Engineering a Zero-Hallucination Enterprise Knowledge RAG Engine

Standard RAG architectures rely on a brittle pipeline: slice text into arbitrary 500-token chunks, compute dense embeddings, and run cosine similarity search. In enterprise legal and financial contexts, this approach breaks down quickly.

Naive retrieval systems exhibit three structural defects:

• Table & Hierarchy Destruction: Naive character slicing splits financial tables across multiple chunks, destroying headers and corrupting numerical context.
• Lexical Blindness: Dense vector embeddings capture semantic themes but miss exact identifiers like clause numbers, patent codes, and statutory references.
• Hallucination Propagation: Standard prompting permits models to fill informational gaps with plausible falsehoods when retrieval confidence is marginal.

Our mandate: engineer a deterministic retrieval pipeline with verifiable character-level grounding, while running a direct outbound campaign to sign 10 mid-market corporate pilots.

We bypassed standard PDF text-strippers. The ingestion pipeline converts incoming documents into structured Abstract Syntax Trees (AST), preserving table layouts, section hierarchy, and breadcrumbs.

Query execution operates across a multi-stage validation pipeline:

1. Hybrid Candidate Extraction: Queries execute concurrently across full-text search (BM25 via PostgreSQL tsvector) and semantic vector search (via pgvector HNSW). Reciprocal Rank Fusion (RRF) synthesizes the top 40 candidates.
2. Cross-Encoder Reranking: A Cohere cross-encoder reranks candidates, filtering out false positives and reducing the context payload to the 5 most mathematically relevant passages.
3. Adversarial Fact-Check Loop: Before returning text to the client, an internal verification sub-agent cross-examines every generated sentence against the retrieved spans. Sentences lacking source attribution are purged.