Hard prerequisites
None. No prior coding, no mathematics background required beyond basic school-level arithmetic.
FLAGSHIP PROGRAM
AI-Native Full Stack Development
100% Placement Support
Build real applications. Integrate AI from day one. Graduate as a developer, not a fresher.
3 months training · 1 month Build Month · 4 months total
Online
Fully live
AI-Native Full Stack Development, built around real product work
Most development courses hand you a syllabus and work through it in sequence. Python for a month. HTML for two weeks. React next. By the time you reach APIs you have forgotten why you are writing them, because you never saw the whole picture.
This program inverts that. On day one, before a single lecture, you and your batch use AI agents and modern coding tools to assemble a working full-stack application together. A real product, with a frontend, a backend, a database, and an AI feature, deployed to a live URL. It is deliberately rough, and much of it will not make sense yet. That is the point.
Every module for the next three months returns to that product and rebuilds one layer of it properly, by hand, with full understanding. You always know why you are learning what you are learning. "Why am I studying HTTP methods?" Because that is the API call the agent scaffolded on day one, and now you are writing it yourself, understanding exactly why every line exists.
By end of month three you have rebuilt the entire machine, understood every component, and taken it to production. Then comes Build Month, your month as a working developer.
Designed for beginners and career-focused builders
This program is designed for:
- Final-year students from any engineering stream (CS, IT, ECE, Mechanical, Civil) who want to enter the software industry
- B.Sc. Computer Science and B.Sc. IT graduates
- Any graduate from any stream willing to put in the work; we have seen students from B.Com and BA backgrounds complete this program and get placed
- Working professionals looking to make a full transition into software development
No prior coding experience is required. The program starts from absolute zero and builds systematically. What matters is commitment, because this is an intensive program that demands consistent effort.
No hard prerequisites, clear setup expectations
Soft prerequisites
Basic computer familiarity is expected: comfortable navigating files, installing software, and using a browser. Typing speed helps but is not a gate.
Setup before day one
We send you a setup checklist after enrollment. Everything is free, including VS Code, Git, a GitHub account, Python, and a free-tier account on OpenAI or the AI tool we are currently using for the batch. We walk through setup together in the first session for anyone who needs help.
Hardware minimum
8GB RAM (16GB recommended if you plan to run local AI tools), Intel i5 / AMD Ryzen 5 or above, 50GB free storage, stable internet. Windows 10/11, macOS, and Ubuntu Linux all work. If you are not sure your machine is suitable, speak to us before enrolling.
Phase by phase
Phase 1: Training (3 months)
Twelve modules, sequenced deliberately, always mapped back to the day-one product. Every module is 30% concept and 70% hands-on building. You do not watch someone code; you code alongside the trainer on real tasks connected to a real product you have been building since day one.
AI tools are present from Module 1 onwards, not as a shortcut, but as a pair-programmer you are learning to manage with judgment. You will use Copilot, Cursor, and Claude to scaffold, suggest, and explain. You will also learn to read what they generate, catch their errors, and rewrite their output when it is wrong. That judgment, knowing when to trust AI and when to override it, is what employers in 2026 are actually hiring for.
Build core programming confidence so students can read, write, and debug backend logic independently before framework-level abstractions.
Topics
- Data types, control flow, loops, functions, and modular code
- Lists, dictionaries, file handling, and exception management
- Problem-solving patterns and clean coding basics
Hands-on builds
- Student score analyzer with report export
- CLI task tracker with persistence
- Mini backend logic simulator used in later API modules
AI integration
- Using AI for test case generation, debugging hints, and refactor checks
- Prompting for explanation-first guidance instead of direct code dumps
Assessment
- Timed coding drills, bug-fix exercise, and logic viva
Establish semantic HTML and responsive CSS fundamentals so frontend structure remains maintainable when the stack scales.
Topics
- Semantic HTML, accessibility essentials, form patterns
- CSS layout with Flexbox and Grid, responsive breakpoints
- Design tokens, reusable component styling, visual hierarchy
Hands-on builds
- Responsive landing page and multi-section product page
- Interactive form UX with validation states
Assessment
- Pixel-accurate frontend reconstruction challenge
Move from static pages to dynamic product behavior by mastering browser-side programming and API interaction basics.
Topics
- Variables, functions, arrays, objects, and ES6 syntax
- DOM updates, events, async patterns, fetch workflow
- State handling and frontend data flow basics
Hands-on builds
- Task board with filters and local state persistence
- API-powered dashboard widget
AI integration
- Prompt-driven debugging for async bugs and event flow errors
Assessment
- DOM and API integration mini-app with review checklist
Teach component-first engineering so learners can build scalable interfaces and connect them to real backend services.
