FREE EXPERT COURSE

Building AI LQA with ContentQuo:
Intro to AI-Powered LQA

AI, Humans and LQA

Learn how to combine human depth with AI scalability to build structured evaluation workflows, validate LLMs, and drive data-driven quality improvements at scale.

Scaling Translation Quality in the AI Era

Content volumes are skyrocketing, costs are under pressure, and expectations for consistent multilingual quality are higher than ever. Traditional QA checks only catch surface errors, what’s needed is structured Linguistic Quality Assurance (LQA) that drives continuous improvement.

Enter AI LQA with ContentQuo: a practical way to combine the rigor of human LQA with the scalability of AI. This introductory course will show you the fundamentals of modern LQA and how AI can make quality evaluation faster, fairer, and more effective.

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  • This webinar includes:
  •    Expert panellist: Gleb Grabovsky, Key Account & Customer Success Manager at ContentQuo
  •  Lifetime access to the course and extra contents
  •  In English
  •  TranslaStars Certificate of Completion
  •  Course duration: 2 h (approximately)
  •  When: 24 October (18.00 CET)
Sponsored and created in collaboration with:

AI POWERED LQA

Building AI LQA with ContentQuo: Intro to AI-Powered LQA

Session 1 - From LQA Basics to LQA AI Automation
SESSION 1

LQA: From the Basics
to LQA AI Automation

Session 1

Part 1 – Why LQA Matters 
1.1 From QA checks to structured evaluation: the evolution of translation quality
1.2 MTQE vs LQA vs AI LQA: similarities, differences, and use cases
1.3 Why AI needs a human baseline to stay reliable

Part 2 – Smart Automation with ContentQuo Plan
2.1 Eliminating the pain of spreadsheets with automated workflows
2.2 Live demo: project setup, TMS integration, evaluation rules

Part 3 – AI Features in Action
3.1 AI Reviewer: scaling evaluation while keeping human oversight
3.2 CQ Test: benchmarking AI reviewers against human baselines
3.3 Live showcase: AI flags vs human validation

Part 4 – Wrap-Up & Discussion
4.1 Key takeaways
4.2 Open Q&A with participants

Course Description

By the end of this free course, learners will be able to:
  • Distinguish between QA, MTQE, Human LQA, and AI LQA, and identify when to use each
  • Set up automated LQA workflows with ContentQuo Plan to replace spreadsheets
  • Use AI Reviewer to scale evaluations while keeping humans in the loop
  • Benchmark AI Reviewer performance against human baselines with CQ Test
  • Apply LQA results to build feedback loops that improve translators, vendors, and MT engines
  • Understand how AI-enhanced LQA can future-proof quality management programs

Who is this course for?

This course is ideal for:
  • Localization project and operations managers
  • Vendor and quality managers
  • Buyers of translation services, seeking scalable Quality Monitoring
  • Anyone interested in LQA (Linguistic Quality Assurance) & LQE (Linguistic Quality Evaluation)
  • Linguists curious about AI in evaluation
  • Anyone who wants to understand how AI is reshaping translation quality assurance

Resources

Linguistic skills and previous knowledge:
  • A good English level is required
  • Familiarity with localization processes (TMS, translation pipelines)

IT resources:
  • A computer with speakers as well as a stable internet connection are necessary

Availability:
  • Life access to the recording of the course and extra contents
Meet

Gleb Grabovsky

Former freelance translator and interpreter turned quality management specialist, Gleb helps global companies establish effective LQA/LQE processes and adopt AI-assisted evaluation.
With 10+ years in localization, he brings hands-on expertise in translation workflows, TMS integrations, and vendor alignment.
Gleb regularly shares insights on translation quality, AI in localization, and evaluation best practices on LinkedIn and at industry events.
Gleb Grabovsky - Course host
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