AI-first, not AI-optional.
Applied AI — agents, RAG pipelines, LLM APIs, production deployment — is the core thread. Everything else supports it.
AI-First Engineering Academy
Build AI products.
Ship real work.
Get hired.
A 3-month intensive program that turns motivated engineers into industry-ready, AI-first developers. You graduate with production projects, real communication skills, and the confidence to contribute on day one — at a Bangalore startup or a Singapore MNC.
4+production-grade projects
3months, full focus
0slide decks, ever
AIin every project, from week one
Production standards from day one.
Git workflows, code reviews, CI/CD, and architectural decision-making. The same practices working engineers use daily.
Industry-ready, not just technically ready.
You'll communicate clearly, handle ambiguity, present your work, and collaborate under pressure. Because showing up is only half the battle.
Real critique, not fake grades.
Instructors who've shipped production systems review your code, your architecture decisions, and how you present your thinking.
About GrepX Hyperscale Academy
The gap is real.
We built the bridge.
There is a well-known distance between finishing a degree and being genuinely useful at work on day one. Engineers who can write code but struggle with a production bug under pressure. Developers who know frameworks but freeze when a requirement is ambiguous. Talented people whose technical ability never fully translates — because the bridge between learning and doing was never built for them.
GrepX closes that gap. We built a program that treats technical depth and professional readiness as two sides of the same coin — not separate tracks, not optional modules.
⚙️
Applied AI at the core
Modern full-stack engineering with AI agents, LLM APIs, and production-grade AI pipelines woven in from week one — not saved for a final module.
🚀
Production, not practice runs
Every project ships. Real architecture decisions, real code reviews, real deployment pipelines. You build the habits that matter before you need them at work.
🎯
Holistic professional readiness
Clear communication. Business context. Confident presence. Collaboration under pressure. The professional edge that makes your technical skills immediately usable.
Graduates walk into Indian startups and global product teams as day-one contributors, not trainees. Our instructors have designed and shipped the kind of production systems your future employer uses. They review your work the way a senior engineer reviews a pull request — direct, specific, and grounded in what actually matters on the job.
Flagship Programs
Structured for the way engineers actually get hired in 2026.
Two tracks. One foundational. One AI-native. Both production-serious.
Flagship · Primary Track
AI-First Full-Stack Engineering
The complete program. Modern full-stack development with Applied AI as the central thread — not an elective bolted on at the end. You build production-grade AI-powered applications, learn to work with LLM APIs, deploy agents, and ship real products that do real things. Technical depth is non-negotiable. Professional readiness is built in.
What You Build
- 4+ portfolio-ready full-stack + AI projects
- AI agent integrated into a production web app
- RAG pipeline with real document ingestion
- Live capstone deployed on AWS with CI/CD
- Architecture write-ups for each project
Technical Core
- React, Next.js, TypeScript, Node.js, Python
- PostgreSQL, Redis, MongoDB, vector databases
- OpenAI API, LangChain, LangGraph agents
- Docker, GitHub Actions, AWS deployment
- WebSockets, real-time systems, caching
Professional Readiness
- How to explain architecture to non-engineers
- Sprint workflows, standups, async collaboration
- Writing documentation engineers actually read
- Handling ambiguous requirements without freezing
- Interview preparation: technical + behavioural
Supporting Track
Full-Stack Foundations
The bedrock. For learners who need solid fundamentals before moving into the AI-first track. Covers the complete web development lifecycle — frontend, backend, databases, deployment — at production standards. Feeds directly into the flagship program.
Core Outcomes
- 2 complete full-stack projects shipped
- Clean, reviewed, documented codebase
- GitHub commit history that tells the story
- Working knowledge of REST, auth, databases
Tech Covered
- HTML/CSS, JavaScript, React, Node.js
- Express, PostgreSQL, Git, REST APIs
- JWT auth, relational data modelling
- Staging deployment, environment config
Who This Is For
- Students with some coding exposure but no shipped project
- Career switchers building a credible foundation
- Anyone who wants to enter the AI track job-ready
Curriculum — AI-First Full-Stack Track
Three months. A complete engineering journey.
Each phase delivers a working project and a concrete set of skills. AI integration is present from month one — not introduced at the end.
Foundations + AI Integration · First Project
Weeks 1–4What You Learn
You build a working full-stack application from a blank file. REST API, database, auth, UI — all connected, version-controlled, and deployed. AI integration starts immediately: you wire up an LLM API within the first two weeks and learn to ship AI-powered features alongside core application logic.
Technologies
Skills Gained
- Component-based UI design
- REST API design + implementation
- User authentication (JWT)
- Relational database queries
- LLM API integration in a live product
- Git branching and PR workflow
- Writing clear commit messages and READMEs
Deliverable → A full-stack web application with an embedded AI feature, documented code, clean README, and a 5-minute walkthrough presentation to the cohort.
Advanced Architecture + AI Agents · Second Project
Weeks 5–8What You Learn
You take on real complexity. TypeScript tightens your code. Multi-user data models, permissions, and dashboards displaying real data. The AI layer gets serious: you build a RAG pipeline, work with vector databases, and start designing LangChain-based agents that take actions inside your application.
