Give your organization a reliable, governed AI knowledge system built on your own content.
Enterprise knowledge is scattered across documents, systems, and databases. RAG 2.0 is the architectural pattern that makes AI systems work reliably on that knowledge — with accuracy, traceability, and enterprise governance built in.
Employees and customers cannot efficiently access the knowledge locked in enterprise documents, policies, SOPs, and databases. Generic LLMs hallucinate when asked about company-specific information.
Reduce information retrieval time, improve answer accuracy, eliminate hallucination risk, and give employees and customers reliable access to enterprise knowledge through AI.
Typical deployment scenario
A financial services firm deploys an internal knowledge assistant that search across 50,000+ policy documents, regulatory guidelines, and product specifications — giving relationship managers instant, accurate answers with citations.
Solution capabilities
- Multi-source document ingestion (PDF, Word, SharePoint, Confluence, web)
- Intelligent chunking, embedding, and hybrid retrieval architecture
- Query understanding and intent routing
- Citation and source traceability in every response
- Access control and document-level permission enforcement
- Evaluation and quality monitoring framework
- Conversational interface or API integration
Key architecture layers
Suitable for
Related services
Ready to explore Enterprise RAG 2.0 for your organization?
Appxerbia will assess your requirements and outline the right solution design and delivery approach.