Topics
- Component architecture, props, state, and hooks
- Routing, reusable UI systems, forms, and API integration
- Error boundaries and frontend quality practices
Hands-on builds
- Student operations dashboard with role-based views
- Data management interface connected to live APIs
Assessment
- Component decomposition review and API wiring challenge
Build robust backend services using Python-first API architecture, with validation, error handling, and clean endpoint design.
Topics
- FastAPI project setup, routing, dependency injection basics
- Pydantic validation, status codes, and error standards
- Service layer design and endpoint testing flow
Hands-on builds
- CRUD API for a multi-entity product backend
- Validation-first endpoints with structured response schema
Assessment
- API quality rubric with test scenarios and code review
Teach when to use relational and document models and how to design data structures that support production queries.
Topics
- SQL design, relationships, indexing, and query optimization basics
- MongoDB collections, document modeling, aggregation intro
- Connection patterns between APIs and storage layers
Hands-on builds
- Schema design for a learning platform dataset
- Query-backed reporting and filter service
Assessment
- Schema defense, query challenge, and data integrity checks
Consolidate backend and frontend integration by implementing production-oriented API contracts and external service workflows.
Topics
- REST patterns, versioning, pagination, and API contracts
- Postman workflows, API documentation, and debugging traces
- Third-party API integration and reliability patterns
Hands-on builds
- Integrated service layer with external provider calls
- API documentation and test collection handover
Assessment
- End-to-end API integration sprint with review board
Train students to use AI coding and productivity tools with intent, evaluation discipline, and traceable decision-making.
Topics
- Prompt patterns for planning, debugging, review, and refactor
- Workflow design for pair-programming with AI assistants
- Output verification, hallucination checks, and guardrails
Tools covered
- ChatGPT, Gemini, Claude, Cursor, GitHub Copilot
- Prompt libraries and reusable review prompts
Responsible AI note: Students learn to treat AI output as a draft, verify claims against docs and tests, and avoid copying sensitive project data into public tools.
Assessment
- Prompt-to-production exercise with quality and safety rubric
Introduce practical LLM integration so students can add retrieval, generation, and assistant workflows inside real full stack products.
Topics
- LLM API patterns, prompt templates, token and context handling
- RAG basics, structured outputs, and evaluation workflows
- Multimodal and document-grounded assistant patterns
Tools covered
- OpenAI API, LangChain, vector database basics, Flowise
- Teachable Machine for prototype signals, Roboflow for dataset preparation awareness
Roboflow note: Roboflow is introduced as an optional workflow for dataset curation and annotation awareness when students explore vision-linked AI features.
Hands-on builds
- Domain FAQ assistant with grounded responses
- Document Q&A workflow with source-aware output
- LLM-powered feature extension for the integrated project
Assessment
- Live integration demo plus prompt and response audit
Secure the product after core build capabilities are stable, so students understand security as a system layer and not as isolated checklist work.
Topics
- Authentication flows, authorization boundaries, session/JWT basics
- Password hygiene, secret management, and secure environment setup
- Input validation and common web risk patterns
Why this sequence: AI-assisted teams move fast; this module is placed after integration depth so students can identify and fix real security gaps in context.
Assessment
- Security hardening pass and threat walkthrough
Prepare students for team workflows and production delivery with branch discipline, release flow, and cloud deployment basics.
Topics
- Git branching, pull requests, code review etiquette
- Release notes, rollback awareness, and deployment checks
- Frontend/backend deployment and environment alignment
Hands-on builds
- Team PR workflow simulation with review corrections
- Live deployment of full stack app with monitored updates
Assessment
- Deployment checklist pass and production-readiness review
Demonstrates that you can scope, build, and ship a complete application without hand-holding. This is the gateway assessment before Build Month: passing it means you are ready to work independently. Your project is jointly scoped with your trainer in the final week of Module 11.
Example projects (from past scoping conversations)
- Education: AI-powered study assistant, turning a syllabus PDF into a study plan, with RAG over notes and adaptive quizzes. React, FastAPI, OpenAI, vector store.
- Local business: hyperlocal AI customer support bot for Tamil Nadu SMEs, turning a product catalogue and FAQ into a RAG assistant for WhatsApp or web. React admin, FastAPI, LangChain, WhatsApp API.
- HR: AI interview prep platform, turning a job description into a mock interview chat and a structured feedback report. React, FastAPI, OpenAI, PDF export.
- Content: AI content operations tool, turning a topic and audience into a research brief, outline, draft, and social variants with human review checkpoints. React, FastAPI, LangChain.
