AI Launchpad for Students

Who this is for

• College students from any degree — engineering, commerce, arts, science
• Final-year students preparing for placements who want to stand out
• Students who want a real project portfolio, not just certificates
• Anyone in college who keeps hearing 'learn AI' but doesn't know where to start

What you'll walk away with

• 2–3 portfolio-ready AI projects published on GitHub and LinkedIn
• Confidence with the modern AI stack — LLMs, prompting, and no-code + code tools
• A resume and LinkedIn story that stands out in placements and internships
• A personal learning roadmap that keeps working after the program ends

Baseline toolkit

Tools form a baseline, not a fixed syllabus — the toolkit is customised around your goals, experience, device, and budget.

• Claude Code — Build, debug, and understand portfolio projects with an AI coding partner.
• Claude Cowork — Plan research, assignments, and multi-step project work.
• Claude Design — Turn project ideas into polished visuals and interfaces.
• Codex — Learn agentic coding workflows and ship working features.
• Ollama — Run and experiment with open-source AI models locally.
• GitHub — Publish your work and build visible proof of skill.

How a typical four weeks runs

• Week 1: Foundations & goal mapping — Where you are, where you want to be, and the exact AI skills your target roles reward. We map your roadmap together.
• Week 2: Tools & prompting in your domain — Hands-on with the AI tools that matter for your field — and prompting techniques that get professional-grade output.
• Week 3: Build your first project — We build a real AI project tied to your interests — something concrete you can demo, not a tutorial clone.
• Week 4: Ship, publish, present — Polish the project, publish it on GitHub and LinkedIn, and craft the story you'll tell interviewers about it.

Included in every tier

• Weekly live 1:1 sessions (60 min) with Arthi — no recordings, no batch classes
• A personalized roadmap built around your goals in session one
• WhatsApp support between sessions for questions and unblocking
• Session notes, resources, and tool recommendations after every call
• Access to the private AI community
• Flexible scheduling — book each session when it suits you via Calendly
• Certificate of completion

Pricing

• 4-Week Sprint — ₹8,999 for 4 live 1:1 sessions over 4 weeks (≈ ₹2,250/week). 4 × 60-min 1:1 sessions. One focused goal, shipped in a month.
• 8-Week Builder — ₹15,999 for 8 live 1:1 sessions over 8 weeks (≈ ₹2,000/week). 8 × 60-min 1:1 sessions. 2–3 deeper projects with iteration time. Placement/internship interview prep session.
• 12-Week Transformation — ₹21,999 for 12 live 1:1 sessions over 12 weeks (≈ ₹1,833/week). 12 × 60-min 1:1 sessions. Full portfolio build across multiple projects. Mock interview with detailed feedback. Capstone project reviewed end-to-end. LinkedIn recommendation from Arthi on completion.

Payment is in INR via Razorpay. Full refund before your first session; pro-rata refunds after.

Frequently asked questions

I'm not from a computer science background. Can I still join?

Absolutely. Some of the most interesting AI projects come from non-CS students applying AI to their own field — commerce, design, biology, law. Your degree is an angle, not a limitation.

How is this different from an online course?

There are no pre-recorded lectures. Every session is a live 1:1 working session with Arthi, built around your goals, your pace, and your projects. You're not watching — you're building, with a mentor beside you.

How does scheduling work?

After you enroll, you get a Calendly link to book your sessions. You choose the day and time that works for you each week — mornings, evenings, or weekends. If a week gets busy, just reschedule.

Which timeline should I pick — 4, 8, or 12 weeks?

4 weeks is a focused sprint on one clear goal. 8 weeks gives room for deeper projects and iteration. 12 weeks is the full transformation — multiple projects, a capstone, and time to build real momentum. Not sure? Start with 4 weeks; you can always extend.

Do I need any technical background?

No. Every track starts from wherever you are. If you can use a browser, you can do this — the roadmap is personalized to your starting point.

What tools do I need? Are they paid?

A laptop and internet connection. Claude Code, Claude Cowork, Claude Design, Codex, Ollama, and other open-source or no-code tools form a baseline — not a fixed syllabus. We customize the toolkit around your goals, experience, device, and budget, and work with free tiers wherever practical. No paid subscription is required unless you choose it.

Is there a refund policy?

Full refund if you cancel before your first session. After that, unused weeks are refunded on a pro-rata basis — no questions asked.

Can I join from outside India?

Yes — all sessions are online and scheduling is flexible across time zones. Payment is in INR via Razorpay.

What mentees say

The class was very exciting and interactive. Arthi mam did a great job at delivering things clearly. I was able to learn many new things through this session.

I am a healthcare professional with no knowledge about AI. I was always scared to explore AI because it is always portrayed as a huge thing. A one-on-one session with Arthi was eye-opening for me — useful, and it gave hope that even a person with zero knowledge about AI can start.

Anybody — I already do. You broke down the fundamentals so well that I left feeling like an expert.

Before this workshop I'd never touched an API. Four hours later I had a voice agent replying to me in the browser. I gave it a 10 out of 10.

I walked in not knowing what a function tool was. I walked out having registered one myself and watched Jarvis call it live.

She made something genuinely complex feel completely approachable without dumbing it down. Confident I can build on this now.

I came in at a 1 on confidence to build AI agents. I left at a 5. The hands-on format meant I was never just watching.

The pacing was exactly right. The agent.py deep dive was the best part. I finally understand how LLMs connect to real-world tools.