Knowledge Graph

CortexDB helps applications work with connected context, not just standalone documents or messages.

At a product level, that means memories can be understood in terms of the people, projects, systems, customers, and decisions they relate to.

Why connected context matters

Many important questions are not purely semantic search questions. They are relationship questions.

For example:

• who owns this service?
• what changed before this incident?
• what decisions relate to this project?
• what tools, teams, and workflows are connected to this customer issue?

A knowledge-graph-oriented memory layer helps applications answer these kinds of questions with more context and continuity.

What CortexDB helps surface

CortexDB is designed to help connect memory across things like:

• people and teams
• services and systems
• projects and initiatives
• documents and conversations
• incidents, actions, and outcomes

This makes it easier for copilots, assistants, and workflow agents to retrieve related context instead of returning isolated fragments.

Common use cases

Connected memory is especially useful for:

• engineering knowledge and ownership workflows
• support and case history exploration
• incident and operations context
• research assistants that accumulate linked findings
• enterprise agents that need cross-tool context

A practical way to think about it

The public-facing idea is simple:

• memory should preserve useful context
• related memories should stay discoverable together
• applications should be able to move from one piece of context to another

That is the value of a knowledge graph in CortexDB: helping memory behave more like connected organizational knowledge and less like a pile of disconnected text.

What this page intentionally does not cover

This page is intentionally focused on the product concept of connected context. It does not go into internal extraction pipelines, storage structures, or graph implementation details.

Next Steps