The patent years
I spent more than a decade in patent and IP work. Prior art analysis, claim mapping, freedom-to-operate assessments; the kind of work where precision matters and there is no shortcut that does not eventually cost someone something.
For a significant part of that time I was at CPA Global, heading the patent research division. It is one of the larger IP companies in the world; and being inside it gave me a clear view of what the tools available to patent professionals were actually doing well; and what they were quietly not doing at all.
The AI years
After that I moved into AI; building products, running startups, including Intents Mobi. That experience taught me what these systems are genuinely capable of and where they consistently fall short. LLMs are not magic. They are very good at some things and confidently wrong about others; and knowing which is which matters a lot when you are building something that is supposed to be trusted.
Somewhere in those years, the overlap between the two fields became obvious to me. Not obvious in a grand way; more like a quiet nagging that the patent world was about to get a wave of AI tools built by people who did not really understand what patent work requires. And that felt like a problem worth trying to fix.
Why it became eety
The tools being built for patent drafting were mostly text generators dressed up in legal language. They did not understand claim construction. They did not know that an Indian patent application closes differently than a US one. They did not know what an examiner actually looks for in a drawing package.
Having spent real time on both sides made some of those problems easier to see clearly. eety is the attempt to address them properly; not perfectly, but properly.