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.
MyAibo builds end-to-end RAG systems: ingestion, chunking strategy, embedding selection, vector database implementation (Pinecone, Weaviate, Qdrant, pgvector), hybrid dense+sparse retrieval, and permission-enforced retrieval — fixing the common failure of good recall but poor generation accuracy.
RAG failure is almost always a retrieval quality problem — chunk boundaries, embedding fit, and re-ranking do more for accuracy than a bigger model.
We build layout-aware ingestion for PDF/DOCX/HTML/Markdown, OCR for scans, and chunking (fixed, semantic, or hierarchical) matched to document type, with benchmarked embedding model selection.
Getting these decisions right early avoids expensive re-embedding of the entire corpus later.
We select the database by deployment need (Pinecone, Qdrant, pgvector, Weaviate) and implement hybrid dense+BM25 retrieval with RRF merging and re-ranking.
Hybrid retrieval typically lifts accuracy 15–25% over pure vector search — decisive for legal, compliance, or support use cases.
We enforce metadata-based permission filtering at the retrieval layer, VPC deployment, audit logging, and PII detection/redaction in ingestion.
Avoids the access-control failures that most enterprises discover through an incident rather than an audit.
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.