I started building things seriously around 2019. I was studying computer science and spending most of my time around machine learning, deep learning, research papers, hackathons, and small products I thought were worth trying.
AI was a very different space then. Most of my exposure came through models, datasets, multilingual systems, and figuring out why something that worked in a paper would completely fall apart when you tried to build it.
Cruxe
Cruxe was my first proper attempt at building a company, and the idea changed more times than I can remember.
One version was something like Inshorts for interesting content. Another was a summarisation API for media companies. I somehow got six editors from The Times of India into a meeting while I was still in college, and they explained quite clearly why they would never buy it.
So we changed it again.
Cruxe eventually became a content recommendation product, something I used to describe as “Spotify for content.” We built a team around it and, at one point, reached around 100,000 users within a few months.
Running it taught me as much about finding users as building software. I had to write emails, explain the product, make content, talk to users, change the positioning, and understand why people came back or disappeared.
Eventually, Cruxe stopped. There were multiple reasons, including my health at the time, and I reached a point where I had to sit down and start preparing for software engineering interviews.
Paytm and the internet
I joined Paytm as a backend engineer and worked around merchant onboarding. It gave me a very different understanding of engineering because the systems were operating at a scale where small decisions could affect a lot of people.
At the same time, I kept posting online. I had started creating content during Cruxe because I needed people to discover what we were building, and I simply continued doing it.
Most of the early content did nothing. Over time, AI became the subject I kept coming back to because it was the area I understood best. Then generative AI started moving very quickly, and suddenly a subject I had been following for years had everyone's attention.
The audience grew to hundreds of thousands of people across platforms. More importantly, publishing constantly taught me how people react to technology. You can explain the same product in five different ways and immediately see which explanation people understand and which one they ignore.
Moving deeper into AI
After Paytm, I moved deeper into AI companies.
At Fluid AI, I spent more time around enterprise AI and customers, including representing the company in rooms like Mastercard in Dubai. I realised I enjoyed understanding the product technically and then sitting with someone who needed to understand why it mattered.
Later, I worked with Unify AI and Athina AI, both YC W23 companies. My work moved across DevRel, community, onboarding, LLM evaluation, technical content, and distribution.
Because these were small AI teams, there was never a clean line between product and GTM. I was constantly moving between understanding what was being built and figuring out how developers or customers should discover it.
Around the same period, I was also teaching a lot. I have now taken more than 100 live sessions and workshops around AI, spoken at companies and institutions such as Mastercard, HSBC and Inc42, and given two TEDx talks.
Teaching made one thing very clear to me: you can understand a technology very deeply and still be terrible at explaining it to the person sitting in front of you.
Varnan
Varnan came out of everything that happened before it.
I kept meeting technical founders who had built genuinely good products but struggled to explain them, distribute them, or reach the right users. I understood the problem because I had been on both sides of it.
At Varnan, we worked with more than 15 AI and developer-product companies. The team grew to seven people, and we spent most of our time experimenting with positioning, technical content, launches, Reddit, communities, founder-led distribution, and whatever else could help a good product reach the right people.
Some things worked extremely well. Others did absolutely nothing. That experience has made me much more skeptical of marketing playbooks and much more interested in understanding the product, the market, and the people before deciding what distribution should look like.
Where I am now
Looking back, the path has moved through engineering, startups, content, DevRel, teaching, GTM, and building companies. It looks slightly random when written as job titles, but it has mostly been the same curiosity showing up in different forms.
I like understanding new technology, building around it, and figuring out how it changes the way people and companies work. That is still where I spend most of my time today.