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Zero-Hallucination Search

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.

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Enterprise RAG Pipelines
99.4%
Accuracy SLA
< 35ms
Median Latency
100%
Zero Data Leak

Eliminate Hallucinations With Mathematically Verified Citations

Simple vector search breaks in production when encountering technical part numbers, legal clause references, and complex multi-page tables. Jemixo builds enterprise-grade Retrieval-Augmented Generation (RAG) pipelines that combine dense vector embeddings with BM25 sparse keyword matching, followed by cross-encoder re-ranking. Every answer delivered by the LLM contains verifiable line-by-line page citations, exact source URLs, and cryptographic confidence scores, completely preventing confabulation.

Architected for Production-Grade Scale

Engineered to eliminate technical debt, hallucination, and unpredictable token expenditure.

Reciprocal Rank Fusion
🔍

Hybrid Dense + Sparse Search

Combines semantic understanding (dense vectors) with exact keyword matching (BM25) to accurately match SKUs, names, and codes.

Cohere / BGE
🎯

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.

LlamaParse / Unstructured
📑

Complex Document & Table Parsing

Extracts tables, financial spreadsheets, and multi-column scanned PDFs without losing hierarchical structure.

Role-Based Access (RBAC)
🏢

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.

01

Semantic Chunking & Parsing

Chunk documents by section headers, tables, and sentence boundaries rather than arbitrary tokens.

02

Vector Embedding Generation

Encode with state-of-the-art embedding models (text-embedding-3-large, BAAI/bge-large).

03

Hybrid Retrieval & Re-Ranking

Simultaneous BM25 + vector search with Reciprocal Rank Fusion and cross-encoder scoring.

04

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.

✔ PostgreSQL pgvector
✔ Qdrant
✔ Pinecone
✔ Cohere Re-ranker
✔ LlamaIndex
✔ LangChain
✔ FastAPI
✔ Redis

Frequently Asked Questions

Technical clarification on privacy, pricing, deployment, and data security.

Q. How does Jemixo guarantee zero hallucinations in RAG?

We enforce prompt instructions that restrict the model strictly to provided context chunks. If the similarity score falls below a verified threshold, the system returns a graceful "information not found in knowledge base" rather than inventing facts.

Q. Can we connect our live Notion, Google Drive, or SharePoint?

Yes. We build automated delta connectors that sync changes incrementally using webhooks, keeping your vector database up to date in near real-time.
Let's Create Together

Ready to Build Your
Enterprise RAG Pipelines?

Speak directly with our senior software architects and AI engineers to map out your sprint scope, preliminary architecture, and budget.

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📞 Direct Call: +91 70141 83576
💬 WhatsApp Support: +91 70141 83576
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