I build retrieval-augmented generation (RAG) systems that ground your AI in your own data — docs, knowledge bases, or product content — so answers are accurate and traceable instead of hallucinated.
What's included:
Document ingestion and chunking pipeline setup for your knowledge base
Vector database setup and embedding pipeline (e.g. Pinecone, pgvector, or similar)
Retrieval logic tuned for relevance and accuracy
Integration into your existing app, chatbot, or agent
Evaluation and tuning to reduce hallucinations and improve answer quality
I build retrieval-augmented generation (RAG) systems that ground your AI in your own data — docs, knowledge bases, or product content — so answers are accurate and traceable instead of hallucinated.
What's included:
Document ingestion and chunking pipeline setup for your knowledge base
Vector database setup and embedding pipeline (e.g. Pinecone, pgvector, or similar)
Retrieval logic tuned for relevance and accuracy
Integration into your existing app, chatbot, or agent
Evaluation and tuning to reduce hallucinations and improve answer quality