🎯 Engineered an autonomous post-meeting triage engine that monitors raw meeting notes, leverages AI to extract core action items, and generates structured project tasks inside ClickUp with zero manual effort.
🛑 The Challenge Post-meeting administrative overhead is a massive drain on team productivity. Key action items, technical requirements, and strategic next steps are frequently buried inside messy text documents or spreadsheets, resulting in forgotten tasks, delayed project timelines, and constant manual updates.
⚙️ The Solution I architected an intelligent operational middleware pipeline using Make that turns passive meeting text into active project deliverables automatically.
The workflow uses an event listener to actively monitor a central Google Sheets notes repository. The moment a new entry is detected, the workflow pushes the raw context to the OpenAI API. Using precise structural prompts, the AI acts as an executive assistant to strip out small talk, isolate the critical action items, and format the task properties. The structured data is then automatically injected directly into the ClickUp API, routing the task straight to the team's live production board.
Key Technical Infrastructure Built:
Automated Polling & State Detection: Configured optimized polling loops to watch spreadsheet rows for un-processed notes with zero data skips.
Semantic Action-Item Extraction: Prompted OpenAI to cleanly isolate complex conversations into singular, clear, action-oriented task headers and descriptions.
Dynamic ClickUp Task Injection: Programmed API payloads to populate ClickUp tickets automatically with appropriate tags, text formatting, and target list parameters.
Closed-Loop Status Reconciliation: Designed final data nodes to write processing logs, task completion links, and AI summaries back into the original Google Sheet for complete operational tracking.
📈 Business Impact & Results
Elimination of Administrative Lag: Transforms meeting summaries into actionable work orders within minutes of a meeting ending.
Flawless Task Integrity: Removes human error and forgetfulness from project handoffs, ensuring 100% accountability.
Unified Workspace Sync: Seamlessly unites loose operational spreadsheets with your core project management engine.
I built an automated lead management workflow using n8n that captures incoming leads, stores them in Google Sheets, and instantly notifies the sales team on Telegram.
Capture → Enrich → Score → Save → Notify
Built to reduce manual work, improve response time, and prevent missed leads.
Nice work sir,
Sir we can Build this normally with App script. I just Built yesterday.
Where Google sheet will received the leads from any channel + store and distribute as per described assignment rules and notify with lead details to respected person.
What if your PC could actually listen, understand, and do the work for you?
I’m building that idea into something real.
Meet AURA — my personal AI voice assistant for Windows.
Instead of building another chatbot that only answers questions, I’m experimenting with an AI agent that can actually interact with my computer and help with everyday work.
So far, AURA can:
• Have live voice conversations
• Convert speech to text and respond with voice
• Open applications like Excel, Power BI, Chrome, and Windows Settings
• Search the web and YouTube through voice
• Perform basic PC and file operations
• Understand requests through Gemini
• Execute actions through a safety-controlled automation layer
• Continue listening after completing a task
But this is only the beginning.
My long-term goal is to make AURA understand my actual workspace — including what’s happening on my screen and inside applications — so I can simply say:
“Open my Excel file, check the sales data, clean the date column, and add a formula for total revenue.”
And let AURA handle the workflow.
I also want it to become useful for my own Data Analytics work — from Excel and Power BI workflows to repetitive desktop tasks, research, and automation.
I’m building AURA with Python, AI, voice technologies, desktop automation, Excel, Power BI, and LLMs, while keeping safety and human control at the core.
It’s still far from the final vision.
But that’s exactly what makes the project interesting.
I’m not just learning how AI agents work.
I’m trying to build one that I can actually use every day.
The screen-reading step is where I would put the guardrail. Once AURA can read what is in the window, everything on that screen is input - and a page it opened from a voice search can hold a sentence written to be read as an instruction. I spent a while trying to rob my own...
Saved an hour each week with a Telegram finance bot
I finally got rid of the weekly spreadsheet grind. Using n8n’s Schedule Trigger I pull orders and delivery logs from Google Sheets, merge them, run a quick Code node to calc the margins and then ask Groq AI for a plain‑English summary. The text lands in my Telegram via an HTTP Request. The whole thing runs on Friday evenings and hands me a ready‑to‑read report – no more manual copy‑pastes. It’s a small win, but it shaves an hour off my workload each week.