We engineer deterministically grounded Retrieval-Augmented Generation (RAG) architectures with hybrid BM25/vector search, contextual chunking, re-ranking, and strict document-level access control to ensure high-precision, verifiable outputs.
Mitigating Hallucination Risk Through Continuous Verification
Combining exact-keyword BM25 search with dense vector embeddings (pgvector) and reciprocal rank fusion to capture precise technical codes, numbers, and semantic intent.
Semantic-aware document splitting with hierarchy preservation, metadata attachment, and parent-document retrieval to prevent fragmented context passing.
Strict row-level security (RLS) enforcement at the retrieval stage, ensuring LLMs only receive embeddings and context the authenticated user is authorized to read.
Let our engineers design an audited ingestion and retrieval pipeline customized to your private corporate data.