RAG Systems for Documents and Knowledge

Search and question answering over internal document collections, with answers grounded in the source material rather than generated from scratch.

Areas covered:

• Document ingestion and parsing across PDF, Word, HTML, and database exports
• Chunking and embedding strategy tuned to document structure
• Vector database setup and configuration
• Retrieval pipelines with reranking
• Source attribution so answers remain checkable
• API layer for integration into existing products
• Accuracy evaluation against real questions before launch
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Production experience includes retrieval augmented generation over internal knowledge bases and document classification pipelines running in live environments.

Prototypes are straightforward. The work here is making retrieval reliable on real and inconsistent company data.