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:
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:
You're in Cursor, working on a new feature, and you need to add a localization key. That means leaving your IDE, opening your TMS, navigating to the right project, making the change, and coming back. Then, doing this all over again the next time you need to check untranslated strings, create a task, or touch anything localization-related. Model Context Protocol (MCP) removes this context-switching loop entirely. Instead of bouncing between tools, your AI coding assistant can inter
Lokalise gives developers two programmatic ways to manage localization workflows: the REST API and the MCP Server. In any MCP vs API localization decision, the key point is that one does not replace the other. The REST API is built for scripted, repeatable, deterministic automation, such as CI/CD pipelines, batch imports, and webhook-driven deployments.
How to build the business case that turns localization from a line item into your highest-return growth lever. Your board wants growth from global markets. Your competitors are already there. And your localization team has the tools to deliver. So why is localization still stuck in the "cost center" conversation? It's a familiar tension. The data overwhelmingly shows that localization drives revenue — through discoverability, conversion, and loyalty. Yet in too many organization
You'd think speaking the same language would be enough. But when Apple—one of the most recognized brands on the planet—ranks dead last for localization in the UK, a native English-speaking market, it's clear that "going global" takes a lot more than translation. Lokalise surveyed 1,000 consumers across eight countries to find out which brands actually feel local, and here's what we found. Key takeaways McDonald's ran
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,
LLMs are fluent in generating outputs, but they're not faithful to your brand. When a general-purpose LLM translates your product UI into 15 languages, it doesn't know which terms are trademarked, which phrases have legal restrictions, or which features are deprecated. It makes a statistical guess. At scale, this guesswork can lead to major inconsistencies, compliance risks, and a post-editing overload. The challenge: AI models’ probabilistic outputs are not ideal
Behind the scenes of localization with one of Europe’s leading digital health providers
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