Enterprise knowledge systems trained on your proprietary documents, SOPs, and internal data - delivering instant, cited answers with full data sovereignty and on-premise deployment.
RAG turns your documents into a citation-grounded answer engine.
SOPs, handbooks, compliance manuals, and internal wikis multiply faster than anyone can read them. Critical knowledge dies in PDFs.
ChatGPT and similar tools cannot access your private documents. When they pretend to, the answers are fiction with no source trail.
Every regulated industry needs to know where an answer came from. Black-box AI cannot pass an audit.
Our enterprise RAG (Retrieval-Augmented Generation) platform ingests your entire organizational knowledge - PDFs, Word docs, Confluence wikis, SharePoint, internal databases - and builds an intelligent, searchable knowledge base that answers questions with source citations. Deployed on-premise or air-gapped for complete data sovereignty, it eliminates knowledge silos and turns your documents into a live, queryable AI assistant.
Hybrid retrieval combining semantic vector search, keyword matching, and knowledge graphs for maximum accuracy
PDF, Word, Excel, PowerPoint, HTML, Confluence, SharePoint, Notion - ingest your entire knowledge base automatically
On-premise and air-gapped deployment options ensure your proprietary data never leaves your infrastructure
AI assistant querying enterprise knowledge base for instant, cited answers from documents and SOPs.
Automated document ingestion, indexing, and intelligent retrieval across organizational knowledge.
Multi-format document ingestion: PDF, Word, Excel, PowerPoint, HTML, Confluence, SharePoint
Hybrid retrieval: semantic vector search + keyword matching + knowledge graph traversal
Source citation on every generated response with confidence scoring
On-premise and air-gapped deployment for complete data sovereignty
Multi-language support including Arabic, Japanese, and 10+ languages
Role-based access control - different knowledge access per user group
Live document sync - new documents indexed automatically within minutes
API-first design: REST + GraphQL endpoints for integration with existing systems
Legal research assistant trained on case law, contracts, and regulatory documents with precise source citations.
Clinical knowledge base trained on medical protocols, drug interactions, and treatment guidelines for instant physician support.
Sovereign knowledge system for citizen inquiry handling, policy documents, and regulatory compliance - fully air-gapped.
Company-wide AI assistant trained on SOPs, HR policies, product manuals, and internal wikis - replacing hours of search.
Common questions about deploying RAG in enterprise environments. Have something specific in mind?
Every answer is generated only from retrieved document chunks, with strict instructions to refuse if no relevant source is found. Confidence scoring flags low-relevance retrievals. We never generate answers without citations.
Yes. Full on-premise deployment, including open-source LLMs, embedding models, and vector storage. Air-gapped environments are supported for defense, healthcare, and government use cases.
PDF, Word, PowerPoint, Excel, HTML, Markdown, CSV, JSON, and most plain text formats. Image-heavy PDFs use OCR. Tables and figures are extracted with structure preserved.
Incremental indexing pipelines monitor your sources (file shares, SharePoint, Confluence, Google Drive, Notion) and re-index changed documents automatically. New documents are searchable within minutes of upload.
Every retrieved chunk includes the source document name, page number, and section reference. Click-through from any answer takes you directly to the cited paragraph in the original document.
Sub-second retrieval, 1 to 3 seconds total response time for typical queries. Optimized through caching, smart chunking, and parallel retrieval across vector and keyword indices.
Start with a 14-day pilot. See it working on your data before you commit.