RAG & Private AI Systems

Design and implementation of retrieval-augmented generation systems for confidential enterprise knowledge. I build governed RAG platforms with authorization before retrieval, adaptive document ingestion, evaluation and private or self-hosted language models.

Turn scattered internal documents into searchable, permission-aware knowledge.

Select parsing, chunking and retrieval strategies for each document type and scale.

Keep restricted data inside controlled infrastructure with access-first architecture.

Define knowledge boundaries

Identify sources, owners, user groups, permissions and acceptable model behavior.

Engineer retrieval

Build ingestion, metadata, hybrid search, reranking and evaluation around real questions.

Operate reliably

Deploy with observability, feedback loops, versioned prompts and measurable answer quality.

What is enterprise RAG?

Enterprise RAG retrieves relevant information from governed company sources before a language model answers. A production system also needs permissions, ingestion, evaluation, monitoring and clear source attribution.

Can departments keep separate confidential knowledge?

Yes. Retrieval can enforce user and department permissions before any context reaches the model, while one shared interface serves multiple governed assistants.

Can RAG use local language models?

Yes. Local models, embeddings and vector stores can keep confidential prompts, documents and answers on self-managed infrastructure.