AI in the platform
- The AI Shelf
- AI title optimization
- AI categorization
- Feed audits
- ChatGPT Feed
- Perplexity Feed
AI resources
Why data completeness matters
A personalization engine can only segment, recommend and merchandise on the attributes it receives. Gaps in category, price or availability data quietly shrink what it can do.
The fields targeting and recommendation logic depend on are populated across the catalog.
Rules standardize categories and attributes so segments group the way you intend.
Sold-out products drop out of recommendations before they get promoted.
The same governed catalog behind your channels powers onsite experiences.
How it works
01
Your full catalog imports automatically with variants, attributes and inventory.
02
Rules normalize values and fill the fields personalization logic reads.
03
Catalog changes flow through automatically, keeping experiences current.
1
governed catalog behind personalization and every channel.
40+
channels the same catalog can feed.
0
stale exports driving what shoppers get recommended.
Questions, answered
What data does Monetate need?
A structured product catalog with attributes, categories, imagery, price and availability - delivered normalized and current.
Why do recommendations feel off?
They mirror the data. Missing attributes and stale availability skew what the engine can pick - fixing the catalog fixes the picks.
How current is the catalog?
As current as your schedule. Changes flow through automatically.
Other AI agents
Light closer. Renders for AI agent type only, which spends its dark beat on the hero.