In this episode of AI Navigators, we sit down with Sasho Savkov, Engineering Manager for the AI/ML team at Lokalise. With a PhD in clinical information extraction and nearly a decade building healthcare solutions, Sasho brings a unique perspective on what’s actually working versus what’s just noise. He challenges one of the biggest assumptions in AI today: that current single-shot learning approaches will lead us to human-level intelligence. His insights reveal why the missing piece isn’t more sophisticated models, but something much more fundamental. Watch the full interview below:
What you'll learn in this article: Why AI answer engines have a built-in language bias — and what it means for your brand's global visibilityThe four signals that determine whether your brand gets cited in AI search resultsHow to build the content infrastructure that earns A
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
Global expansion is back on the table for 2026. But many teams are moving into new markets faster than their localization strategy can keep up. To see how companies are actually preparing for international growth, Lokalise surveyed 500 global business leaders. What we found was a gap between intention and execution, especially around translation quality, regional investment, and budget priorities. For product, marketing, and growth leaders, the takeaway is straightforward: localiz
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
Every time you launch a new campaign, you have to translate everything across multiple languages — product UI, marketing pages, help docs, and more. Without translation memory, translators start from scratch every time — even for content they've already translated. You pay full rates again. Every team uses its own terminology again. And your localization process delays market entry. Translation memory (TM) software stores every translated string as a reusable segment in a d
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
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
Behind the scenes of localization with one of Europe’s leading digital health providers
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