- Healthcare: Symptom-to-specialist routing tool with responsible AI constraints in prompt design. FastAPI, OpenAI, MongoDB.
These are examples, not a list to choose from. Your project comes from the conversation with your trainer.
Assessment
Code review with trainer followed by a 20-minute viva on your codebase, covering why you built it the way you did, what you would change, and what breaks if you scale it. Pass this and you enter Build Month.
Build Month (1 month, mentor-supervised)
Build Month is not a continuation of coursework. It is your first month as a working developer.
You propose an original product, different from anything built during training but using the same stack, and you have four weeks to scope it, architect it, build it, deploy it, and defend every decision to a panel. You are not given a project brief. You write one. Your mentor approves it. Then you build.
Week 1: Scope and Sprint 1
Product brief written and approved. Architecture documented, covering database schema, API structure, component tree, and AI integration plan. Sprint 1 begins with the database, core API routes, and authentication. You have a working backend by end of the week.
Week 2: Build and Connect
React frontend built and connected to your backend. Core user flows working end-to-end. First biweekly mentor session (45 minutes, written agenda): your code is reviewed and specific follow-up actions are assigned in writing.
Week 3: Ship and Review
AI feature integrated and working. Application deployed to a live public URL. Mid-build panel demo with two reviewers, where you present what you have and they give honest, documented feedback. Second biweekly mentor session. You address the panel feedback before week 4.
Week 4: Harden and Defend
Security pass across the app (no exposed keys, input validation, edge cases), then documentation written so someone else could pick it up. Final panel viva: your lead trainer, an external industry mentor, and an alumni or recruiter contact. Thirty minutes, fifteen for the demo and fifteen answering questions on every decision you made.
What you leave Build Month with
- Two live deployed applications with real public URLs
- A GitHub portfolio with two polished, documented repositories
- A narrated demo video
- A panel viva on record with external industry reviewers
- A certificate of completion
- The ability to walk into any interview and say, honestly, "here is something I built, here is why I built it this way, ask me anything"
Production-oriented stack, with depth and awareness
Frontend
HTML5 · CSS3 · JavaScript · React.js · Tailwind CSS
Backend
Python · Flask · FastAPI
Databases
MySQL · MongoDB
AI and LLM integration
OpenAI API · Anthropic Claude API · LangChain · Prompt Engineering · Flowise · n8n
AI coding tools
GitHub Copilot · Cursor · Claude Code · Google Gemini · Microsoft Copilot
Security and auth
JWT · bcrypt · python-dotenv · Pydantic
Deployment and DevOps
Git · GitHub · Vercel · Netlify · Render · Railway · GitHub Actions
Design and content AI
Canva AI · Adobe Firefly
Awareness only (not assessed): Roboflow · Teachable Machine
All tools and platforms above are free or have a free tier that is enough for the full program. No paid software required.
Interview-ready roles and measurable differentiation
Junior Full Stack Developer
Frontend Developer (React)
Backend Developer (Python)
AI Application Developer
Software Development Engineer (SDE-1)
Associate Software Engineer
Junior DevOps / Cloud Associate
What makes our graduates different in interviews is two live products with real URLs, a GitHub portfolio with genuine commit history, a panel viva on record with external reviewers, and the vocabulary to defend every technical decision made, not just what was built but why, and what they would do differently at scale.
Companies hiring these profiles from Tamil Nadu: TCS, Infosys, Wipro, Cognizant, HCL at services tier. Freshworks, Zoho, Chargebee, Hexaware, Perficient, Maersk Tech, Standard Chartered GTH at product tier. Early-stage startups and product companies across the Chennai corridor and remote-first companies nationally.
What you walk in with vs. what you walk out with
| Walk in with | Walk out with |
|---|---|
| No coding experience | Working Python, JavaScript, and React, written by hand |
| No projects to show | Two live deployed applications with public URLs |
| No GitHub presence | Polished GitHub portfolio with two documented repositories |
| No interview practice | Three mock interview cycles completed, panel viva on record |
| No AI tool discipline | Confident, judgment-led use of Copilot, Cursor, Claude, LangChain |
| No security awareness | Authentication, JWT, OWASP Top 10, and AI-specific risk habits |
| Fresher | Developer |
Online, and fully live
Every session is instructor-led, over Zoom or Google Meet, at a fixed time each week. Nothing is pre-recorded: a real trainer, teaching in real time, with the batch present and able to ask questions as the work happens.
Recordings are available afterwards if you need to revisit a session, but the course is built around showing up live. That is where the debugging, the questions, and the back-and-forth with the trainer actually happen. If you are working full-time, batch timing is worked out with you directly during the admission call, so it does not clash with your job.
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