Mia has 13+ years of experience in content & growth marketing in B2B SaaS. During her career, she has carried out brand awareness campaigns, led product launches and industry-specific campaigns, and conducted and documented demand generation experiments. She spent years working in the localization and translation industry.
In 2021 & 2024, Mia was selected as one of the judges for the INMA Global Media Awards thanks to her experience in native advertising. She also works as a mentor on GrowthMentor, a learning platform that gathers the world's top 3% of startup and marketing mentors.
Earning a Master's Degree in Comparative Literature helped Mia understand stories and humans better, think unconventionally, and become a really good, one-of-a-kind marketer. In her free time, she loves studying art, reading, travelling, and writing. She is currently finding her way in the EdTech industry.
Mia’s work has been published on Adweek, Forbes, The Next Web, What's New in Publishing, Publishing Executive, State of Digital Publishing, Instrumentl, Netokracija, Lokalise, Pleo.io, and other websites.
The meeting is going well. Translation costs are down, turnaround times are shorter, and AI is taking on more of the work. Then your VP asks a question: “How do you know the quality of AI translations is good enough?” The expensive part usually starts once you try to answer that question inside a system you built: the real cost of buildi
Developer-focused translation management systems (TMS) are designed to fit into existing engineering workflows. Instead of relying on manual exports, imports, and handoffs, they help teams automate localization through APIs, version control, CI/CD pipelines, and SDKs. This guide compares the best TMS platforms for developers, based on what actually matters in practice:
GenAI isn’t just changing how translations are produced. It’s reshaping the entire localization workflow. Most teams didn’t build their workflows for this shift. They’re still relying on a mix of spreadsheets, standalone MT engines, and manual file handoffs. It’s familiar, and for a while, it worked. But it was never designed for the scale teams are dealing with now.
A translation changed, but no one knows who did it or when. The release is already live. What do you do? When multiple contributors work on the same project, there’s a good chance that strings get edited, approved, overwritten, or renamed. Without a clear localization audit trail, small changes turn into production issues, and tracking them down takes time you don’t have. This lack of visibility slows you down significantly. It makes troubleshooting harder, weakens accountability,
AI translation post-editing tools promise 40-60% cost savings. In practice, you only get those savings when AI output is controlled. This means your terminology is enforced, risky segments are flagged before they go live, and linguists only touch what truly needs human attention. That’s why the best MTPE tools today aren’t standalone CAT tools or MT engines. They’re actually translation management systems (TMS) that orchestrate AI, terminology, and quality assurance in one place.
As of 2026, the Total Cost of Ownership (TCO) for enterprise localization has shifted from a per-word human model (~$0.20/word) to an orchestrated AI model (~$0.002/word). This 100x efficiency is driven by AI Orchestration, which automates context retrieval (RAG) and eliminates manual project management overhead. This shift comes from AI orchestration. These systems combine large language models with retrieval-augmented context, terminology databases, translation memory, and automated q
Headless CMS platforms are built for speed. You publish once, every frontend updates. But then localization shows up, and the “fast” stack suddenly depends on a rather slow routine: export strings, email a file, chase approvals, import, fix broken formatting, repeat. If you’re a developer or content manager, you’ve felt the cost. Releases slip and translators work from stale content without even knowing it. Engineers get pulled into one-off fixes. Users end up seeing mixed terminology a
Fine-tuning sounds like the clean way to improve AI translation quality. You train the model on your content with the expectation it’ll learn your style. In practice, generic fine-tuning is where enterprise translation programs get stuck. The issue is, the model absorbs everything in the training mix. This includes old releases, mixed brands, and inconsistent phrasing, which means you end up with contextual contamination. That’s when the model starts making confident ch
Behind the scenes of localization with one of Europe’s leading digital health providers
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