Background
I've spent the last several years working at the intersection of consumer products and data — first at Swiggy, then at Kirana Club, where I watched the same problem play out at scale, over and over: brands had customer data, but no clear way to act on it. Reviews, returns, support tickets, surveys — all sitting in silos, none of it talking to each other. Decisions got made on gut, or on whichever metric someone pulled that week.
Before that, I co-founded multiple startups and experimented with a lot of AI products before I was focused on Lexsis AI
Why I Started Lexsis
The idea for Lexsis came directly from that frustration at Swiggy. When you're trying to make product decisions at scale, fragmented signals don't just slow you down — they cost you. A return spike here, a review pattern there, a drop in repeat purchases somewhere else. Each team sees a piece, no one sees the whole picture. By the time you've connected the dots, the window to act has closed.
I started Lexsis because I believed that problem was solvable — and that solving it would unlock real, measurable revenue for brands who were losing money not from lack of data, but from lack of decision clarity.
What Lexsis Does
Lexsis unifies customer signals across every touchpoint and surfaces ranked, actionable decisions , so C brands stop reacting and start leading with outcomes