Ecommerce ops · Production

Reversia uses Claude to power 99%-accurate e-commerce translation for 100+ Shopify brands

The problem

Existing Shopify translation apps produced low-quality, overly literal translations, capped supported languages, and left key content types like metafields and metaobjects untranslated with broken SEO internal linking. Reversia's own first version using conventional machine translation had the same flaws and required two to three weeks of manual work to launch each new language.

First attempt

Reversia's first version used a conventional machine translation engine that produced overly literal output with significant HTML errors, requiring two to three weeks of manual verification work per language launch.

Workflow diagram · grounded in source
1
Content change detected
trigger
“When content is created or updated, the platform detects the change and triggers a new Claude translation within 1 to 3 minutes”
2
Glossary rules injected
ai_action
“The core of Reversia's product is a glossary system that injects merchant-defined rules directly into Claude's prompt context at translation time. Rather than simple find-and-replace word matching, merchants can force specific term trans…”
3
Claude translates all content types
ai_action
“Claude also translates content types that often go unaddressed in localization: metafields and metaobjects, SEO title tags, meta descriptions, hreflang attributes, canonical URLs, and internal cross-language linking”
4
Haiku quality-check pass
validation
“Claude Haiku 4.5 running a secondary quality-check pass on all translated content to flag inconsistencies before publication”
5
Native speaker audit
human_review
“Reversia validates the output by having native-speaking translation professionals audit batches of merchant content across multiple language pairs”
6
Translated store published
output
“A merchant activates a new language, and the entire store is translated automatically: products, collections, metafields, URLs, SEO metadata, internal linking. No manual verification of links or content completeness is required”
Reported outcome

Claude-powered translation achieves 99% accuracy in native-speaker audits.
New language launches that previously took two to three weeks now take minutes with no manual verification required. Pricing runs 70–80% less than traditional translation services.

Reported metrics
Translation accuracy99%
Cost vs traditional translation pricing70–80% less
Time to launch new languageminutes vs two to three weeks
Translation trigger latency1 to 3 minutes
Show all 7 reported metrics
translation accuracy99%
cost vs traditional translation pricing70–80% less
time to launch new languageminutes vs two to three weeks
translation trigger latency1 to 3 minutes
benchmark quality vs alternativesscored highest on all four quality metrics
brands using Reversiamore than 100
supported languages110+
Reported stack
Claude Sonnet 4.6Claude Haiku 4.5Cloud RunCloud TasksShopify
Source
https://www.anthropic.com/customers/reversia
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Claude-powered translation achieves 99% accuracy in native-speaker audits.

What tools did this team use?

Claude Sonnet 4.6, Claude Haiku 4.5, Cloud Run, Cloud Tasks, Shopify.

What results were reported?

Translation accuracy: 99%; Cost vs traditional translation pricing: 70–80% less; Time to launch new language: minutes vs two to three weeks; Translation trigger latency: 1 to 3 minutes (source-reported, not independently verified).

What failed first in this deployment?

Reversia's first version used a conventional machine translation engine that produced overly literal output with significant HTML errors, requiring two to three weeks of manual verification work per language launch.

How is this ecommerce ops AI workflow structured?

Content change detected → Glossary rules injected → Claude translates all content types → Haiku quality-check pass → Native speaker audit → Translated store published.

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