Multilingual catalog work involves more than translating a product title. A term can refer to different materials, sizes or use cases across markets, while two different phrases can describe the same attribute. An AI workflow should preserve product identity and surface ambiguity before it writes a confident description.
This is a proposed catalog-quality process. It does not claim that the projects on this site operate a multilingual AI catalog system, and it does not assume that fluent translation establishes product accuracy.
Anchor every language to the same product record
Use stable product and variant identifiers as the connection between languages. Titles are presentation fields and can change. The authoritative attribute record should retain units, permitted values, source and the date of verification.
Separate translatable language from facts that require conversion or market-specific handling. A measurement can be expressed in another unit, but the conversion must be checked. A local size label may require a mapping rather than a literal translation. Do not let the model infer equivalence from familiar wording alone.
Classify ambiguity before resolving it
| Ambiguity type | Example | Preferred handling |
|---|---|---|
| Missing attribute | Material not supplied | Keep unknown; request evidence |
| Conflicting records | Two different dimensions | Flag the conflict for an owner |
| Local terminology | A category name has several meanings | Use an approved glossary and context |
| Unit conversion | Centimeters to another unit | Apply a verified deterministic conversion |
| Product identity | Similar names across variants | Resolve through stable identifiers |
| Claim scope | A translated benefit becomes stronger | Review against the original evidence |
This classification helps route the problem. A missing fact should not be solved by selecting a more elegant phrase. A terminology disagreement should not require altering the product’s underlying identity.
Maintain a small controlled glossary
Start with terms that affect customer decisions: materials, compatibility, dimensions, package contents and care instructions. Include preferred wording, prohibited interpretations, context and examples. Let native-language reviewers challenge terms that are technically correct but misleading in ordinary use.
The glossary should be versioned and linked to affected records. When a term changes, identify which descriptions need review. Otherwise older language can remain mixed with the new standard, creating inconsistent promises across the catalog.
Design the model output to expose uncertainty
Ask for proposed wording and unresolved issues as separate fields. Require references to the source attributes used for factual claims. A missing or conflicting value should remain visible in the output rather than being smoothed into a plausible sentence.
NIST’s generative AI profile discusses confidently stated false content as a risk. The profile supports treating factual verification as a separate task from fluent generation. In catalog work, this is particularly important when the reviewer does not speak every target language.
Review with paired and adversarial cases
Compare the same product across languages for factual equivalence, then inspect each language for customer clarity. Include difficult cases: unfamiliar units, similar model names, accessories not included and attributes that do not apply. Test whether the system abstains when the evidence is insufficient.
Use bilingual reviewers where the wording affects suitability or a material product claim. Automated comparisons can catch changed numbers and missing fields, but they may not detect a subtle shift in meaning. The offline recommendation evaluation guide offers a related method for separating hard constraints from preference quality.
Publish with traceability
Keep the original attributes, proposed text, reviewer decision and published version connected. A corrected source fact should trigger review of affected languages. A customer report of misunderstanding should feed back into the glossary or the product data, not only into one edited paragraph.
The objective is consistent product meaning across useful local language. A multilingual AI workflow is dependable when it preserves what is known, clearly marks what is uncertain and makes the human review task easier to complete accurately.