Notes & Blog

NovaX AI wasn't born from a business plan. It was born from frustration. I was building things — small tools, automation scripts, chatbot experiments — and every time I wanted to add AI to something, the process was unnecessarily complicated. The APIs were powerful, but the experience of using them was broken. Every tool felt like it was built for someone who already understood how everything worked.

I kept thinking: why can't this just be simpler? Why does using AI have to feel like navigating a technical manual? Most of the people I wanted to help — small creators, students, early-stage builders — weren't getting access to these tools in any meaningful way because the barrier was too high.

So I built NovaX AI as the platform I wanted to exist. Not a demo. Not a wrapper. A real product with a real purpose — making AI accessible, practical, and useful to people who aren't AI researchers. I built the backend, the API routing, the prompt architecture, and the frontend entirely myself. Not because I had to, but because building every layer taught me how each part actually works.

The deeper reason, honestly, was that I needed to prove something to myself. I had tried and failed at around 20-25 startups before this. NovaX AI was the one I decided to actually take seriously — to not abandon when it got hard, to actually ship, to actually build. That decision to stay has been the most important one I've made.

NovaX AI is still evolving. The vision is bigger than what it is today. But the core belief that drives it hasn't changed: AI should work for everyone, not just those who already understand it.

The idea for Ghumakkad AI came from a simple observation: planning a trip is still unnecessarily painful. You open five different apps — one for flights, one for hotels, one for itineraries, one for reviews, one for budgets — and none of them talk to each other. You spend more time planning than actually travelling.

I started thinking about what a single intelligent interface for travel would look like. Not a search engine. Not a booking aggregator. Something that actually understands what you want — your budget, your travel style, your constraints — and gives you a complete, actionable plan in one conversation.

What makes Ghumakkad AI different from existing tools is the philosophy behind it. Most travel apps optimize for transactions. I want to optimize for the traveller. There's a huge difference. A transaction-first product shows you 200 hotels and lets you filter. A traveller-first product asks you three questions and tells you exactly where to stay and why.

The technical challenge is real — maintaining context across a long travel planning conversation, integrating live data, handling mid-conversation changes in plans — but that's exactly why I find it interesting. The hard problems are where the value is.

I'm building Ghumakkad AI because India has a massive, underserved travel market and AI is genuinely the right tool to solve the fragmentation problem. It's early. The architecture is still evolving. But the core product vision is clear, and I'm shipping fast.

The hardest part isn't the AI model — it's the orchestration. Getting multiple data sources (travel APIs, hotel availability, weather) to work together in a conversation without breaking context is the real puzzle. I spent three days just on prompt templates that maintain state across 10+ turns.

Interesting observation: users don't want a travel "search engine." They want a companion that remembers what kind of trips they like, adapts to changes mid-conversation, and gives them exactly what they need without asking five clarifying questions. The UI will be invisible if the LLM layer does its job.