Hybrid Retrieval

Different questions need different kinds of context.

Some questions need exact matches. Some need conceptual similarity. Others need related context such as ownership, history, or neighboring information.

CortexDB is designed to support all of those retrieval needs together instead of forcing every query through a single retrieval mode.

Why hybrid retrieval matters

In practice, AI applications ask for more than one kind of answer.

For example:

• exact names, identifiers, and terms
• semantically related ideas phrased in different ways
• connected context across people, systems, and workflows
• supporting evidence around a decision or event

A single retrieval strategy often misses part of the picture. Hybrid retrieval is about improving the quality of context made available to the application.

What CortexDB is designed to combine

At a high level, CortexDB helps applications work with:

• text-oriented retrieval for exact matches
• semantic retrieval for conceptual similarity
• connected-context retrieval for related entities and history

This makes it easier to support assistants, copilots, and agent workflows that need recall to be both relevant and useful.

Common workflows

Hybrid retrieval is especially valuable for questions like:

• what should I know before changing this service?
• what changed recently for this customer or project?
• who owns this workflow and what history matters here?
• what prior discussion or decision explains the current state?

Retrieval as part of an application workflow

The most important public-facing idea is not the exact internal ranking method. It is that CortexDB is designed to return context that is more complete, cross-referenced, and operationally useful than a single retrieval mode alone.

What this page intentionally does not cover

This page stays at the product and workflow level. It does not describe the internal ranking formulas, fusion logic, or indexing implementation.

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