You rolled out the interface in German — the "Save" button went off-screen, dates are in American format, and the error message is still in English. Each manual addition of a new language turns into 3–4 weeks of fixes: errors in plural forms, lost placeholders, inconsistent terminology. We automate this process: from code audit to automatic translation with context awareness. Over many years, we've handled dozens of projects for clients in fintech, e-commerce, and SaaS — average localization time savings reached 60%. For instance, for one fintech product, we cut the localization cycle from 3 months to 2 weeks, saving the company over $50,000 per release.
A typical case: a fintech startup with a React interface in 8 languages. After audit, we found 1200 hardcoded strings, 300 of which broke layout on RTL languages. Implementing i18n + AI translation shortened the release cycle from 2 weeks to 2 days.
Why Internationalization Is the Foundation of Localization
Without proper i18n architecture, any translation breaks layout and logic. Main issues in existing projects:
- Hardcoded strings instead of i18n keys
- String concatenation instead of placeholder formatting
- Ignoring plural forms (in Russian — 4 forms: 1, 2-4, 5+, 0)
- No support for RTL (Arabic, Hebrew)
- Hardcoded date and number formats
# Bad: concatenation message = "Найдено " + str(count) + " результатов" # Good: ICU MessageFormat message = t("search.results_count", count=count) # In locale file: "search.results_count": "{count, plural, one {Найден # результат} few {Найдено # результата} many {Найдено # результатов} other {Найдено # результата}}" How AI Analysis of the Codebase Identifies Bottlenecks
We scan the repository using an AST parser and machine learning. The system finds:
- All hardcoded strings (AST analysis + regular expressions)
- Date/number formatting without using the Intl API — MDN recommends this API for localization
- String concatenations with variables
- Images with embedded text (OCR)
class I18nAudit: def audit_codebase(self, repo_path: str) -> AuditReport: issues = [] for file in self.scan_files(repo_path, extensions=[".ts", ".tsx", ".jsx", ".py"]): ast_tree = parse_ast(file) for node in ast_tree.string_literals: if not self.is_in_i18n_call(node) and self.looks_like_ui_text(node.value): issues.append(I18nIssue( file=file, line=node.line, text=node.value, issue_type="hardcoded_string", suggested_key=self.suggest_key(node.value) )) return AuditReport(issues=issues, summary=self.summarize(issues)) How AI Understands That "Save" Is Both a Button and an Action?
Ordinary machine translation (MT) outputs "Сохранить" for both cases. Our system considers context: element type (button, header, message), screen, user role. A terminology glossary ensures consistency — one term is translated the same way throughout the application.
def translate_with_context( key: str, source_text: str, context: UIContext, target_lang: str, glossary: Glossary, tm: TranslationMemory ) -> Translation: tm_match = tm.find_match(source_text, min_similarity=0.85) if tm_match and tm_match.similarity > 0.95: return tm_match.translation terms = glossary.find_terms(source_text, target_lang) translation = mt_engine.translate( text=source_text, target_lang=target_lang, context=f"UI element: {context.element_type}, screen: {context.screen_name}", enforce_terms=terms ) tm.store(source_text, translation, target_lang, context) return translation According to our data, context-aware translation reduces post-editing corrections by 60% compared to direct MT, further saving the localization budget.
Pseudolocalization: How to Test Localization Before Translation?
Before the real translator starts, we run pseudolocalization: replace characters with decorated ones (e.g., [Ħȇŀŀǿ]) and expand strings by 30% — simulating German or Finnish behavior. This immediately reveals text truncation in UI, incorrect placeholder markup, and hardcoded element sizes.
Continuous Localization: How Not to Break CI/CD?
Integration with TMS (Crowdin, Lokalise, Phrase) via API: with each commit, new strings are automatically sent for translation. QA check before publication: string length, placeholder integrity, absence of machine artifacts. The whole process takes minutes, not days.
| Approach | Time for 5 languages | Cost | Quality |
|---|---|---|---|
| Manual translation | 10–15 weeks | High | Depends on translator |
| Machine translation (without context) | 2–4 weeks | Medium | Requires post-editing |
| Our AI system | 1–2 weeks | Optimal | High, minimal fixes |
Steps for Implementing AI Localization
| Step | Duration |
|---|---|
| Codebase audit | 2–3 days |
| i18n infrastructure implementation | Up to 2 weeks |
| TMS and glossary setup | 1 week |
| Translation automation | From 2 weeks |
| Pseudolocalization and QA | 3–5 days |
What's Included in the Work?
- Codebase audit — identification of all i18n issues (report with recommendations). Takes 2–3 days.
- i18n infrastructure implementation — framework setup, string formatting. Up to 2 weeks.
- TMS and glossary setup — connection to Crowdin/Lokalise, terminology creation. 1 week.
- Translation automation — integration of AI engine with context. From 2 weeks.
- Pseudolocalization and QA — layout testing before translation, string validation.
- Release support — monitoring new strings, automatic translation.
Timelines: from 2 weeks (audit + basic implementation) to several months for deep localization of 10+ languages. Cost is calculated individually per project.
Advantages of AI Localization
Over many years, we have implemented dozens of projects in fintech, e-commerce, and SaaS. We guarantee terminology consistency and full placeholder coverage. Automation allows entering new markets 3 times faster compared to manual approach.
Contact us for a consultation — we'll show how localization automation can accelerate your entry into new markets. Request a codebase audit: get a free analysis of one of your repositories.