Built by engineers who've shipped at the highest level.
Koovis AI was founded on a conviction: the AI industry has a delivery problem. Not a talent problem. Not a funding problem. A delivery problem. Companies invest heavily in research and prototyping, then watch those investments stall at the production boundary — the gap between “it works in a notebook” and “it drives revenue reliably at scale.”
We built Koovis AI to close that gap. Our founding team spent years building ML systems at global scale — recommendation engines, NLP systems, advertising optimization — across some of the most demanding engineering environments in the world. We know what production-grade AI looks like because we've built it under real constraints, with real stakes.
Now we bring that same standard to companies that are too early for a full data science team but too ambitious for off-the-shelf solutions. We partner with founders and technical leaders who understand that the real competitive advantage in AI isn't the model — it's the engineering that turns a model into a product.
Every engagement we take is a bet on a shared outcome. We don't bill hours and disappear. We ship systems that work, and we stand behind them.
Our Vision
We're building Koovis AI to become one of the most trusted names in applied AI engineering. Not the largest. Not the flashiest. The most trusted. The company that technical founders call when the stakes are real and the margin for error is thin.
Our aspiration is grounded: earn trust through results, grow through reputation, and build a portfolio of production systems that speaks louder than any pitch deck ever could.
Rajesh Kolachana
Founder & CEO
I'm Rajesh Kolachana. I started my career in structural engineering — IIT Roorkee for undergrad, Indian Institute of Science (IISc Bangalore) for my master's, ranked in the top 0.013% nationally in entrance exams (GATE AIR 5). Then I discovered that the same mathematical frameworks I used to model physical structures could model something far more interesting: human behavior, markets, and decisions.
That pivot led me through InMobi, where I earned a Rising Star Award after scaling an ad account from $3K to $80K daily budget in 8 months. Then to AgreeYa Solutions, where client recognition followed pricing optimization work for Best Buy, Sam's Club, and Dick's Sporting Goods. And ultimately to Amazon, where I spent 7 years as a Senior Data Scientist building the systems that mattered most.
At Amazon, I built the FBA Recommendation Engine that drove $4.4B in GMS using causal ML, engineered NLP systems generating $26M in annual revenue across 9 marketplaces, created probabilistic reorder models that lifted click-through rates by 37%, and built a keyword bidding engine that improved annual profits by $1.9M. I won a hackathon building a natural language to SQL system that became a production product used by 1,000+ account managers globally.
I left because I kept seeing the same problem from the outside: companies with strong ideas but no practical path from prototype to production. The gap isn't the model — it's everything around it. Data pipelines, deployment infrastructure, monitoring, model maintenance, and the judgment calls that come from having shipped ML systems at scale.
That's why I founded Koovis AI. To bring that same level of ML engineering to companies that need it most — with the honesty, rigor, and ownership that every engagement deserves.
How we work
Ship, Don’t Demo
Demos impress. Production creates value. We optimize for systems that run reliably at 3 AM, not systems that look great in a slide deck.
Honest Architecture
We’ll recommend the simplest solution that works — even if it means less work for us. Sometimes the answer isn’t ML. Sometimes it’s a well-written SQL query.
Own the Outcome
We’re not a staffing agency. We don’t hand off and disappear. We stay from architecture through deployment, and we’re available when things break.
Relentless Standards
Good enough isn’t. Every system we ship meets the bar we’ve held at global scale — because your users deserve production-grade quality, not MVP compromises.
Ready to work with a team that ships?
We take on a limited number of engagements to ensure every client gets the attention they deserve. Let's start with a conversation.