OEMDOC (Agentic AI Search System)
Developed an AI-powered search system using RAG architecture and Elasticsearch
Developed an advanced AI-powered search system leveraging Retrieval-Augmented Generation (RAG) architecture combined with Elasticsearch to deliver intelligent, context-aware search results across large-scale document datasets.
The system was designed to go beyond traditional keyword-based search by integrating agentic workflows, enabling dynamic query understanding, contextual document retrieval, and enhanced response generation. This significantly improved the relevance and accuracy of search results, especially for complex and multi-step queries.
Built and optimized backend services using Laravel to handle data ingestion, indexing pipelines, and efficient communication between the AI layer and search engine. Implemented scalable APIs to support real-time querying and high-performance data retrieval.
Additionally, improved system efficiency by structuring and preprocessing large datasets, enabling faster indexing and more precise semantic matching.
The system was designed to go beyond traditional keyword-based search by integrating agentic workflows, enabling dynamic query understanding, contextual document retrieval, and enhanced response generation. This significantly improved the relevance and accuracy of search results, especially for complex and multi-step queries.
Built and optimized backend services using Laravel to handle data ingestion, indexing pipelines, and efficient communication between the AI layer and search engine. Implemented scalable APIs to support real-time querying and high-performance data retrieval.
Additionally, improved system efficiency by structuring and preprocessing large datasets, enabling faster indexing and more precise semantic matching.