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Make Google Workspace Automation for Gmail and Sheets
I built Make automation workflows that connect Google Workspace tools so teams can reduce copy-paste work and keep information moving between Gmail, Google Sheets, and business processes.
Problem
Teams often spend time moving data from emails into spreadsheets or checking tools manually.
Solution
I created Make workflows that connect Gmail and Google Sheets so information can be captured, organized, and routed automatically.
What it does
Captures useful information from Gmail
Sends or organizes data into Google Sheets
Triggers follow-up actions based on workflow logic
Reduces manual copy-paste and checking
Keeps repeated admin processes more consistent
Tools used
Make, Gmail, Google Sheets, Google Workspace automation, workflow integration.
Outcome
The workflow helps reduce repeated admin work, improves data organization, and makes Google Workspace processes easier to manage.
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Automated a manual Google Sheets → Slack workflow to eliminate repetitive data entry and give the team instant notifications.
Previously, new data had to be manually checked, processed, and shared across the team — slow and easy to miss. I built a Make.com scenario that:
Watches Google Sheets for new/updated rows
Processes and routes the data through a router based on conditions
Creates corresponding Trello cards for task tracking
Logs processed records back to Google Sheets
Sends real-time Slack notifications so the team is instantly updated
Result: What used to be manual checking and updating is now fully automated — saving hours weekly and removing room for human error.
Tools: Make.com, Google Sheets, Trello, Slack
wow love this
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.
Obviously automated