Oryne turns work context into a graph of entities, sources, and time-aware commitments so your assistant can answer with the chain of evidence, not just a similar paragraph.
Primary search intent
AI memory graph
Teams that need AI recall to understand relationships, deadlines, and changing context over time.
What teams get
Trace why an answer was returned
Recover multi-hop context across apps
Separate stale facts from active commitments
Vector search is useful, but it often returns isolated snippets. Oryne links the entities inside those snippets so the assistant can reason across people, documents, and time.
Real work questions rarely match one document perfectly. Oryne combines semantic similarity, keyword matching, and graph paths for higher-confidence recall.
When AI is used for executive, legal, finance, or security workflows, answers need to be explainable. Oryne keeps memory tied to sources.
Questions
An AI memory graph stores relationships between entities such as people, documents, projects, and events so an assistant can recall connected context over time.
No. RAG retrieves relevant content, while a memory graph also models relationships and change over time. Oryne uses both approaches together.
Work facts change. Temporal context helps the assistant distinguish a past plan from the current decision or unresolved promise.