
Tools used:
Make · Make AI Toolkit · Google Forms · Google Sheets · Gmail · Slack
The workflow can also be adapted for client enquiries, sales leads, service requests, bookings, applications, and other repetitive lead-management processes.
The goal isn't simply to automate tasks. It's to build a system that helps businesses respond faster, stay organized, and reduce manual follow-up 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
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.