Master Unbabel with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Contact Unbabel sales team at unbabel.com to discuss your customer support translation needs and volume requirements Schedule a demo to see how Unbabel integrates with your existing customer support platform (Zendesk, Intercom, etc.) Work with Unbabel team to configure language pairs and quality settings based on your customer base Complete integration setup and train your team on the Unbabel dashboard for managing translation workflows
💡 Quick Start: Follow these 1 steps in order to get up and running with Unbabel quickly.
Explore the key features that make Unbabel powerful for testing & quality workflows.
Source text is translated by Unbabel's machine translation stack, scored by a Quality Estimation model, and selectively routed to human editors only when confidence is below a configurable threshold. This minimizes cost and latency while maintaining quality above pure-MT baselines.
Unbabel's proprietary translation-focused large language model, TowerLLM, is tuned for multilingual tasks and can be further adapted on customer-specific glossaries, translation memories and style guides to enforce brand voice across languages.
Native connectors for Zendesk, Salesforce Service Cloud, Freshdesk, Intercom, Helpshift and Gorgias allow inbound and outbound tickets, chats and emails to be translated inside existing agent workflows without platform switching.
A central console surfaces translated volume, quality scores, cost, channel breakdown and SLA attainment, giving localization and support leaders visibility that traditional LSP workflows rarely expose.
Administrators can manage do-not-translate lists, approved terminology, tone-of-voice rules and translation memory in a single interface, ensuring consistency across agents, channels and languages.
A core piece of Unbabel's IP: a model that predicts translation quality at the segment level, driving the routing decision between full-automation delivery and human post-editing. Used in research publications and deployed in production.
SOC 2 Type II, ISO 27001 and GDPR compliance, plus data residency options, role-based access control and audit logging, meet the procurement requirements of regulated enterprise buyers.
Google Translate and DeepL are pure neural machine translation engines — fast, cheap and fully automated, but with no human quality layer. Unbabel runs its own MT first, then uses a Quality Estimation model to identify low-confidence segments and route them to human post-editors before delivery. The result is higher and more consistent quality for business-critical use cases like customer support, at the cost of higher price and some added latency.
Unbabel supports more than 30 languages across the major European, Asian and Latin American markets, including all widely spoken enterprise languages like English, Spanish, French, German, Portuguese, Italian, Dutch, Japanese, Chinese (Simplified and Traditional), Korean, Arabic, Russian and the Nordic languages. Coverage is oriented around customer-service demand rather than long-tail languages.
Unbabel uses custom enterprise pricing negotiated per contract, typically based on translated word volume, language pairs and the integrations or quality tier required. There is no public price list or self-serve plan; prospective customers engage through a sales team that scopes usage and provides a quote.
Unbabel supports chat and messaging translation with near-real-time latency when confidence is high and content is routed fully through MT. However, segments sent to human editors add minutes to hours of turnaround, so teams needing sub-second translation for live voice or synchronous chat often combine Unbabel with an MT-only fallback or use it primarily for asynchronous channels like email and tickets.
Yes. Enterprise customers can upload glossaries, translation memories, style guides and do-not-translate lists, and Unbabel's models can be adapted on customer-specific data. The LangOps layer lets teams enforce brand terminology and tone consistently across languages and channels, which is a key differentiator from off-the-shelf MT.
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Tutorial updated March 2026