Ora.Doc
AI Doc Search
Year
2026
Type of Project
Side Project
My Role
Full-Stack Developer
Case Study
Objective
Build a semantic document search system that ingests, chunks, embeds, and retrieves content using vector search and natural language queries, mirroring the core retrieval architecture behind modern AI search experiences.
Process
I designed Ora.doc as a full-stack system focused on efficient document retrieval. The pipeline begins with document ingestion and chunking, followed by embedding generation using modern NLP models.
These embeddings are stored in a vector database, enabling similarity-based retrieval for natural language queries. I implemented a backend service to handle indexing and querying, paired with a clean frontend interface for user interaction.
The system was built with modularity in mind, allowing for easy scaling and experimentation with different embedding models and retrieval strategies.
Outcome
Ora.doc highlights my ability to work with AI systems at the infrastructure level, particularly in building retrieval-based architectures.
The project deepened my understanding of vector databases, search relevance, and system design for AI applications, positioning me to contribute to products in the emerging AI search space.
Standout Features
Semantic Search via Vector Embeddings
Document Chunking & Indexing Pipeline
Natural Language Query Interface
Scalable Retrieval Architecture