Technologies
Skills Gained
- TypeScript type safety in production code
- Multi-user data ownership and permissions
- Server-side rendering with Next.js
- RAG pipeline design and implementation
- Vector similarity search with pgvector
- Presenting architecture decisions to peers
- Handling scope changes mid-project
Deliverable → A production-architecture Next.js application with an AI agent or RAG feature, fully documented API, and a written architecture decision record.
Production Systems + AI Agents at Scale · Capstone
Weeks 9–12What You Learn
Real-time systems, cloud deployment, DevOps. You set up a CI/CD pipeline, containerise your app, and deploy to AWS. The capstone is your most ambitious project: a complete AI-first product — agent or AI-powered SaaS — with end-to-end infrastructure. You also do mock interviews, portfolio reviews, and client-presentation simulations.
Technologies
Skills Gained
- Multi-step AI agent design with LangGraph
- Real-time bidirectional communication
- Cloud deployment + environment management
- Automated CI/CD pipeline setup
- Caching and performance fundamentals
- Technical interview and presentation prep
- Writing engineering blogs + portfolio docs
Deliverable → Capstone: a fully deployed AI-first application with CI/CD, cloud infrastructure, written architecture overview, and a live demo to a panel — including senior engineers and an industry guest.
End of program — 4+ projects — AI-first full-stack skills — CI/CD + cloud experience — Documented codebase — Interview-ready
Technology Stack
The tools 2026 engineering teams actually use.
Every tool was chosen because it's genuinely useful right now and because it teaches a pattern that transfers when tools change. You learn the reasoning, not just the syntax.
⚡ Applied AI — Flagship
OpenAI API + LangChainLLM integration, prompt engineering, streaming, function calling
LangGraphMulti-step agent design, state machines, tool-calling workflows
RAG PipelinesDocument ingestion, chunking, embedding, retrieval, re-ranking
Vector Databasespgvector, Pinecone — embeddings, similarity search, hybrid retrieval
AI-Assisted Dev WorkflowsCopilot, cursor-style dev, AI in the build and test pipeline
Model DeploymentServing predictions via FastAPI REST endpoints, latency tuning
Frontend
React 18 + Next.js 14Hooks, App Router, SSR, SSG, streaming, API routes
TypeScriptType safety, interfaces, generics, strict mode in real codebases
Tailwind CSSUtility-first styling, design systems, responsive layouts
JavaScript ES2022+Async/await, modules, modern DOM APIs
WebSocketsReal-time bidirectional communication, event handling
Backend
Node.js + ExpressRouting, middleware, async I/O, error handling
Python + FastAPIType-annotated APIs, dependency injection, async — the AI backend layer
REST APIsResource design, status codes, OpenAPI/Swagger documentation
AuthenticationJWT, session tokens, OAuth2 concepts, secure cookies
Message QueuesEvent-driven patterns, async job processing
Database
PostgreSQLRelational design, joins, indexing, transactions, pgvector extension
MongoDBDocument model, aggregations, schema design for AI workloads
RedisCaching, pub/sub, session storage, rate limiting, AI response caching
Prisma + SQLAlchemyORM layer, Alembic migrations, query building
Cloud — AWS
EC2 + App RunnerBackend services, API servers, web app hosting
S3Object storage, static hosting, file handling for AI pipelines
RDSManaged PostgreSQL — production-grade database in the cloud
IAM + SecretsRoles, policies, environment variable management
CloudWatchLogs, metrics, alerting — because production systems need observability
DevOps + Tooling
Git + GitHubBranching strategies, PRs, commit conventions, code review
DockerContainerising apps, Dockerfile, Compose — reproducible environments
GitHub ActionsAutomated build, test, and deploy pipelines — CI/CD from scratch
VS Code + TerminalExtensions, debugging, shell scripting — working like a professional
Postman + curlAPI testing, environment configs, debugging production issues
Real-World Case Studies
What production development actually looks like.
Instructors here have shipped these systems. The architecture decisions, trade-offs, and hard lessons from this work shape every project in the program.
Case Study 01
Fleetwise
Fleet Management Ecosystem
An integrated fleet management platform giving logistics businesses complete, real-time visibility over vehicles, drivers, jobs, financials, and notifications. Designed to scale from a 10-truck operator to an enterprise fleet.
Next.js admin with TypeScript and Zustand. Python FastAPI backend. Ionic Angular cross-platform mobile for drivers on iOS and Android. PostgreSQL core with SQLAlchemy. Firebase for real-time updates.
Tech Stack
- —Frontend: Next.js, TypeScript, Tailwind, Zustand
- —Backend: Python, FastAPI, SQLAlchemy, Alembic
- —Mobile: Ionic, Angular, Capacitor
- —DB: PostgreSQL, SQLite (dev)
- —Services: Firebase, CI/CD via Bitbucket
What This Teaches
- —Multi-tier system design from requirements to prod
- —Why Python and TypeScript serve different layers
- —Cross-platform mobile alongside web admin
- —Third-party API integration + real-time sync
- —Full DevOps: migrations, backups, environments
Case Study 02
Nexus
Pluggable Microservices Framework
An abstraction-first microservices framework built on a single principle: every component is pluggable through well-defined interfaces. Swap databases, brokers, or deployment strategies with a config change, not a rewrite.
