AI-Powered Digital Library
RAG Search Across 16,000+ Books
Rebuilt a legacy digital library into a modern AI-powered platform, enabling multilingual semantic search and conversational discovery across a collection of 16,000+ books.
Project Overview
A Large Library Isn't Useful If People Can't Find What They're Looking For.
The existing product was built on a legacy .NET platform and needed to evolve into a more modern digital experience. With more than 16,000 books across five languages, traditional discovery mechanisms were not enough for users looking for information by meaning, context, or natural language.
Legacy Platform
A mature .NET-based platform needed modernization while preserving the core digital-library experience.
Discovery at Scale
Users needed a better way to discover relevant information across 16,000+ books.
AI-Powered Search
The platform needed semantic understanding and conversational interaction rather than relying solely on conventional keyword-based discovery.
From Digital Archive to Intelligent Discovery Platform
- LEGACY PLATFORM
- MODERN APPLICATION
- SEMANTIC SEARCH
- RAG
- AI-POWERED DISCOVERY
Modernize
The legacy .NET platform was rebuilt around a modern Next.js and NestJS application architecture.
Understand
Library content was processed into semantic representations that could be searched by meaning.
Answer
Retrieval and AI were combined to provide conversational discovery grounded in the library content.
Designed for Search. Built for AI.
The platform combines a modern application stack with vector search and retrieval-augmented generation to turn a large digital library into an intelligent discovery experience.
The AI Doesn't Guess. It Retrieves.
Instead of relying solely on a language model's general knowledge, the system retrieves relevant information from the library and uses that context to generate responses grounded in the underlying content.
- 01
Books
Library content
- 02
Content Processing
Prepare content for retrieval
- 03
Chunking
Break content into searchable sections
- 04
Embeddings
Convert content into semantic representations
- 05
pgvector
Store and search vector representations
- 06
Semantic Retrieval
Find relevant content for the user's query
- 07
Context
Assemble relevant information
- 08
LLM
Generate a response from retrieved context
- 09
Grounded Response
Return an AI response based on library content
Search by Meaning, Not Just Keywords.
“machine learning”
System primarily looks for matching terms.
“How can computers learn from examples?”
The system can identify conceptually relevant content even when the exact words differ.
One Library. Five Languages.
The AI-powered discovery experience supports English, Urdu, Arabic, Farsi, and Gujarati, allowing users to interact with a multilingual collection through semantic search and conversational discovery.
Technology Choices With a Reason.
PostgreSQL + pgvector
Vector search was integrated alongside the application's relational data layer using PostgreSQL and pgvector.
NestJS
NestJS provided a structured backend foundation for the modernized application.
OpenAI Embeddings
Embeddings allow library content and user queries to be represented semantically for similarity-based retrieval.
RAG
Retrieval-Augmented Generation allows AI responses to be grounded in relevant library content instead of relying exclusively on general model knowledge.
The Hard Part Wasn't Adding AI.
The challenge was integrating AI into an existing digital product while modernizing the underlying platform and making a large multilingual collection easier to explore.
Product Integration
AI capabilities needed to become part of the actual library experience rather than a disconnected AI demonstration.
Retrieval Quality
The system needed to retrieve relevant library content before generating useful responses.
Platform Modernization
The AI capabilities needed to fit into a broader modernization of the existing application.
A Modern AI-Powered Discovery Experience
From Architecture to Implementation.
SME & Team Lead
Architecture
Led technical architecture and modernization decisions.
AI Engineering
Worked on the AI-powered search and RAG capabilities.
Backend Engineering
Worked with NestJS and PostgreSQL as part of the modernized application.
Technical Leadership
Provided technical direction and helped guide implementation across the team.
Technology
Let's Talk Architecture.
Have an AI product idea that needs more than a prototype? Let's figure out what it should take to build it properly.