A global industrial automation manufacturer maintains an extensive library of product information spanning hundreds of thousands of SKUs. This content includes technical specifications, reference manuals, installation guides, and marketing descriptions, all of which needed to be available in a dozen languages to serve a worldwide customer base. This meant million of documents and web pages needed to be translated. The sheer volume made manual translation impractical. The organization needed an automated approach, but one that could handle the precision demands of technical content.
General-purpose machine translation tools such as Google Translate are effective for conversational language but struggle with domain-specific terminology where a single word carries different meanings depending on context.
Consider the English word "current." In everyday language it can describe the flow of a river or mean "present-day." In an electrical engineering context it refers specifically to the flow of electric charge, measured in amperes. In Chinese, these meanings map to entirely different characters. A general translation engine frequently selects the wrong one because it lacks the surrounding technical context to disambiguate.
This class of error appeared throughout the corpus. Words like "drive," "fault," "load," "bus," and "terminal" each have technical meanings that diverge significantly from their common usage. At scale, these mistranslations rendered the output unusable without extensive human correction, which defeated the purpose of automation.
PiSrc deployed Metaphora, its AI-powered content transformation platform, to address these requirements.
Metaphora's translation pipeline operates differently from conventional machine translation in several ways.
Contextual disambiguation. Metaphora processes content within its surrounding technical context rather than sentence by sentence. When the word "current" appears alongside terms like "rated," "output," or "AC/DC," the system recognizes the electrical domain and selects the appropriate target-language term. This contextual awareness extends across paragraphs and document sections, not just individual sentences.
Embedded business rules. The client required conformance to specific ISO standards for units and measures. Rather than treating this as a post-processing step, PiSrc integrated these rules directly into the translation pipeline. Units are converted and formatted according to the applicable ISO standard for each target language and region.
Currency and price formatting. Product pricing needed to be presented consistently across all languages, even when the price was denominated in a currency other than the local default. Formatting rules for decimal separators, currency symbol placement, and thousand-group delimiters were encoded as business rules that the AI applied during translation rather than after.
Brand and terminology glossaries. A controlled vocabulary of product names, trademarks, and proprietary terms was maintained as a glossary that the translation engine referenced to ensure these terms were handled consistently, whether transliterated, kept in English, or mapped to approved local equivalents.
The translation pipeline processed the full content library across all target languages. Several outcomes are worth noting.
Accuracy on domain-specific terminology improved substantially compared to the organization's prior experience with conventional machine translation. The contextual approach eliminated the most disruptive class of errors, those where a common English word was translated into an entirely wrong domain.
ISO and formatting compliance was enforced automatically, removing what had previously been a manual audit step.The cost per translated page came in at a fraction of what human translation agencies had quoted for comparable volume, even accounting for the review cycles applied to the initial output.
The pipeline is reusable. As new products are added to the catalog, their content enters the same translation workflow without additional configuration.
