Tech to Transform

A Note of Thanks First off, a heartfelt thank you to everyone who has been following my blog over the past few weeks. I'm thrilled to share that within just 7 weeks, we've crossed 1,000 readers , and the thoughtful comments I’ve received have been incredibly motivating. Your support truly fuels this journey. This blog has unexpectedly become a learning accelerator for me. Before writing each post, I dive deep— reading not just to solve, but to truly learn . In many ways, it feels like being back in college again, where learning was the goal, not just the means. A special mention to my daughter Veena , who creates the beautiful line art illustrations you've seen across these posts. She’s in 12th grade and an art enthusiast with her own Instagram channel. While AI isn’t her subject, our weekly 30-minute collaboration sessions have become a precious bonding ritual for both of us. It’s a reminder that when diverse minds collaborate — one rooted in technology, the other in ...

Building a 100% Free On-Prem RAG System with Open Source LLMs, Embeddings, Pinecone, and n8n After the last post on building a financial statement analyzer using OpenAI and n8n, many readers reached out with a common question: “Can I build a similar RAG system without relying on OpenAI APIs or paid cloud services?” The answer is — yes, absolutely. In this tutorial, I’ll walk you through building a complete Retrieval-Augmented Generation (RAG) system entirely on-prem , using free and open-source tools . No API keys, no vendor lock-in, and no code required. With the help of: n8n for orchestrating your workflow Pinecone as a vector database (free-tier available) Ollama for running open-source LLMs and embedding models locally Windows Command Prompt for setup and automation You’ll create a fully functional RAG pipeline that: Accepts documents Converts them to embeddings Stores and retrieves relevant context Answers user queries intelligently — all fro...

Building a Financial Statement Analyzer in 15 Minutes Using AI Agents, n8n & GPT-4o From Concepts to Code: Our First Hands-On GenAI Project As I mentioned at the start of this blog series, this space isn't just for high-level ideas and industry perspectives — it's also a place to roll up our sleeves and build. While the last few posts explored theoretical foundations, ethical considerations, and strategic frameworks around GenAI, it’s time to mix things up with something a bit more hands-on. Starting this week, I’ll be sharing a few mini-projects — simple yet meaningful applications of Generative AI. These are designed to help you: Get comfortable with GenAI APIs and tools See real-world implementation patterns Learn by doing — not just reading Build your confidence to take on larger, domain-specific use cases Here’s the first project to kick things off  Financial data analysis is often manual, time-consuming, and error-prone. So I challenged myself: ...