As the global utilization of AI-powered translation continues to increase exponentially, there's a growing interest in ensuring that its outputs align with brand standards and are tailored to customer’s specific needs. One effective approach to achieving this is through the use of custom machine translation (MT) engines. By leveraging a company's linguistic assets, such as translation memory, style guides, do-not-translate lists and glossaries, these engines can be customized to optimize outputs for brand compliance and quality.
However, the pivotal question remains: what is the actual impact of customized engines on aspects like translation quality and brand alignment? Are they truly worth the investment, and how can they be utilized most efficiently? Watch this webinar to hear Smartling AI leaders Olga Beregovaya and Alex Yanishevsky delve into these questions and more.
This session covers:
- What it takes to build a custom MT engine
- The advantages (and potential drawbacks) of custom models
- How to evaluate the return on investment of implementing a custom models
- The best place to add custom models to your translation workflow
