Systems ship. Demos don't.
8 weeks. 48 hours live. You walk out with production-grade builds, an Enterprise Agentic RAG mid-program project, a capstone, and the architecture instincts that separate senior AI engineers from notebook coders. Join early — start with full recordings, get ahead before the live cohort begins.
✓ Live sessions Saturdays & Sundays · 8:30 PM – 11:30 PM IST · Cohort 4 · Starting April 12
The gap between building a demo and owning production isn't skill — it's architecture thinking.
Jupyter notebooks that break at scale. Tutorials that stop at "hello world". No understanding of what happens when real users hit your agent at 3 AM.
Latency spikes, runaway token costs, hallucinations in production — without the right observability and guardrails, you can't debug what you can't see.
Everyone claims "AI experience." Hiring managers want to see architecture decisions, tradeoffs, deployed systems. A capstone they can actually evaluate.
We go from foundations to production deployment in a logical, compounding progression.
From LLM fundamentals and prompt engineering to building your first agents and memory-backed systems. Every concept is taught through a working build.
LangGraph multi-agent orchestration, async task queues, FastAPI deployment, Docker containerization, cloud hosting, and observability that catches failures before users do.
Every week you ship something real. Click any week to see what you'll build and why.
Understand how LLMs actually work — tokenization, context windows, temperature, and why the same prompt gets different results. You'll learn prompt engineering patterns that professional engineers use, not tutorials.
Build your first real agent. Move beyond wrappers and understand how LangChain chains, agents, and tools actually compose. You'll understand why most agent tutorials break and how to fix them.
Memory is where most agents break in production. You'll learn when to use in-context memory vs vector retrieval vs external storage, and how to build systems that remember what matters without ballooning costs.
RAG is table stakes. Agentic RAG — where retrieval triggers actions, multi-hop reasoning, and re-ranking — is what production systems actually need. You'll build both, understand the tradeoffs, and know when RAG becomes a bottleneck.
LangGraph is where serious agent orchestration happens. You'll understand state machines, node and edge composition, conditional branching, and how to build multi-agent networks that coordinate without getting into infinite loops.
Real AI systems don't run synchronously. You'll learn how to queue long-running agent tasks, manage state across workers, handle failures gracefully, and give users real-time status on background jobs.
Wrap your agent in a real API. Containerize it. Deploy it. You'll build a FastAPI service with authentication, rate limiting, and proper error handling, then containerize with Docker and deploy to AWS/GCP/Azure.
The final differentiator. You'll instrument your system with Langfuse and LangSmith, write evals with pytest, set up alerting, and ship your capstone project — a complete production AI system that demonstrates real engineering judgment.
Every tool you'll use is production-relevant — not just what looks good on a tutorial.
This isn't a course. It's a system: curriculum, community, code, and career collateral.
8 weeks of live sessions with Nachiketh. Real-time Q&A, architecture reviews, debugging together.
A real enterprise-grade Agentic RAG system built mid-cohort — a production portfolio piece that demonstrates you've shipped, not just learned.
A 4-week end-to-end production system with observability, deployment, and evals. The thing you show in interviews.
System design templates for multi-agent pipelines, RAG architectures, and async processing patterns.
Miss a session? Rejoin the next live cohort for the same week at no cost. Your learning never stalls — every concept gets a chance to click live.
A cohort of serious engineers working on the same problems. Debug together, share patterns, stay accountable.
Every build, every pattern, every utility. Fork it, extend it, deploy it. The codebase is yours.
All 48 hours recorded. Revisit architecture decisions, re-watch debugging sessions, move at your own pace after live.
Proof of completion from a program with a verifiable GitHub history. Something hiring managers can actually verify.
"Before this, I could build chatbots. After this, I can architect multi-agent systems that handle real load. The LangGraph week alone changed how I think about state."
"Nachiketh doesn't teach you to copy-paste from docs. He teaches you to reason about systems. That's rare and that's what you need in an interview loop."
"Week 6 on async systems with Celery was a revelation. I'd been hacking around this for months. Three hours of live session fixed my production architecture."
"The capstone project is genuinely something I put on my resume and GitHub. My interviewer asked me to walk through it for 30 minutes. I got the offer."
"I've done other AI courses. The difference here is that everything deploys. Week 7's Docker + cloud deployment session finally connected all the pieces for me."
"The community alone is worth it. I debug with four other engineers from the cohort regularly. That kind of network doesn't come from watching YouTube videos."
Nachiketh has built and shipped production AI systems, taught 100,000+ engineers through Manifold AI Learning, and developed a reputation for teaching architecture thinking — not just syntax. His approach: every concept goes through a build, every build has production context.
One-time investment. Lifetime access to recordings, code, and the cohort community.
✓ Secure checkout · EMI available at checkout
If you're serious about transitioning into Agentic AI, enroll now. Get full recorded access immediately, work through the curriculum at your own pace, and enter Cohort 4 live sessions already ahead — while others are just getting started.
3 hours of live instruction on Saturdays and Sundays (6 hours total per weekend). Expect 4–6 hours additional for the weekly build and any debugging. Engineers who put in 10–12 hours per week get the most out of the cohort.
No ML background required. You need solid Python, some experience with APIs, and intellectual curiosity. Backend engineers, full-stack developers, and data engineers join this program and thrive. We start from LLM fundamentals in Week 1.
All sessions are recorded and available within 24 hours. You can catch up asynchronously. That said, live sessions are where the real learning happens — the Q&A, the debugging, the "why did this fail?" moments. We recommend attending live whenever possible.
Yes — and this is the best way to get the most out of the program. Enroll now, get full access to all recordings immediately, and work through the curriculum before Cohort 4 live sessions start. You'll enter the live sessions already ahead, able to ask deeper questions and get more out of every session.
It's designed to give you the capability and portfolio that opens doors. Engineers who complete the capstone come out with a real deployed system they can walk through in interviews. We don't promise job placement — but we give you something far more valuable: demonstrated engineering judgment that interviewers can evaluate.
Yes. EMI options are available at checkout through our payment processor. You'll see installment options when you proceed to payment.