Stop watching tutorials. Start building real AI systems.
A 3-month, 1:1 mentorship programme for Python developers who want to build production RAG systems, LangGraph agents, and MCP integrations — guided by an expert who has shipped these systems in the real world.
Pankaj Kumar — Founder, Technovids · AI Engineering Specialist
- 24 sessions, 2×/week
- 3 Months24 sessions, 2×/week
- All on your GitHub
- 5 ProjectsAll on your GitHub
- No shared cohort
- 1:1No shared cohort
- Maximum at any time
- 3 SeatsMaximum at any time
You have watched the tutorials. Your GitHub still shows no AI work.
Most developers trying to learn RAG and agents follow the same path: YouTube → Udemy → Medium → tutorial works → try to adapt to a real use case → it breaks → back to YouTube. The loop repeats. The problem is not the content. It is the absence of someone who can see exactly where you are stuck and get you unstuck in real time.
The Tutorial Loop
You replicate the tutorial perfectly. Then you try to adapt it for your own use case and nothing works. Without someone to debug with, most people give up and start the next tutorial.
Architecture Paralysis
LangChain or LlamaIndex? Pinecone or ChromaDB? LangGraph or CrewAI? These decisions matter and affect everything downstream. Without expertise to guide them, developers default to whatever tutorial they last watched.
Empty GitHub
Months of learning, nothing to show for it. No deployable projects. No evidence of what you can build. The portfolio gap is the #1 barrier between mid-level engineers and senior AI engineering roles.
Why 1:1 beats every other learning format for this content
Production AI engineering is not a subject you can learn by following a script. It requires making real decisions, debugging real failures, and building real systems. That requires a real person watching your screen.
Curriculum shaped by your goals
Interview prep? Shipping a specific internal tool? Career pivot? The programme adapts. You are not following a fixed syllabus — you are working toward a specific destination.
Your actual code, debugged live
Group courses debug tutorial code. Here, the mentor looks at your project, your stack, your errors — and explains exactly what went wrong and why, not a generic workaround.
Architecture decisions made together
Should you use Pinecone or Weaviate for your use case? LangGraph or CrewAI? These are decisions engineers need to make independently. You practise making them here, with expert input.
Pace that matches your existing commitments
Working full-time? Busy sprint this week? The schedule flexes. Sessions are reschedulable with 24 hours notice. No cohort to keep up with.
Continuity across all 3 months
Your mentor knows your learning history, your weak spots, and your project context at every session. No repeating yourself, no starting from scratch.
Portfolio that proves what you built
Five production-grade projects on your GitHub, reviewed and refined by someone who knows what production AI systems look like. Visible proof, not just a certificate.
The 3-Month Engineering Journey
Each month ends with deployable projects on your GitHub and measurably deeper skills. Pace adapts to you — milestones are targets, not deadlines.
RAG Mastery — Foundations to Production
- ✓Build your first RAG pipeline from scratch — PDF chatbot with citations
- ✓Understand vector embeddings, chunking strategies, and similarity search deeply
- ✓Set up and compare Pinecone, ChromaDB, and FAISS for your specific use case
- ✓Implement hybrid search (BM25 + dense) and metadata filtering
- ✓Add re-ranking to improve retrieval precision measurably
- ✓Move to Advanced RAG: multi-query, query rewriting, context compression
- ✓Build a multi-document knowledge assistant with RAGAS evaluation
- ✓End of Month 1: you have 2 deployable RAG projects on GitHub
Agentic AI — From Pipelines to Autonomous Systems
- ✓Understand the ReAct framework — the foundation of every modern AI agent
- ✓Build your first LangGraph agent: nodes, edges, conditional routing, state management
- ✓Implement memory systems: conversation, entity, and external memory stores
- ✓Add reflection loops — agents that detect and fix their own errors
- ✓Multi-agent systems with CrewAI: define roles, tasks, and agent collaboration
- ✓Compare LangGraph vs CrewAI vs AutoGen — know when to use each
- ✓Build an AI Research Assistant: autonomous multi-step research and report writing
- ✓End of Month 2: you have 2 more deployable agent projects on GitHub
Tool Calling, MCP & Production Deployment
- ✓Master function calling across OpenAI, Anthropic, and Gemini APIs
- ✓Build agents with real integrations: Gmail, Slack, Calendar, and database querying
- ✓Understand MCP (Model Context Protocol) — architecture and enterprise use cases
- ✓Build a custom MCP server from scratch, exposing tools to Claude and Cursor
- ✓Connect internal databases and APIs as MCP tools
- ✓Add observability with LangSmith: trace, debug, and monitor your agent runs
- ✓Deploy your capstone to a cloud endpoint (AWS Lambda, GCP Cloud Run, or Vercel)
- ✓End of Month 3: capstone project live + full portfolio review
5 Projects. All on Your GitHub.
Every project is production-deployable. Each one becomes a portfolio piece you can demo in technical interviews or ship inside your company.
