From Drift to Discipline: Building Product Catalogs That Make AI Work

A practical execution guide for improving search, recommendations, and personalization

Retail AI performance doesn’t stall because models fail—it stalls when product catalogs stop behaving like production infrastructure.

As catalogs scale, taxonomy blurs, attributes fragment, and signals drift out of alignment. Models still run, but relevance plateaus and personalization underperforms.

This eGuide outlines a proven execution model used by leading retailers to keep product catalogs working as AI-ready infrastructure—so search, recommendations, and personalization continue to improve at scale.

In this e-book we’ll cover:

Enforce taxonomy precision so retrieval starts from the right universe
Differentiate similar products using attributes that drive intent and conversion
Normalize and validate catalog signals to prevent performance drift
Define measurable quality standards that make errors fixable

See how these controls are applied in real retail environments to support AI systems already in production—and prepare for increasingly AI-mediated shopping experiences.

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