I will build a rag knowledge base dataset from your documents

Turn your documents into a clean, structured dataset ready for Retrieval-Augmented Generation (RAG).

GBSN Research prepares high-quality RAG datasets from your files so your AI system can retrieve information accurately. We clean, normalize, chunk, and structure your content into a format ready for vector databases and LLM pipelines.

What we do:

• Clean and normalize raw document text
• Split content into optimized chunks
• Structure data into a consistent RAG-ready format
• Add basic metadata such as source and chunk ID

Ideal for knowledge bases, support docs, manuals, policies, research libraries, and product documentation.

You receive a structured dataset ready for embedding and indexing, delivered in CSV or JSON depending on your package.

Packages assume mostly text-based documents with consistent structure. Advanced schema design, heavy cleanup, or custom JSON formats are available as Extras.

To start, send your documents, intended use, preferred chunk size, and any metadata requirements.

Message us first if your dataset is large or complex.