Abstraction turns raw records into events, profiles and metrics.
This layer reads from the raw store and abstracts it three ways at once. First, raw behavior is abstracted into an event model, where user actions are extracted for intent inference and assembled into a per-member timeline. Second, raw data is abstracted into member tags that build the user profile, or portrait. Third, it is extracted into metrics, a governed semantic layer that expresses the business truth. The three abstractions are produced in parallel from the same raw substrate.
Raw data is abstracted three ways in parallel: an event model for intent and timeline, member tags for the profile, and metrics for the business truth.
The value is operational, not just technical.
The AI does not have to parse raw records. It reads a member's inferred intent and timeline from the event model, their profile from tags, and governed business numbers from the metrics layer, all derived consistently from the same raw source.
Abstracts behavior into an event model for intent inference and a per-member timeline.
Abstracts raw data into member tags that assemble the user profile.
Extracts a governed metrics layer that expresses the business truth consistently.
Produces all three abstractions from one raw store, so they stay mutually consistent.
What this layer is responsible for
The event model extracts user behavior into intent signals and an ordered per-member timeline.
The tag model turns raw attributes and behavior into member tags that form the profile.
The metrics layer defines governed business measures so numbers mean the same thing everywhere.
All three read the same raw substrate rather than each maintaining a private copy.
What it makes usable
This is how the layer improves AI decisions.
Without a shared abstraction layer, intent logic, profile tags and business metrics get redefined in every tool. That produces conflicting numbers, inconsistent segments and AI context that no one can fully trust.
A sequence of product views can be abstracted into an intent signal on the member's timeline.
A member's raw attributes and behavior can be summarized as profile tags rather than raw rows.
A dashboard and an AI workflow can reference the same governed metric definition.