AI Translation for Ecommerce in 2026: Picking the Right Tool for Catalogs

AI translation for ecommerce catalogs: why it is different

AI translation for ecommerce catalogs is fundamentally different from translating website copy or marketing content. Product catalogs contain structured data — titles, descriptions, attribute values, category names, and spec labels — that must be translated with terminological precision, consistency across thousands of SKUs, and format preservation.

A blog post can tolerate creative translation. A product listing cannot. If "stainless steel" is translated differently across 500 kitchen products, your filters break, your search relevance drops, and your customers lose trust.

The challenge has gotten more manageable in 2026 as LLM-based translation tools have improved dramatically. But the quality gap between "good enough" translation and "catalog-ready" translation still requires the right workflow, tooling, and QA process.

This guide compares translation approaches, covers the critical role of glossaries and terminology control, and shows how to integrate translation into your product data pipeline. For a companion guide on what happens after translation (cultural adaptation, units, compliance), see our article on AI localization for ecommerce.

Translation approaches compared: MT, LLM, and human hybrid

Traditional machine translation (MT)

Tools like Google Translate, DeepL, and Amazon Translate use neural machine translation models trained on large parallel corpora. They are:

• Fast and cheap — millions of words per hour at minimal cost
• Good for simple product content — standard descriptions, common materials, basic attributes
• Weak on terminology consistency — the same term may be translated differently in different contexts
• No context awareness — MT does not know that "mouse" means a computer peripheral in your electronics catalog

Best for: high-volume, low-complexity catalogs where speed matters more than nuance. Supplement with glossary enforcement and post-editing.

LLM-based translation

Large language models (GPT-4, Claude, Gemini) offer a step up from traditional MT:

• Context-aware — you can provide instructions like "this is an electronics catalog, translate technical terms consistently"
• Style-controllable — you can specify tone, formality, and target audience
• Glossary-friendly — include a terminology list in the prompt and the model will follow it
• Better at edge cases — handles ambiguous terms, measurement units, and format requirements more intelligently

Best for: mid-to-large catalogs where quality matters and you can invest in prompt engineering and glossary management.

Human translation and post-editing

Professional human translators remain the gold standard for:

• Regulated categories — medical devices, supplements, cosmetics, children's products
• Brand-sensitive content — luxury goods, fashion, lifestyle products where tone matters
• Complex technical content — industrial equipment, specialized materials, safety-critical specs

The practical approach in 2026 is LLM translation with human post-editing (MTPE). The AI handles 80-90% of the work; human translators review and fix terminology, style, and cultural issues.

Comparison summary

Most ecommerce teams use a tiered approach: LLM translation for standard product content, human MTPE for high-value categories, and full human translation for regulated or legally sensitive content.

Terminology control: the make-or-break factor

The single most important element in catalog translation quality is terminology consistency. Without it, even great AI translation produces a messy catalog.

Building a translation glossary

A translation glossary (also called a termbase) is a list of terms with their approved translations. For ecommerce, it typically includes:

• Brand names — do they stay in English or get adapted? (Usually stay in English)
• Material names — "polyester," "GORE-TEX," "memory foam" → approved translations per language
• Category terms — "running shoes," "trail shoes," "walking shoes" → consistent terminology
• Technical specs — "watts," "lumens," "BTU," "mAh" → units and labels
• Attribute values — standardized translations for "small," "medium," "large," or color names
• Do-not-translate terms — brand names, model numbers, certifications (CE, UL, GOTS)

Enforcing the glossary

• In LLM prompts — include the glossary directly in the translation prompt. Modern LLMs follow terminology lists well when explicitly instructed.
• In MT post-processing — run automated checks after translation to flag terms that deviate from the glossary
• In TMS tools — Translation Management Systems like Smartcat, Phrase, or Lokalise have built-in glossary enforcement

Maintaining the glossary

Glossaries are not static. Assign an owner who:

• Adds new terms when new product categories launch
• Resolves conflicts when translators disagree on terminology
• Reviews glossary coverage quarterly against your active catalog

For more on how inconsistent terminology affects product titles across suppliers, see our article on fixing inconsistent product titles.

QA for translated product data

Translation QA for catalogs requires both automated and manual checks.

Automated QA checks

Run these on every translation batch:

• Glossary compliance — flag terms translated differently than the glossary specifies
• Number and unit preservation — verify that measurements, weights, and dimensions were not altered during translation
• Format preservation — bullet points, HTML tags, special characters, and field delimiters must survive translation intact
• Length checks — translated titles and descriptions that exceed channel character limits need truncation or rewriting
• Placeholder integrity — if your content uses placeholders ({brand}, {size}), verify they are preserved
• Missing translations — flag empty fields or fields that were not translated

Manual QA sampling

Pull a stratified sample (by category, language, and content type) and have a native speaker review for:

• Natural phrasing (not robotic or awkward)
• Correct terminology usage
• Cultural appropriateness
• Accuracy of technical content

For high-value categories, review 10-15% of translated SKUs. For standard categories, 3-5% sampling is usually sufficient.

Tools like Lasso can help standardize the source data before translation — ensuring consistent attribute values, clean descriptions, and normalized formats — which dramatically reduces translation errors downstream.

Integrating translation into your product data pipeline

Translation should not be a standalone project. It should be a continuous step in your product data pipeline that runs whenever new products are added or existing products are updated.

Pipeline architecture

A robust translation integration looks like this:

1. Source enrichment — clean, enrich, and standardize product data in the source language (tools like Lasso handle this step)
2. Translation trigger — new or updated products are automatically queued for translation
3. Translation execution — API call to your translation tool (LLM, MT, or TMS) with glossary and instructions
4. QA checks — automated validation runs on the translated output
5. Human review — flagged items go to a translator or reviewer queue
6. Publishing — approved translations are pushed to the target catalog, feed, or PIM

Integration patterns

• PIM-to-TMS — your Product Information Management system sends content to the Translation Management System via API, and receives translations back
• Direct LLM integration — for simpler setups, call an LLM API directly from your catalog pipeline with structured prompts including the glossary
• Feed-level translation — translate the output feed rather than the source catalog. Simpler but harder to maintain consistency.

Practical tips

• Translate at the source, not the feed. Translating in your PIM or catalog system means the translation persists and does not need to be regenerated every feed cycle.
• Track translation coverage. Dashboard showing what percentage of your catalog has been translated per language.
• Version translated content. When the source changes, flag the translation for re-review rather than silently overwriting.

For more on managing multichannel product content at scale, see our guide on listing 1,000 products across channels.

Choosing the right tool for your team

The right translation tool depends on your scale, quality needs, and technical setup:

Most teams start with LLM-based translation and add TMS tooling as language count and catalog size grow.

Lasso helps on the upstream side — ensuring your source product data is clean, complete, and structured before it enters the translation pipeline. Explore use cases or reach out for a demo to see how enrichment and translation work together.