Clean layered architecture: client, load balancer, API gateway, protocol adapters, service mesh, business logic services, and data persistence. Kubernetes deployment with blue-green, canary, and automated rollback.
Architecture Highlights
- —Gateway: REST, GraphQL, gRPC, WebSocket adapters
- —Databases: PostgreSQL, MongoDB, Redis (pluggable)
- —Messaging: Kafka, RabbitMQ, AWS SQS (swappable)
- —Observability: ELK, Prometheus/Grafana
- —Deploy: Kubernetes, ArgoCD, blue-green/canary
What This Teaches
- —Interface segregation and dependency inversion
- —Why hot-swappable components matter in practice
- —Designing services that scale independently
- —CI/CD with automated testing at every stage
- —Production deployment strategies + rollbacks
These are real systems, built by the people teaching you. Students learn from engineers who have shipped production code — not slides about it. Program projects run at smaller scale but follow the same architectural thinking and decision-making rigour.
Outcomes & Placements
Concrete things you leave with.
Not vague promises.
4+ Production-Grade Projects
Complete applications you designed and built from scratch. AI-integrated, documented, version-controlled, and ready to show to any hiring manager.
Real Applied AI Skills
You can build with LLM APIs, design and deploy RAG pipelines, create multi-step agents with LangGraph, and integrate AI features into full-stack applications.
Cloud & DevOps Experience
You've set up CI/CD pipelines, managed secrets, containerised applications, and deployed to AWS. Not theory — working systems you personally shipped.
A GitHub Profile That Proves It
Months of real commit history across multiple projects. Anyone reviewing your work can read the actual code and follow every decision you made.
Professional Readiness
You can explain technical decisions to non-engineers, present your work confidently, handle ambiguous requirements, and collaborate in a real team environment without hand-holding.
Documentation & Written Communication
Architecture decision records, clean READMEs, API documentation, and a personal engineering portfolio that demonstrates how you think — not just what you built.
A Path Into GrepX
Top performers who complete the program are considered for an internship at GrepX. Selection is based on overall course performance and an internal interview — merit-based, no guarantees, but a genuine opportunity for those who earn it.
What we do promise
You will leave with real, working projects and real skills in your hands. Your GitHub history will tell the story clearly. You'll know how to communicate your work and handle yourself in a professional environment. Every graduate who has put in the work has been genuinely competitive in the job market.
What we don't promise
We don't guarantee placement numbers, specific salary figures, or that any particular company will hire you. What you get out of this depends heavily on what you put in. The portfolio and the GitHub history you build here are the credential. The work speaks for itself — and so do you, if you've done the work.
Why GrepX Hyperscale Academy
What makes this different from every other training program.
01
AI is the headline, not the footnote. Most programs add an "AI module" at the end. Here, Applied AI is woven into every project from week one. You graduate as an AI-first developer, not a web developer who has heard of ChatGPT.
02
No tutorials to copy-paste. Ever. Every project starts from a blank file. You design the architecture, make the decisions, debug the errors. You understand why something works because you've felt the problem it solves.
03
Instructors who've shipped real systems. You're reviewed by people who have designed and deployed production platforms. They critique your architecture decisions the way a senior engineer reviews a pull request — direct and specific.
04
Holistic readiness, not just tech skills. Communication, professional presence, client-facing confidence, and collaboration under pressure. Not as an add-on but as a natural part of every project — because that's how real work works.
05
Production standards from day one. The same Git workflows, code review practices, CI/CD discipline, and deployment processes that working engineering teams use daily. You're trained in the habits, not just the syntax.
06
A deliberately small cohort. You're not a registration number here. Small, intentional groups mean every student gets real attention, real feedback, and real accountability. The kind of environment where growth actually happens.
Who this is for
- ✓You've done tutorials but never shipped a real project
- ✓You want to work with AI tools, not just use them
- ✓You're serious about building a credible engineering career
- ✓You can handle direct feedback and a fast pace
- ✓You want to work at startups or global tech companies
- ✓You're switching careers and need a real portfolio fast
Who it's not for
- ✗People looking for a certificate to pad a resume
- ✗Anyone expecting lectures with pre-built starter code
- ✗Anyone unwilling to present, write, and communicate their work
- ✗People who need to be told what to do at every step
How to Apply
Ready to actually build something?
Four steps. No complicated forms. No entrance exam. Just a real conversation about whether this is the right fit for you.
Step 01
Send a short email to support@grepx.co.in introducing yourself and what you want to build. No long forms.
Step 02
We reply within 2 business days to schedule a 20-minute intro call. Real conversation, not a sales pitch.
Step 03
If it's a genuine fit, you get admission confirmation with fee details and batch dates.
Step 04
Set up your machine following our pre-batch checklist. Show up on day one ready to write real code.