Machine Translation
AI Post-Editing at Scale: What a 71,262-Segment Study Reveals About the Future of TranslationÂ
What is the most effective way to apply large language…
Why Semantic Similarity Isn’t Enough for Translation Quality EstimationÂ
As AI becomes more deeply embedded in enterprise translation workflows,…
When Is a Picture Worth a Thousand Words? What New Research Reveals About Visual Context in AI TranslationÂ
As AI continues to reshape translation workflows, advances in vision-language…
+16 Points Closer: What ChrF Means for Your Workflow
Why translation quality metrics can reveal hidden efficiency gains long…
Why Trust Is Part of AI CapabilityÂ
When organizations evaluate AI, the first question is usually straightforward:Â Can…
Improving Translation Quality Without Starting OverÂ
Why the next generation of AI should build on your existing linguistic…
Translation Systems Should Learn Over TimeÂ
Why continuous feedback loops create compounding quality gains that static…
Three Assumptions About AI in Localization That Production Experience ChallengesÂ
Assumptions harden fast in a fast-moving market.
What a Year of Agentic AI for Multilingual Content Taught UsÂ
The operational lessons shaping Opal’s continuous evolution and the next…
How Global Brands Are Scaling Content Without Losing Brand VoiceÂ
Enterprise AI translation has entered a new phase.
Rethinking Patent and IP Workflows Across Jurisdictions
The hardest part of global patent filing has never been…
How AI Is Changing Multilingual Execution
From AI training data to agentic workflows, multilingual execution is…