XPndAI builds enterprise-grade RAG systems — private knowledge retrieval, agentic RAG, hybrid search, and multi-source indexing for large organizations with complex information architectures.
Index across Confluence, SharePoint, Google Drive, Notion, Jira, databases, APIs, and custom sources into a unified, searchable knowledge graph.
Combine dense vector search with BM25 keyword search and re-ranking models — optimal retrieval precision across diverse enterprise knowledge bases.
Multi-step retrieval agents that decompose complex queries, retrieve from multiple sources, synthesize across results, and reason to produce comprehensive answers.
Document-level permissions ensure users only see content they are authorized to access — critical for enterprise confidentiality and compliance.
Retrieval quality metrics, query patterns, zero-result tracking, and chunk-level analytics to continuously improve your RAG system performance.
Fully on-premise or VPC-isolated deployment — your enterprise knowledge never leaves your controlled infrastructure environment.
A top-tier law firm deployed our enterprise RAG system across 15 years of matter files, precedent databases, and legislative documents. Partners and associates now query the entire firm knowledge in seconds — surfacing relevant cases and clauses with full citation, compressing research time dramatically.
An engineering conglomerate built enterprise RAG across 50,000+ technical specifications, engineering manuals, safety protocols, and project reports. Engineers query cross-plant knowledge instantly — avoiding duplication, accelerating troubleshooting, and preserving institutional knowledge against attrition.
Talk directly to our lead engineers. We audit your requirements, propose the exact architecture, and give you a transparent roadmap — all in one call.