Nine years in martech and conversational AI SaaS. Then a break — and a circle back to the AI world to see what had changed. What he found surprised him. The move from customer success to harness engineering didn't feel like a leap. It just clicked.
It wasn't burnout. After nine years close to customers — Swiggy, Ola, Reliance, Amazon miniTV — he stepped back, then circled back to the AI world just to understand what was new. What he saw was surprising: how far AI had evolved, how much had become possible. That pull is what brought him back — not as a CSM, but as a builder.
"Not as a chatbot. As an operating layer — a system that could think alongside you, hold context, synthesise, build."
— the moment Claude reframed everythingThe AI CSM Mastermind — four intelligence modules built on top of nine years of domain knowledge. The principle is simple: AI does the surfacing and preparing; the human stays in the decision seat.
"Operate like a team of one with the output quality of a team of five."
He interviewed at Anthropic — it didn't go through. But the Claude learning mindset, the way the best minds in AI think about agents and systems, stayed with him. That's what ignited it: a real passion for agentic AI and harness engineering. He learned from the top minds in the field, and started building the system.
"I didn't read the papers. I reasoned my way to the same architecture — then found the papers later."
Five times, Raj built something from scratch — a dreaming layer, a harness, a memory framework, a compound system, a context-engineering philosophy — and later discovered the research community had already published the same concept. He arrived there independently. Every time.
Nine months. A day job. No engineering team. No research budget. Just a first-principles instinct and a system that compounds.
Architecture audit: 10.5 / 11 on first ever run. WorkOS and Ramp built their harnesses with engineering teams. Raj built his as a Senior CSM. That's the anomaly.
Not trend-following. First-principles thinking. He builds the concept — the industry names it later.
Each layer built because the previous one had a gap. No grand plan. No roadmap. Just a builder's instinct — and nine months of compounding.
"Most people use AI to save time. I use it to build compounding systems — every week operating at a higher level than the week before."
View the full architectureThe long-term vision is products. But first — join an organisation serious about making AI work at scale, and sharpen harness engineering at enterprise level. Chief Commander AI is the proof of work. This site is the portfolio.
A role where I implement AI harness engineering at enterprise level — helping teams build reliable, repeatable AI workflows that actually ship
Organisations serious about the gap between AI capability and AI reliability in production
Teams where CS domain expertise + AI infrastructure thinking = genuine competitive advantage