Axent.AI

Inspiration

AxentAI was inspired by a common problem faced by engineering students: studying hard but still feeling unprepared.
Most planning tools are static and treat all subjects the same, ignoring difficulty, prerequisites, and personal energy levels.
This often leads to cramming, burnout, and discovering weak areas too late.
I wanted to build a system that helps students decide what actually matters right now, like a good academic mentor would.

What it does

AxentAI is an AI-powered study assistant that creates adaptive study plans and visual roadmaps for engineering students.
It generates personalized weekly schedules based on subjects, exam dates, available time, and learning preferences.
The platform also includes an AI tutor that explains complex topics, highlights weak areas, and helps students stay consistent and exam-ready.

How we built it

AxentAI was built using React, TypeScript, and Tailwind CSS for a clean and modern frontend.
Firebase handles authentication and data storage.
The AI system uses a hybrid architecture:

• Google Gemini powers the conversational tutor and deep technical explanations.
• Tambo AI handles structured intelligence like curriculum roadmaps, adaptive planning logic, and topic summaries. This separation keeps planning reliable while conversations remain natural.

Challenges we ran into

One of the biggest challenges was designing adaptive schedules that felt helpful instead of overwhelming.
Balancing flexibility with structure, preventing unrealistic AI plans, and keeping the UI simple while showing meaningful insights required several iterations.
Coordinating outputs between two AI systems without conflicts was also technically challenging.

Accomplishments that we're proud of

We’re proud of building a fully working, deployed application that goes beyond basic scheduling.
The adaptive planning logic, visual roadmap, and AI tutor come together to create a product that feels practical and usable.
Seeing the system generate realistic study plans in real time was a major milestone.

What we learned

This project taught us how important structure and constraints are when building AI-driven products.
We learned how to design systems where AI guides decisions instead of replacing them, and how good UX plays a crucial role in building trust with AI features.
It also strengthened our skills in full-stack development and AI integration.

What's next for AxentAI

Next, we plan to add deeper progress tracking, smarter confidence-based rebalancing, and more refined analytics.
We also want to expand support for more branches, add collaborative study features, and improve personalization based on long-term learning patterns.

Built With

• adaptive-planning-logic
• api
• css
• firebase
• framer-motion
• fullcalendar
• gemini
• google
• html
• javascript
• json
• jspdf
• lucide-react
• react
• recharts
• tailwind-css
• tambo
• typescript
• vercel
• vite