On-Premise RAG & Vector Search
Semantic search across all your company documents. Ask questions in natural language, get answers from your contracts, policies, and reports. Everything runs on your server.
What's included
Document Indexing
We process and index all your documents: contracts, policies, emails, reports, presentations. PDFs, Word, Excel, scanned documents with OCR. Arabic and English support.
Vector Database
Install Qdrant, Chroma, or Milvus on your server. Documents are converted to vector embeddings using models like BGE-large and multilingual-e5. Search by meaning, not just keywords.
Embedding Models
Deploy local embedding models (BGE, GTE, multilingual-e5) for converting text to vectors. Optimized for your language mix and document types.
Search Interface
Browser-based search interface for your team. Natural language queries return ranked results with source citations. Optional LLM integration for conversational answers.
Auto-Indexing Pipeline
Set up automatic indexing of new documents as they arrive. Watch folders, SharePoint connectors, email ingestion. Your search index stays current automatically.
Access Control
Department-level access restrictions. Users only see search results from documents they are authorized to access. Full audit trail of who searched what.
How vector search works
Traditional search matches keywords. Vector search understands meaning.
Document Embedding
Every document is split into chunks and converted into a high-dimensional vector (array of numbers). Similar meanings produce similar vectors, regardless of the exact words used.
Semantic Query
Your question is also converted to a vector. The system finds the document chunks with the most similar vectors using cosine similarity and HNSW indexing for speed.
Context Retrieval
The most relevant document chunks are retrieved and presented with source citations. This works across languages: an English query finds Arabic documents and vice versa.
LLM Answer (Optional)
Optionally, retrieved chunks are passed to a local LLM which generates a natural language answer with citations. This is RAG: Retrieval-Augmented Generation.
Supported Document Sources
We index any document format and data source your organization uses.
Who needs on-premise RAG
Law Firms
Search 50,000+ contracts, agreements, and legal filings by meaning. Find all clauses about liability caps, non-compete terms, or force majeure across your entire archive in seconds instead of hours.
Banks & Financial Institutions
Compliance teams search internal policies, CBUAE circulars, and regulatory filings. Find relevant precedents and requirements across thousands of documents. Audit-ready search history.
Compliance & Risk
Ask "What does our policy say about PEPs?" and get answers from 15 different documents: AML policy, training materials, CBUAE guidance, committee reports. All sources cited.
Government Departments
Search across decades of government circulars, ministerial orders, and internal policies. Find relevant precedents instantly. Enable knowledge continuity across staff changes.
Research & Consulting
Search research papers, case studies, and project reports by concept. Find similar projects, related findings, and relevant methodologies from your organization's knowledge base.
Enterprise Knowledge Base
Centralized search across HR policies, IT documentation, operational SOPs, and training materials. New employees find answers instantly instead of asking colleagues.
Technical Details
Vector search with HNSW indexing, access control, and auto-indexing — deployed on your servers.
# RAG System Architecture Vector Database: Engine: Qdrant / Chroma / Milvus Indexing: HNSW (approximate nearest neighbor) Dimensions: 768-1024 (model dependent) Embedding Models: English: BGE-large, GTE-large Arabic: multilingual-e5-large Mixed: BGE-M3 (multilingual, 1024d) Document Pipeline: Input: PDF, DOCX, XLSX, emails, scans OCR: Tesseract / PaddleOCR (local) Chunking: Semantic chunking with overlap Metadata: Date, author, department, type Search Performance: Index size: 100,000+ documents Query time: < 1 second Relevance: cosine similarity + reranking Languages: Cross-lingual (EN/AR query → AR/EN results)
Every project is different
Pricing depends on your document volume, number of data sources, and integration complexity. We'll assess your requirements and propose a solution that fits.
Ready to make your documents searchable?
Tell us about your document volumes and use cases. Free initial consultation.