Still unsure if AI translations are “good enough”? You’re not alone.

Despite major advances in AI, many localization teams still hesitate to adopt it at scale.

Why? Because quality often feels generic and like a black box.

In this webinar, you’ll learn how to:

Build real confidence in AI translations
Move beyond biased spot checks
Evaluate AI quality with data
Personalize AI using your own past translations and localization assets

So you can finally achieve human-like results and trust AI with your most important content.

Who should attend?

This session is designed for Localization Managers, Product, and Engineering teams looking to improve translation quality, streamline workflows, and confidently scale AI in their localization programs.

What you’ll learn

By the end of this session, you’ll know how to:

✅ Reach 90–95% publish-ready quality using personalized AI
✅ Use your Translation Memory and project data to guide AI output
✅ Avoid costly re-training and fine-tuning
✅ Evaluate AI quality with objective metrics
✅ Build a strong internal case for AI adoption

Alesia Nikalaichyk

Product Manager on AIML at Lokalise

Alesia is a Product Manager at Lokalise, with 5+ years of localization expertise. Thanks to previously having worked as a Customer Success Manager, she has a deep understanding of Enterprise customer needs, which she now applies to shaping impactful product strategies and results.

Adam Šoltys

Senior Lead Product Manager on AIML at Lokalise

Adam is a Senior Product Lead at Lokalise’s AI and Machine Learning team. He has a strong background in launching new disruptive products in startups and scale-ups with global reach. He joined Lokalise to help build up innovative AI-based solutions that are aiming to radically transform the localization industry and enable customers to expand their business to new markets with minimal total costs while keeping the quality high.