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:

Rachel Wolff·Updated on September 9, 2025
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headless cms localization workflow tools

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
best translation memory tools

6 best translation memory software for every use case

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

Updated on February 20, 2026·Shreelekha Singh
Fine-tuning_vs_RAG

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

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 localization platform

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
Automating context management in translation.webp

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
In-context-editor-tools.webp

The 5 best in-context editor (ICE) tools for localization quality

When translators work on a bunch of strings listed in spreadsheets and CSVs, they're essentially flying blind. Without seeing where these phrases appear, even experienced linguists struggle to make the right call on tone, length, and word choice. In-context editors (ICE) tools like Lokalise solve this issue by giving translators a real-time preview of the pages or apps they’re translating. So, they can see each string in the actual interface and edit it accordingly.

Updated on January 29, 2026·Shreelekha Singh
7 Predictions

7 bold predictions for the future of localization in 2026

The localization industry is currently sitting at a high-stakes betting table. The chips are AI, automation, and data, and the jackpot is global growth. But knowing exactly where to place those bets is the difference between leading the market and playing catch-up. We didn’t want safe guesses. We gathered 7 top localization experts from major tech partners like Webflow and language service providers like Acclaro and Argos to industry veterans and strategists, and asked them to place the

Updated on January 28, 2026·Brittany Wolfe

Stop wasting time with manual localization tasks.

Launch global products days from now.

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