Continuous Localization Meets AI:
Designing Workflows for the Future
The Transformation in Localization
Modern localization has shifted from large, infrequent projects to continuous streams of small updates requiring instant translation.
This transformation has broken traditional project management approaches, creating both challenges and unprecedented opportunities for AI integration.
This transformation has broken traditional project management approaches, creating both challenges and unprecedented opportunities for AI integration.
Continuous Localization Workflows
Why You Should Enroll
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If you have any questions, please visit the FAQ section (for courses or subscription plans) or get in touch with us.
Learn to build AI-ready localization pipelines that scale efficiently
while adapting to rapidly evolving technology capabilities
SESSION 1 - Foundations
From Traditional Project Management to AI-Ready Continuous Localization
Session 1
1. Modern shift: Projects → continuous updates
2. Why traditional PM breaks in flow-based localization
3.3 Real-world examples
2. Why traditional PM breaks in flow-based localization
3. Continuous localization workflows
3.1 Characteristics
3.2 Project-based vs. flow-based models
4. AI integration opportunities
4.1 Content ingestion & routing
4.2 MT/LLM translation & post-editing
4.3 Quality prediction & auto-QA
4.4 File handling, versioning, error detection
5. Workflow automation principles
5.1 Scalability, modularity, flexibility
5.2 Error-tolerant design
5.3 Balancing speed, cost, quality
5.4 Common pitfalls
6. Hands-on: Map workflows and AI touchpoints
SESSION 2 - AI-POWERED PIPELINES
Designing AI-Powered Localization Pipelines
Session 2
1. No-touch workflows & human oversight
1.1 Fully automated vs. hybrid models
1.2 Human-in-the-loop principles
2. AI routing logic & quality gates
2.1 Assignment logic: AI vs. human
2.2 Risk-based routing (content, importance, deadline)
2.3 Automated QA + anomaly detection
2.4 Error-handling strategies
3. Future-proofing strategies
3.1 Adaptability to evolving AI tools
3.2 Avoiding vendor lock-in
3.3 Designing for scale
4. Hands-on:
4.1 Build full pipeline architecture (ingestion → AI → QA → delivery)
4.2 Apply routing & automation rules
4.3 Peer review and iteration
Course Description
Who is this course for?
Resources
Meet
István Lengyel
István Lengyel is the founder of BeLazy, the translation industry's project centralization platform.
BeLazy works with small and large companies - both enterprises and translation providers - intending to remove the unnecessary hassle of localization project management. Previously, he co-founded memoQ, and held various positions in the company.
He tried most roles in the industry: he worked as a translator, as Director for Customer Success for On Global Language Marketing, and as a consultant for Nimdzi Insights.
He's an economist, a translator and interpreter, and holds a PhD in translation studies from the ELTE University of Budapest.
BeLazy works with small and large companies - both enterprises and translation providers - intending to remove the unnecessary hassle of localization project management. Previously, he co-founded memoQ, and held various positions in the company.
He tried most roles in the industry: he worked as a translator, as Director for Customer Success for On Global Language Marketing, and as a consultant for Nimdzi Insights.
He's an economist, a translator and interpreter, and holds a PhD in translation studies from the ELTE University of Budapest.
István Lengyel - Course host
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