Enterprise AI · Knowledge retrieval
UniSmart Hybrid RAG Knowledge Engine
Retrieval-augmented generation (RAG) uses relevant information from knowledge sources to provide context to an AI response. The UniSmart product’s availability and implementation details have not been confirmed on this website.
Status: Availability to confirm.
Product details to verify
These are validation questions, not claims about implemented behavior.
- Knowledge source connectors, supported file formats, and ingestion limits.
- Parsing, chunking, embedding, keyword and vector retrieval, ranking, and citations.
- Tenant boundaries, source-level permissions, and how source updates or deletions propagate.
- Retention, re-indexing, evaluation methods, deployment dependencies, and known limitations.
- How retrieved context is sent to a model and what that provider may retain or process.
Questions for a RAG evaluation
Bring the knowledge sources, access rules, user groups, data sensitivity, and expected answer behavior you need to support. The team can confirm whether a current product or engineering engagement fits those requirements.
Retrieval can surface incorrect, outdated, or unauthorized content if source permissions and evaluation are inadequate. Validate access control, data retention, and answer quality for the intended deployment.
Discuss a knowledge AI use case