PDF Chatbot
Month 1, Week 2LangChain · ChromaDB · OpenAI Embeddings
Company Knowledge Assistant
Month 1, Week 4LlamaIndex · Pinecone · RAGAS Evaluation
AI Research Assistant
Month 2, Week 6LangGraph · ReAct · Tool Calling
Multi-Agent Workflow System
Month 2, Week 8CrewAI · AutoGen · Memory Persistence
Custom MCP Server + Deployed Capstone
Month 3, Week 12MCP · Claude Integration · Cloud Deployment
All 5 projects committed to your personal GitHub. All code is yours — no licences, no restrictions.
Who This Is For (and Who It Isn't)
- ✓Python developer with 2–8 years of experience
- ✓Has used ChatGPT or Claude — understands what LLMs can do
- ✓Wants to build AI systems, not just use AI tools
- ✓Has a specific goal: job switch, internal project, or startup idea
- ✓Can commit 8–10 hours per week including session time
- ✗Complete Python beginner (no coding background)
- ✗Looking for a quick certificate without building real projects
- ✗Wants purely business/non-technical AI training
How the Process Works
Application & Fit Call
Fill out the enquiry form. We schedule a 30-minute call to understand your background, goals, and whether this programme is the right fit. No hard sell — if it is not right for you, we will say so.
Technical Assessment
A short async task to gauge your current Python level and how you approach problems. Takes 45–60 minutes. Not a pass/fail — it helps us design your Month 1 starting point.
Personalised Roadmap
Before Session 1, you receive a customised 3-month roadmap aligned to your specific goals — whether that is a job switch, shipping an internal tool, or building a portfolio.
Session 1 — First Build
No orientation session. Session 1 starts coding immediately. You will have a working RAG pipeline by the end of the first 2 hours.
Ongoing Sessions + Async Support
Two sessions per week, with async code review, resource sharing, and questions answered between sessions via WhatsApp or Slack.
Month-End Reviews
At the end of each month, a dedicated review session assessing your GitHub projects, identifying gaps, and adjusting Month 2/3 focus accordingly.
What Past Mentees Built
“I had been watching YouTube tutorials on LangChain for six months and building nothing I could actually show. Three months with a mentor who reviewed my actual code changed that completely. I now have 4 projects on GitHub that are genuinely production-quality.”
Rahul Sharma
Senior Software Engineer → AI Engineer
Moved to a product company AI role
“The MCP module in Month 3 was the career differentiator I wasn't expecting. I was the only person in my company who knew how to build an MCP server. That visibility led directly to a promotion to the AI architecture team.”
Divya Krishnan
ML Engineer
Bangalore-based AI startup
“Best learning investment I've made. The 1:1 format meant every session was exactly what I needed — no time wasted on things I already knew, and deep dives when I was stuck. My RAG evaluation scores went from 58% to 91% in 6 weeks.”
Karthik Menon
Data Scientist → AI Systems Engineer
Chennai-based fintech
Investment & What You Get
- ✓24 live 1:1 sessions (2 hrs each) — 48 hours of dedicated instruction
- ✓Pre-programme technical assessment + personalised roadmap
- ✓Async code review and Q&A between sessions (WhatsApp/Slack)
- ✓All 5 portfolio projects — your code, your GitHub, yours to deploy
- ✓Month-end reviews with adjusted focus for the following month
- ✓LangGraph agent architecture reference guide
- ✓Custom MCP server starter kit
- ✓RAG evaluation dashboard template (RAGAS integration)
- ✓Technovids AI Engineering certificate on completion
- ✓30-day post-programme support after Month 3 ends
💡 Your company should pay for this. The projects you build are directly applicable to your employer's AI initiatives. Many mentees expense this as professional development. We can provide an invoice addressed to your company.
3-Month 1:1 Mentorship
₹75,000
All-inclusive · 24 sessions · 5 projects
(₹25,000 per month or single payment)
Availability
Maximum 3 mentees at any time — by design. If slots are full, you will be added to a waitlist and contacted when a slot opens (typically 4–6 weeks).
Full refund after Session 1 if not the right fit.
Frequently Asked Questions
How is this different from the group corporate course?+
The group course delivers a fixed curriculum to 8–16 people. This programme adapts entirely to you — your speed, your codebase, your goals. The mentor is a collaborator, not a presenter. Every session goes wherever you need it to go.
What is the weekly time commitment?+
2 sessions × 2 hrs = 4 hours of live sessions. Plus 4–6 hours of async project work. Total 8–10 hours per week. Designed for working professionals.
What Python level do I need?+
Intermediate Python — functions, classes, API calls. You do not need prior LLM framework experience. If you have built a simple REST API or used pandas, you are ready.
Can I use this to prepare for senior AI engineering roles?+
Yes. The 5 production-grade GitHub projects plus RAG + LangGraph + MCP breadth is exactly what senior AI engineering job descriptions are asking for.
How many mentees are in the programme at one time?+
Maximum 3 — by design. More than that and quality drops. Apply early.
Is the schedule flexible?+
Yes. Sessions are reschedulable with 24-hour notice. Pace adapts to your sprint cycles and personal commitments.
What is the refund policy?+
Full refund after Session 1 if not the right fit. Pro-rata refund available after Month 1.
Can sessions be in-person in Bangalore?+
Primarily online. In-person Bangalore sessions available on request.
Apply for the Mentorship Programme
Tell us about your background and what you want to build. We will reach out within 24 hours to schedule a 30-minute fit call.