Nexariza AIBook a call

Agentic AI

Zero-Hallucination Enterprise RAG: Combining Dense Vector Search with BM25 Sparse Retrieval & Cross-Encoders

A technical blueprint for eliminating hallucinations in compliance-heavy domains: hybrid chunking, recursive retrieval, and reranking across millions of documents.

Start your project

In consumer applications, an occasional hallucination is a minor nuisance. In enterprise legal, financial, and medical workflows, an ungrounded model response is an unacceptable liability.

Simple vector-search RAG (Retrieval-Augmented Generation) fails in the real world because semantic embeddings frequently overlook exact keyword identifiers (e.g., part numbers, legal clause citations, or tax codes). Groundbreaking research published on arXiv:2005.11401 established foundational retrieval principles, but production demands hybrid execution.

Here is the Nexariza Hybrid Dense-Sparse Architecture powered by Qdrant Vector Database and PostgreSQL pgvector. Explore our Enterprise RAG & Knowledge Systems Service.

The Three-Stage Retrieval Pipeline

User Query
    │
    ├──> Dense Search (Cosine Distance / text-embedding-3-large)
    │
    └──> Sparse Search (BM25 Token Inverted Index in PostgreSQL)
    │
    ▼
Reciprocal Rank Fusion (RRF) ──> Top 50 Chunks
    │
    ▼
Cross-Encoder Reranker (Cohere / BGE-Reranker-Large) ──> Top 5 High-Precision Chunks
    │
    ▼
LLM Generation with Strict Citation Grounding

1. Dense Semantic Retrieval + BM25 Sparse Search

Dense embeddings capture conceptual meaning ("What are the termination clauses?"), while BM25 captures exact lexical matches ("Clause 14.2(b)"). Running both in parallel and combining scores using Reciprocal Rank Fusion (RRF) ensures neither nuance is lost.

2. Cross-Encoder Reranking

Bi-encoder embeddings compress entire passages into a single vector, losing token-to-token cross-attention. Our pipeline passes the top 50 candidates through a dedicated cross-encoder reranker, computing true query-document cross-attention and elevating only the top 5 most relevant passages.

3. Strict Citation & Source Verification

If the retrieved context fails a minimum relevance threshold, the system returns a verified "Information Not Found in Context" rather than inventing speculative answers. See how we deployed deterministic retrieval in our Medical Knowledge Graph Clinical Decision Support Case Study.

Nexariza deploys this blueprint in our Enterprise RAG Template. Speak with our engineers to integrate with your enterprise repositories or test feasibility with our AI Consultant Tool.