Enterprise Hybrid RAG &
Vector Knowledge Pipelines
Turn messy enterprise documents, PDFs, and databases into a verified, low-latency semantic knowledge engine with dense + sparse hybrid search and contextual re-ranking.
Eliminate Hallucinations With Mathematically Verified Citations
Architected for Production-Grade Scale
Engineered to eliminate technical debt, hallucination, and unpredictable token expenditure.
Hybrid Dense + Sparse Search
Combines semantic understanding (dense vectors) with exact keyword matching (BM25) to accurately match SKUs, names, and codes.
Contextual Cross-Encoder Re-Ranking
Second-stage re-ranking using Cohere / BGE-Reranker filters out noise and feeds only top-5 relevant chunks to the LLM.
Complex Document & Table Parsing
Extracts tables, financial spreadsheets, and multi-column scanned PDFs without losing hierarchical structure.
Multi-Tenant Document Security
Strict row-level security and ACL filters ensure users only query documents authorized by their enterprise role.
Our 4-Stage Deployment Pipeline
From initial data sanitization to hardened Kubernetes GPU container rollout.
Semantic Chunking & Parsing
Chunk documents by section headers, tables, and sentence boundaries rather than arbitrary tokens.
Vector Embedding Generation
Encode with state-of-the-art embedding models (text-embedding-3-large, BAAI/bge-large).
Hybrid Retrieval & Re-Ranking
Simultaneous BM25 + vector search with Reciprocal Rank Fusion and cross-encoder scoring.
Strict Grounded Generation
LLM synthesizes response with direct hyperlinked page references and citation quotes.
Technologies Powering This Solution
We select battle-tested, state-of-the-art libraries with active enterprise backing.
Frequently Asked Questions
Technical clarification on privacy, pricing, deployment, and data security.
Q. How does Jemixo guarantee zero hallucinations in RAG?
Q. Can we connect our live Notion, Google Drive, or SharePoint?
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Enterprise RAG Pipelines?
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