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Hello everyone,
Did you know we could use voice commands for RevOps?
Turn Voice Commands Into Instant Revenue Alerts
Just pushed a working prototype update for EchoPipeline-AI, unifying our entire RevOps stack.
It connects autonomous AI agents—powered by AWS Bedrock and Google GenAI SDK (Gemini 3.6 Flash reasoning bus)—with Model Context Protocol (MCP) for ultra-low latency reasoning.
Live telemetry flows instantly through Supabase PostgreSQL and Slack, while a simulated Alexa+ handles voice dispatching.
Built from the ground up for zero-latency execution.
We've bundled live dashboard and API endpoint testing directly into the build.
Explore the live prototype and run the API tests at
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Same thing in engineering. Before I write a line of Core ML pipeline code I sketch the data flow on paper — where the tensor lives, where it gets copied, what actually runs on the ANE. The messy version catches the expensive mistakes while they're still free to fix. The keyboard is where you commit; paper is where you're still allowed to be wrong.
Is sketching part of your creative workflow or we've forgotten what it feels to be human?
Depending on the projec's scope, I normally like to explore my ideas on paper before I come up with the best solution.
And no, they're not perfect.
They are rough and messy, my goal is so...
Hum is a voice AI - real-time text-to-speech, voice cloning, and voice agents. Just designed the full identity for it.
🚀 My First AI Automation Project with n8n
I recently built my first automation project — an AI-Powered Invoice Extraction & Tracking System.
The problem I wanted to solve was simple:
Imagine a business receiving dozens of invoices or cash memos every week. Someone has to open each file, read the information, and manually enter the vendor name, date, items, total amount, and category into a spreadsheet.
That’s repetitive, time-consuming, and easy to get wrong.
So I built a workflow where:
📂 An invoice is uploaded to Google Drive
→ 🤖 Gemini AI reads and extracts the invoice data
→ ⚙️ n8n processes and validates the information
→ 📊 The data is automatically added to Google Sheets
The best part?
No manual data entry. Just upload the invoice and the rest happens automatically.
While building it, I also worked with things like workflow triggers, loop control, API errors/retries, and handling multiple files.
For me, this project was less about simply “building an n8n workflow” and more about learning how AI + automation + data workflows can solve real business problems.
This is just my first step into AI automation, and I’m excited to build more practical systems like this.
If your business has a repetitive process that could be automated with AI, n8n, or data workflows, feel free to DM me. I’d be happy to discuss it.
#AIAutomation #n8n #Automation #ArtificialIntelligence #DataAnalytics #DataAnalyst #WorkflowAutomation #GenerativeAI #BusinessAutomation #AI
Smart pipeline! Have you added strict JSON schema validation after Gemini extracts the invoice fields? Found that it stops edge case hallucinations before the data ever hits Sheets.