I design and build production-ready RAG and knowledge-based AI systems that can retrieve relevant information from documents, databases and other knowledge sources and provide contextual responses using LLMs.

I can help with:

• Retrieval-Augmented Generation (RAG) architecture • Document ingestion and processing pipelines • Document parsing, chunking and metadata extraction • Embeddings and semantic search • Vector databases and vector search • Knowledge bases and enterprise search • Contextual retrieval and relevance optimization • RAG pipelines integrated with LLMs • PDF, document and structured data ingestion • Metadata filtering and hybrid retrieval • Conversational knowledge assistants • API and third-party data integration • RAG evaluation and retrieval quality improvements • Secure and scalable AI knowledge systems

I can work with existing documents, databases, APIs or application data and turn them into an intelligent knowledge system that users can search and interact with using natural language.

My focus is on accurate retrieval, relevant context, scalable architecture, security and maintainable production-ready implementations.

Whether you need an internal knowledge assistant, document intelligence system, enterprise search solution or RAG capability added to an existing application, I can help design and implement the solution end-to-end.