What I'm focused on

Most of my time is going into Majhi OS — autonomous hiring operations infrastructure for recruiting teams. The problem: 68% of VP searches stall past week 10, not because of sourcing, but because no one can see inside the system in real time. Majhi OS is the observability and recovery layer. Think Datadog for hiring systems.

We're at the early customer stage. Targeting 12 clients by December 2026. Each one goes through a 45-minute Mission Walkthrough using their actual mandate as working context — not a demo.

In parallel, I still run Majhi Group — retained VP and C-suite executive search. 30–45 day median. This is the business that generates the real mandate data that informs what I'm building in Majhi OS.

Publishing on LinkedIn 3×/week — mostly about what I'm actually seeing inside hiring systems: recruiter load, mandate failure patterns, operational blind spots that no one talks about. Not advice columns. Observations from the inside of searches.

On this site I'm continuing the Odisha and Kalahandi writing. There is a lot that has not been written about this part of India — the development trajectory, the talent story, what the landscape actually looks like — and I want to write it before the moment passes.

How AI changes the executive search business. Not in the sourcing layer — everyone is working on that. In the evaluation layer. The gap between a candidate who looks right and a candidate who will actually perform is not a data problem. It is a judgment problem. I'm skeptical that AI closes it, and interested in what that means for firms that are betting it does.

Also: the economics of building from a second-tier city in India. Odisha is not Bangalore. The cost structure is different, the talent availability is different, the network density is different. Whether those differences are advantages or constraints depends heavily on what you are building. I am running the experiment in real time.

Mostly primary sources — annual reports, government data releases, research on Indian state-level development. I find most writing about India's development story is either too optimistic or too pessimistic and I prefer to read the underlying data directly.