Bring the context around a name together.
A name can appear in many notes without any one note telling the whole story. Entity recall brings a page, relevant facts and recent events into a single view.
Illustrative example
What do we know about the Atlas project?
Retrieve its project page, recorded facts and recent timeline entries, then follow the source pages for details.
Illustrative scenario, not a live product result.Recall more than a matching keyword.
An entity can have a page, a facts ledger and a timeline, with links to other people or projects. Entity recall gathers these pieces so an assistant can orient itself before answering. You can focus the fact retrieval on a topic when appropriate, such as a project’s current constraints. The result is grounded in stored knowledge, not an automatically complete profile.
Resolve names before making connections.
A person or project may appear under a short name, a title or an alias. Memrain has identity and slug-resolution tools that help find existing pages and surface ambiguity. Conservative matching matters: two similar names should not become one person just to make a graph look tidy. Check candidates before writing a relationship or adding a claim to an entity.
Keep the view honest about its limits.
Entity reads follow the caller’s permitted sources and can be bounded by a response budget. Missing content may reflect a limited grant, a soft reference without a full page or a truncated response. Follow the available pointers when a decision needs more evidence. A useful entity view should make investigation easier without pretending that every conversation or relationship was captured.
Keep the context. Move the work forward.
Connect your knowledge to the AI you already use. Find your starting point and make the next conversation count.