How fragmentation happens
It usually looks like this: one model files the product under one category and another files it elsewhere; the same capability is named differently in each; or two engines cite sources from different years and report contradictory parameters.
The cause is a missing shared source of truth. When each locale maintains its own product description, drift is a matter of time.
What a source of fact must contain
- An official product name with a defined mapping across languages.
- A category definition covering what it is and is not.
- A capability list written as behaviour rather than adjectives.
- Parameters and limits, so vague or exaggerated answers are avoidable.
- Provenance for each material claim.
Capability descriptions matter most. A model can match "handles multi-region failover" in a way it cannot match a marketing adjective.
Let translations corroborate each other
Locale pages are two expressions of one fact set, not two documents. They should cross-link and agree on numbers, parameters and definitions.
Where both versions agree, each is more likely to be trusted: corroboration across sources is itself a signal.
Resolve conflicts before adding volume
While the entity is still fragmented, more content multiplies the inconsistency. Establish the shared fact set first, remove conflicts, then expand coverage.
Sequence markets deliberately
AI penetration differs sharply by market. Most teams should win the English ecosystem first, where the corpus is deep and competition is measurable, and add other languages once the fact set is stable. Running every language at once spreads resources thin and deepens fragmentation.