Adiptify Adaptive Learning Platform

Kumar Aditya — Sun, 04 Oct 2026 20:41:15 +0000

What I Built

I built Adiptify, an adaptive AI-powered learning platform for my friend's younger sibling.

The idea came from a simple problem: they were having difficulty using existing learning apps and systems. At the same time, their school had policies around student data and did not allow school-related information to simply be circulated to external AI platforms.

Instead of building another generic chatbot, I wanted to create a learning system that could adapt to the student while keeping their learning material and data under greater control.

Adiptify uses RAG, vector search, and open-source/open-weight AI models to provide contextual explanations based on trusted learning material. The goal is to make learning simpler, more personalized, and more privacy-conscious.

Code

GitHub: https://github.com/Adiptify/Adiptify-0.1

Adiptify is built as an open-source project so the system can be explored, modified, and extended by others.

How I Built It

Adiptify is built around open-source AI and retrieval-based learning.

The core workflow is:

Learning Material → Processing → Embeddings → Vector Search → Relevant Context → LLM → Personalized Explanation

The system uses:

LangChain for LLM and RAG orchestration
LangGraph for structured AI workflows
FAISS for vector similarity search
Ollama for running open-weight models locally
RAG (Retrieval-Augmented Generation) to ground responses in the learner's actual study material
React / Next.js for the application interface
Azure for cloud/deployment components

Instead of sending every question and piece of learning material to a closed AI API, Adiptify is designed so that models can run locally and the AI can retrieve relevant information from the learner's own knowledge base.

This makes the system much more suitable for situations where privacy, data ownership, and control matter.

Why Does Open Innovation Matter?

Open innovation made Adiptify possible because I could combine different open technologies instead of depending entirely on a single closed AI platform.

For this project, that matters especially because the original problem involved student data and school restrictions around sharing that data with external AI platforms.

Using open-weight models and local inference gives us more control over:

Where the learner's data is processed
Which model is used
How the model is connected to the learning material
How the retrieval system works
How the system can be modified in the future

Tools such as Ollama, LangChain, LangGraph, and FAISS allowed me to build the AI pipeline around the learner's needs rather than designing the product around the limitations of a single closed API.

Open innovation also means the system can evolve. As better open models become available, the model layer can be changed without rebuilding the entire application.

Built for a Friend

The most important part of Adiptify is that it started with a real person.

My friend's younger sibling was struggling with existing learning applications and systems. Rather than simply telling them to “learn how to use another app,” I wanted to build something that adapted to the learner.

That became the idea behind Adiptify: