Headless CMS localization tools: Build a hands-off translation workflow
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
Updated on February 20, 2026·Mia Comic The fine-tuning trap in AI translation
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
Updated on February 11, 2026·Mia Comic Term base best practices: How to build a living terminology system
Most term bases fail because they live somewhere where nobody works. A spreadsheet gets created, a few people bookmark it, and then the real work happens in the editor, in Slack, in Figma, and in whatever AI tool is generating the next draft. That gap is expensive. Terminology drifts, reviewers rewrite the same phrases, and “small” naming mistakes turn into brand inconsistency in translation, SEO issues, and support tickets that shouldn’t exist. This guide covers term base best pr
Updated on February 10, 2026·Mia Comic ChatGPT vs. a localization platform: Which translation solution is right for your business?
Translation used to be a bottleneck for businesses looking to grow internationally. Thanks to AI, now it can happen in seconds, which changes how teams make decisions about localization. ChatGPT can produce quick translations for many everyday needs. But when quality requirements rise or the workload grows, teams start running into familiar problems: inconsistent terminology, formatting and placeholder issues, missing context, and a lack of review workflows. That’
Updated on February 3, 2026·Mia Comic How to automate context management in translation
Most localization teams struggle with finding the right context. Translators jump between the TMS, Jira, Figma, Slack, and old docs just to understand a single string. Reviewers approve copy without ever seeing the screen it lives on. Developers spend hours every week explaining where text appears and what it must not break. All of this context switching in localization slows releases and drives up translation rework. On paper, it looks like “bad translation.” In reality, it
Updated on February 3, 2026·Mia Comic Top 5 most secure localization tools for healthcare
If you work in healthcare or life sciences, your localization stack lives under the same microscope as your clinical and regulatory content. In translation, GxP validation means your workflows run in a controlled, documented, and tested environment, with defined roles, locked-down steps, and an audit trail for every change that touches patient, clinical, or device-facing text. Keep reading for a clear comparison of the five best healthcare localization tools that can support this
Updated on January 7, 2026·Mia Comic Best fintech translation tools for secure and compliant localization
When you localize banking products, relying on “good enough” fintech translation services isn’t safe.The most secure option is a dedicated translation management system (TMS) suitable for fintech, like Lokalise. Your tool of choice needs to promise ISO 27001-grade security, granular access controls, reliable APIs, and strict glossary enforcement so every disclosure, rate, and fee description stays consistent and audit-ready. In this guide, you’ll find the best financial transl
November 23, 2025·Mia Comic ChatGPT vs. machine translation: Is it a fair comparison?
Most teams today are asking themselves: Should we use ChatGPT or machine translation for localization? The real question should be: How can we get the best results from both, without wasting time or money? Large Language Models (LLMs) like ChatGPT are changing the way we approach translation. They offer fluency, creativity, and context awareness that traditional Neural Machine Translation (NMT) engines can’t always match. However… To get localization-ready output fro
Updated on January 30, 2026·Mia Comic 
