🚀 Ascent – AI driven SaaS for engineering institutes to provision Student Ecosystem

Ascent is a next-generation, AI-powered SaaS platform designed to revolutionize student engagement, institutional efficiency, and career readiness in modern engineering institutions.
It transforms fragmented digital systems into a unified, intelligent ecosystem that evolves with each student through their academic journey.

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🧠 Overview

Modern engineering institutions face challenges due to disconnected systems for communication, career services, and academic management — resulting in:

• Low student engagement
• Heavy administrative workload
• Poor visibility into student progress
• A widening gap between academia and industry

Ascent directly addresses these challenges by providing a single, AI-driven B2B ecosystem designed around the four key phases of a student’s academic journey (FE, SE, TE, BE).
Each phase progressively unlocks new tools, insights, and experiences, ensuring students receive what they need exactly when they’re ready for it.

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🌐 Vision

To empower institutions with an adaptive student lifecycle platform that dynamically evolves with user behavior and data — fostering engagement, skill growth, and career success.

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🧩 Core Architecture

Ascent is structured around four progressive phases, each unlocking more features and intelligence:

🩵 Phase 1: Foundation (FE)

Introduction and onboarding phase to build self-awareness and interest discovery.

• Home Dashboard – Personalized student hub.
• Behavior Tracker v1 – Simple trivia-based interest extraction to identify domain inclinations.
• Timeline (DAG) – Interactive graph of achievements and milestones (Phase 1 node unlocked).
• BlogPost v1 – Tech newsfeed with AI-curated articles suited to beginner-level understanding.
  • Engagement metrics (likes/dislikes, dwell time) feed into AI for domain inference.

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💙 Phase 2: Exploration (SE)

Encourages student involvement, collaboration, and competitive growth.

• Club Exploration – AI-suggested clubs based on domain interests; managed by club admins.
• Behavior Tracker v2 – Smarter trivia with refined question complexity.
• BlogPost v2 – More advanced topics matching domain maturity.
• Bounty Board – Micro-challenges posted by clubs (poster design, bug bounties, etc.).
• Monthly Macrothons – Lightweight hackathons for consistent practice.
• Leet Tracker – LeetCode API integration for tracking problem-solving progress.
• Timeline Update – Adds achievement branches dynamically.

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💜 Phase 3: Growth (TE)

Transforms awareness into skill-building and professional readiness.

• Career Roadmaps – AI-curated, domain-specific learning and career paths updated yearly.
• Timeline Comparison – Compare personal growth with top alumni.
• Git Tracker – Tracks contributions via GitHub API.
• Macrothon v2 – Advanced-level problem statements.
• Behavior Tracker v3 – Deep data retrieval with domain-focused engagement.
• BlogPost v3 – Advanced content curated per user domain.

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💚 Phase 4: Launch (BE)

Career readiness, interviews, and specialization culmination.

• Interview Pods – AI + peer-based interview simulation by job role/domain.
• Final Timeline Completion – All four nodes unlocked with full journey visualization.
• Leaderboard – Domain-wise student ranking.
• Behavior Tracker Final – Consolidates lifetime data to suggest the most fitting career domain.
• Skill Tree – Dynamic visualization of all acquired skills via Skill Log integration.

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🧾 Default Features (Available Across Phases)

• Skill Log – Students input learned skills, automatically categorized and added to their skill tree.
• Achievement Hall – Repository for achievements, synced via LinkedIn API.
• Data-Driven Insights – AI models evolve based on behavioral, academic, and technical data.

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🎓 Institutional Dashboard (For HODs & Admins)

Gain a holistic, real-time view of student progress and institutional performance.

Key Features:

• Phase-wise student list & filtering.
• Individual student dashboards displaying:
  • Domain interests
  • Placement/higher study inclinations
  • Skill tree visualization
  • Behavior and engagement analytics
  • AI-powered SWOT analysis
• Data-driven insights for curriculum improvement and mentorship.

