Ertas for Translation & Localization

The Challenge

Translation in specialized domains is fundamentally different from general-purpose translation. Medical documents require precise translation of drug names, anatomical terms, and dosage instructions where an error can endanger patients. Legal contracts must preserve the exact meaning of clauses across languages, including jurisdiction-specific legal concepts that have no direct equivalent. Technical documentation needs consistent translation of product terminology, API names, and UI labels that match the localized version of the software.

Generic translation models — even high-quality ones — consistently fail on these domain-specific requirements. They translate brand names when they should be left untouched, use colloquial medical terms instead of clinical ones, and produce legally ambiguous phrasings of contract clauses. The cost of post-editing generic translations in specialized domains often exceeds the cost of translating from scratch, because reviewers must check every term and phrasing against domain-specific glossaries and style guides.

The Solution

Ertas allows organizations to fine-tune translation models on their own bilingual corpora, including translation memories, previously approved translations, and domain-specific glossaries. By training on examples of correct translations from their specific domain, the model learns not just language-pair mappings but the organization's preferred terminology, style conventions, and handling of untranslatable terms. Ertas Studio accepts parallel text pairs in JSONL format and handles the training pipeline end-to-end.

The fine-tuned model preserves brand names, uses approved terminology consistently, and produces translations that match the target market's conventions — formal vs. informal register, metric vs. imperial units, date formats, and cultural references. Deployed through Ertas Cloud or locally, the model serves as the first-pass translation engine in a human-in-the-loop workflow, producing drafts that require minimal post-editing by professional translators. As the organization's glossary evolves and new products are launched, periodic retraining in Ertas Studio keeps the translation model current without starting from scratch.

Key Features

Example Workflow

A global medical device manufacturer needs to translate product manuals, regulatory submissions, and training materials into 8 languages. Their existing translation workflow relies on a generic MT engine followed by extensive post-editing by specialized translators, costing an average of 12 cents per word. The localization team collects 200,000 previously approved translation pairs from their translation memory system and uploads them to Ertas Vault. Using Ertas Studio, they fine-tune language-pair-specific models that learn the company's product terminology, regulatory phrasing conventions, and style preferences. The fine-tuned models are deployed as API endpoints integrated with their CAT tool. Post-editing effort drops by 60%, and terminology consistency — measured by glossary adherence rate — improves from 78% to 97%. The per-word cost drops to 5 cents, saving the company over $400,000 annually on translation costs while improving quality.