Document Similarity & Search Engines
Build the engine behind "show me docs like this one" — turn text into vectors and find near-matches the way real search and recommendation systems do.
A structured Generative AI program built by an ex-Mercedes-Benz AI lead. Build real projects and ship with confidence.
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The same AI I built for self-driving cars now powers the tools you use daily. Learn to build with it — not just use it.
Not theory you'll forget in a week. By the end you'll have shipped real AI projects that sit in your portfolio and on your resume.
I'll show you how I'd break into AI today — the portfolio, the GitHub, and the resume that gets the interview. Founders hire what you've built, not what you've watched.
We go past notebooks. You'll wire models into a real app, add guardrails, and deploy it — the path from idea to something a user can open.
Before you prompt a model, know what's under the hood. We strip the hype off LLMs and look at tokens, embeddings, and attention — the parts that actually decide whether your app works.
Build the engine behind "show me docs like this one" — turn text into vectors and find near-matches the way real search and recommendation systems do.
Train and deploy a tokenizer on your own industry jargon so the model stops guessing at the words that matter in your field.
query = "refund policy?"
emb = model.encode(query)
scores = cosine(emb, docs)
Go past naive keyword matching: blend embeddings, cosine similarity, and hybrid retrieval into semantic search good enough to ship in production.
I built perception systems for self-driving cars at Mercedes-Benz and production AI at HP, Wipro, and TCS. You're learning from a builder, not a presenter reading slides.
Lessons are recorded and self-paced. Watch on the train, at night, whenever you have an hour — lifetime access means no expiry pressure.
The founding batch isn’t a comment section. You ship projects alongside people who’ll be your future references, co-founders, and hiring leads.
Every module ends in something you could drop into a work problem this week — RAG, fine-tuning, agents. No toy examples you’ll never touch again.
You’ll write the code, break it, fix it. The capstone is a real app in your GitHub — the thing that gets you the interview, not a certificate.
Beginner-friendly Python is included, and the path runs all the way to a portfolio and resume review. You leave with proof of work, not just notes.
One Time Payment
The full course, start to finish.
( FOUNDING COHORT )
One Time Payment
Everything in Basic, plus the fast track to hired.
Founding Cohort · Live & Limited • Jan 2026 batch
“Been there, done that — from building AI for self-driving cars to building Moncerra for self-driven learners”
Vishal Mehta is a founder, AI researcher, and educator with 12+ years of hands-on experience building real-world AI systems. He has worked with global innovation teams at Mercedes-Benz Research & Development, HP R&D, Wipro, TCS, Hero Electronix, and Persistent Systems — turning cutting-edge ideas into production-ready AI products.
As a former Senior Technical Lead at Mercedes-Benz R&D India, Vishal led projects in self-driving car perception and advanced deep learning. Today, he is building Moncerra, a next-generation AI learning and innovation platform.
Inside this course, I bring the frameworks, tools, and hard-won lessons from a decade of shipping AI — the stuff that separates a model that demos well from one that survives real users. I teach fast and to the point, because you're here to build, not to sit through lectures.
Join Vishal's learning community — the founding cohort — and start building AI you can actually show people.
Professional recommendations from LinkedIn.
What Working Professionals Say
Honestly, most people. If you can use a laptop, you can take this. Students, career-switchers, founders, and engineers who want to stop using AI like a black box and start building with it. You don't need a CS degree — you need curiosity and a few hours a week.
None. We start at module one explaining what a token even is. The Founding Cohort also bundles beginner-friendly Python, so even if code scares you today, you'll be writing it by week two.
It's self-paced, so it's on you. Most people in the cohort move through it in 6–10 weeks while working. You get lifetime access, so there's no clock counting down — pause it, come back, finish when it fits your life.
Recorded video lectures I actually wrote, live sessions where we build in real time, hands-on projects, and a capstone. It runs on Graphy, and you get the community plus a portfolio and resume review.
Once you enroll (checkout is handled securely by Graphy), you get your own login. It's personal and non-transferable, and it stays open for life — including future course updates.
Almost certainly. The skills — RAG, fine-tuning, agents, evaluation — are the same ones I used on self-driving perception at Mercedes-Benz and on production systems elsewhere. The projects are built so you can swap in your own domain's data and walk away with something relevant to your work.
Stay Ahead
The AI papers, tools, and build tricks I'd actually forward to a friend. One short email a week — no spam, no "revolutionize your workflow" nonsense.