AI Personalization

What AI personalization means for AI-native products

Classic personalization was built for web and app funnels: track behavior, segment users, serve the closest match. AI-native products need something different. The output is generated, the interaction is conversational or agentic, and the bar is no longer relevant to the query but right for this person. That raises two questions most systems answer poorly: how to model a person's taste, and how to do it without collecting personal data.

Relevance is not the same as resonance

A relevant result matches the request. A resonant result matches the person. Most ranking systems optimize for relevance because it is measurable from clicks and queries. But two people asking the same question rarely want the same answer, and the gap between correct and compelling is taste. Personalization that ignores taste plateaus at relevance.

The two problems that break most systems: cold-start and PII

Behavioral personalization needs history, so it fails exactly when it is needed most: new users, new items, sparse catalogs. And the usual fix, collecting more personal data, is now a liability under GDPR and CCPA and a growing source of user distrust. A modern approach has to work from the first interaction and without storing personal identifiers.

How Galya approaches it

Galya models taste as structured context. Its Taste Graph places people and content on grounded scales of perception, so an AI product can estimate affinity, how well an item fits a person, and rank by resonance rather than popularity. Because taste is modeled structurally, it works in the cold and sparse regime, and because it computes preference signals at inference without personal data, it stays compliant by design. The result is delivered through developer SDKs and agent tools, so teams add it without rebuilding their stack.

Give your AI a sense of taste

See how the Taste Graph composes for your product, and what preference-correct ranking looks like on your own data.