Custom RAG Pipeline Development: Secure Enterprise LLM Access to Your Private Knowledge Base
MyAibo engineers production RAG pipelines giving enterprise LLM apps accurate, secure access to private knowledge — document ingestion, chunking, vector database, retrieval, and citation-grounded generation.
Why Is Your RAG System Retrieving the Right Documents but Still Generating Incorrect Answers?
RAG failure is almost always a retrieval quality problem — chunk boundaries, embedding fit, and re-ranking do more for accuracy than a bigger model.
The work, in detail.
Four phases, week by week.
Inventory the corpus, define query patterns, and select the vector database.
Build ingestion, chunking, embedding, and indexing; benchmark retrieval.
Implement hybrid retrieval, re-ranking, and citation-grounded generation.
Deploy with monitoring and run monthly retrieval-quality evaluation.
Works best together.
Almost always a retrieval quality problem beneath the surface — chunk boundaries, embedding fit, or missing re-ranking — which is why those decisions get the most attention in our process, not model selection.