hardy-ai

1. Introduction

The hardy-ai project aims to develop an AI-powered assistant tailored for educational purposes, specifically focusing on science-related content. Designed for users in India, this system will provide robust support for both students and teachers, leveraging cutting-edge AI technologies to enhance learning and teaching experiences. Het Shah, the project owner, envisions a platform that is secure, reliable, and user-friendly, with a focus on delivering accurate scientific information and tools for academic assistance.

This document outlines the system requirements for hardy-ai, including functional and non-functional specifications, user personas, design concepts, and the proposed technology stack.

2. System Overview

The hardy-ai system is an AI-powered educational assistant designed to cater to students and teachers in India. It will provide accurate scientific answers, explanations, and tools grounded in proprietary textbook content. The system will operate as a Progressive Web Application (PWA), ensuring accessibility across devices without requiring installation.

Key features include:

• Retrieval Augmented Generation (RAG) for accurate, context-aware responses.
• LaTeX/MathJax rendering for scientific formulas.
• Teacher assistance tools for generating explanations, practice questions, and summaries.
• Secure deployment on industry-standard private cloud infrastructure.

The MVP phase will include both Teacher Assistance and Formula Rendering features to maximize utility for users from the outset.

3. Functional Requirements

As a User:

• I should be able to ask science-related questions and receive accurate answers grounded in textbook content.
• I should be able to view rendered scientific formulas (e.g., physics equations, chemical formulas) seamlessly.

As a Teacher:

• I should be able to generate explanations of textbook concepts.
• I should be able to create practice questions for students.
• I should be able to generate summaries of chapters.
• I should be able to get examples for classroom teaching.
• I should be able to access analytics on student concept mastery and engagement quality.
• I should be able to generate adaptive worksheets based on student performance.

As an Admin:

• I should be able to manage user roles and access control.
• I should be able to monitor system performance and usage analytics.

As a Principal:

• I should be able to view school-vs-school and city-vs-city performance comparisons.

4. User Personas

1. Student

• Description: Primary user of the system, seeking answers to science-related questions while studying.
• Needs: Accurate, textbook-grounded answers; easy access to scientific formulas.

2. Teacher

• Description: Secondary user, utilizing the system for teaching support and content generation.
• Needs: Tools to generate explanations, practice questions, summaries, and examples; analytics on student performance.

3. Admin

• Description: Responsible for managing system operations and user access.
• Needs: Role-based access control, monitoring, and analytics tools.

4. Principal

• Description: Oversees school performance and comparisons.
• Needs: Insights into school and city-level performance metrics.

5. Visuals Colors and Theme

The visual design of hardy-ai will reflect a modern, clean, and professional aesthetic suitable for educational purposes.

Color Palette:

• Primary Color: Deep Indigo (#1A237E) – Represents trust, reliability, and academic focus.
• Primary Light: Soft Lavender (#E8EAF6) – A lighter tint for hover states and secondary UI.
• Secondary Color: Vibrant Coral (#FF6F61) – Adds energy and emphasis to the interface.
• Accent Color: Electric Lime (#CCFF00) – A vibrant pop for CTAs and active states.
• Highlight: Golden Amber (#FFC107) – For hover states and notifications.
• Background: Snow White (#FFFFFF) – Clean and neutral for the page background.
• Surface: Light Slate Gray (rgba(240, 240, 240, 0.8)) – For cards and panels.
• Text: Charcoal Black (#212121) – For primary text and headings.
• Text Muted: Slate Gray (#757575) – For secondary text and labels.
• Border: Light Silver (rgba(200, 200, 200, 0.5)) – Subtle borders for UI elements.

6. Signature Design Concept

Interactive Science Lab Homepage

The homepage of hardy-ai will resemble an interactive science lab, immersing users in a visually engaging experience.

Design Details:

• Background: A dynamic 3D environment resembling a futuristic science lab with animated elements like bubbling test tubes, rotating molecules, and glowing equations.
• Interactive Features:
  • Users can click on different lab equipment (e.g., microscopes, flasks) to navigate to specific sections of the platform.
  • Hovering over elements triggers micro-interactions, such as molecules spinning or formulas lighting up.
• Transitions: Smooth animations when switching between sections, mimicking the feel of moving through a virtual lab.
• Color Shifts: The background subtly changes hues (indigo, coral, and lime) based on the time of day, creating a dynamic and personalized experience.

This unique design will make the platform instantly memorable and engaging for users, setting it apart from traditional educational tools.

7. Interaction Model & Motion Direction

• Intended Interaction Model: Parallax

  • Layered depth via scroll with decorative layers translating at different speeds.
  • Real content stays in normal flow and scrolls naturally.
  • Best for marketing and storytelling, providing a visually rich first impression.

• Internal Pages: Static

  • Layout clarity first, minimal motion for dashboards, settings, and data-heavy pages.

Each section on the landing page will have a distinctive core mechanic, such as interactive 3D scenes or kinetic typography, ensuring a unique experience throughout.

8. Non-Functional Requirements

• Performance: The system must respond to user queries within 2 seconds.
• Scalability: The architecture should support up to 100,000 concurrent users.
• Security: All data must be encrypted during storage and transmission.
• Accessibility: The PWA must comply with WCAG 2.1 standards for accessibility.
• Reliability: The system must maintain 99.9% uptime.

9. Tech Stack

Frontend:

• ReactJS for web-based PWA.

Backend:

• Python with FastAPI for efficient API development.

Database:

• RDBMS: MySQL with Alembic for migrations.
• VectorDB: WeaviateDB for semantic search and retrieval.

AI Models:

• GPT 5.4 for user-friendly responses.
• Claude 4.6 Opas for academic and coding support.
• Google Nano Banana for image generation.

AI Tools:

• Litellm for LLM routing.
• Langchain for AI orchestration.

Infrastructure:

• Docker for local orchestration.
• Kubernetes for server-side orchestration.
• Private cloud deployment using AWS or Azure.

10. Assumptions and Constraints

• The system will operate only on proprietary textbook content; no external knowledge sources will be used.
• The MVP phase will focus on science textbooks for Classes 6–10.
• Deployment will be limited to a private cloud infrastructure to ensure data sovereignty.
• The system will be accessible only via the PWA interface; no native mobile apps will be developed initially.

11. Glossary

• RAG: Retrieval Augmented Generation, an AI architecture that combines retrieval of relevant content with generative AI responses.
• PWA: Progressive Web Application, a web-based application that functions like a mobile app without requiring installation.
• LaTeX/MathJax: Tools for rendering scientific formulas and equations.
• VectorDB: A database optimized for storing and retrieving vector embeddings used in semantic search.
• LLM: Large Language Model, an AI model trained on vast amounts of text data to generate human-like responses.

This updated SRD incorporates the new requirements for Teacher & Student Analytics and National Ranking Logic, ensuring the platform is well-prepared for school adoptions. Let me know if there are any additional updates or refinements you'd like, Het!