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⚙️ Tech Stack

Layer	Technology
Frontend	Next.js (React), TailwindCSS, Framer Motion
Backend	Node.js / Express
Database	MongoDB
AI & ML	Python (Flask/FastAPI), TensorFlow, Scikit-learn, LangChain
Integrations	GitHub API, LeetCode API, LinkedIn API
Architecture	Microservice-based SaaS Model
Deployment	Docker + Vercel / AWS

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🧠 AI-Driven Intelligence

• Behavioral Modeling: Adaptive trivia and blog recommendation systems refine student interests.
• Domain Prediction: ML models analyze cumulative data (blog interactions, hackathon performance, skill logs) to identify strongest domain fit.
• Career Personalization: AI dynamically adjusts roadmaps and resource suggestions.
• HOD Insights: Aggregated AI reports highlight department-level trends and bottlenecks.

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🗂️ Folder Structure (Proposed)

Ascent/
│
├── client/                    # Next.js Frontend
│   ├── components/
│   ├── pages/
│   ├── styles/
│   └── utils/
│
├── server/                    # Node.js Backend
│   ├── routes/
│   ├── models/
│   ├── controllers/
│   └── middleware/
│
├── ai_service/                # Python ML Backend
│   ├── behavior_model/
│   ├── domain_predictor/
│   ├── roadmap_generator/
│   └── api.py
│
└── docs/                      # Documentation and reports

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🧩 System Flow

1. Student signs up → assigned to Phase 1 (FE).
2. Platform collects behavioral + skill data → feeds to ML model.
3. As the student advances academically, Ascent auto-upgrades their phase.
4. Each new phase unlocks additional features + complexity tailored to student maturity.
5. HODs and admins get live analytics through the institutional dashboard.

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⚙️ Installation & Setup

🖥️ Prerequisites

Ensure the following are installed:

• Node.js (v18+)
• Python (v3.10+)
• MongoDB
• Git
• npm or yarn
• Docker (optional for containerized deployment)

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🧩 Clone the Repository

git clone https://github.com/your-username/Ascent.git
cd Ascent

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🪄 Environment Configuration

Create .env files in both client/, server/, and ai_service/ directories.

server/.env

PORT=5000
MONGO_URI=your_mongodb_connection_string
JWT_SECRET=your_jwt_secret
CLIENT_URL=http://localhost:3000
GITHUB_API_KEY=your_github_api_key
LEETCODE_API_KEY=your_leetcode_api_key
LINKEDIN_CLIENT_ID=your_linkedin_client_id
LINKEDIN_CLIENT_SECRET=your_linkedin_client_secret

client/.env.local

NEXT_PUBLIC_API_URL=http://localhost:5000
NEXT_PUBLIC_AI_SERVICE_URL=http://localhost:8000

ai_service/.env

MODEL_PATH=./models/domain_model.pkl
FLASK_ENV=development
PORT=8000

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💻 Install Dependencies

Frontend (Next.js)

cd client
npm install

Backend (Node.js)

cd ../server
npm install

AI Service (Python)

cd ../ai_service
pip install -r requirements.txt

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🏃 Run the Application

Start Backend Server

cd server
npm run dev

Start AI Service

cd ../ai_service
python api.py

Start Frontend

cd ../client
npm run dev

Now open the app at http://localhost:3000 🎉

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🐳 Run Using Docker (Optional)

If you prefer containerization:

docker compose up --build -d

This starts the full stack defined in docker-compose.yml:

• frontend (Next.js) on http://localhost:3000
• backend (Express + Prisma) on http://localhost:5000
• postgres on localhost:5432
• kafka on localhost:9092
• zookeeper on localhost:2181

To stop everything:

docker compose down

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🧑‍💻 Developed By

Team Ascent

• Omkar Chandgaonkar (Founder & Architect)
• [Add Contributors]

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🏁 Future Scope

• Integration with LMS systems (e.g., Moodle, Google Classroom).
• Predictive placement analytics for institutions.
• AI mentors for personalized skill coaching.
• Resume builder & automatic portfolio generator.