EXPERT COURSE

AI Quality Specialist

Beyond Post-Editing

AI is creating new roles for linguists, and the smartest ones are already moving

AI is reshaping translation work. Rates are under pressure, turnaround times are shrinking, and it can feel like the industry is moving in one direction: cheaper and faster, with less room for linguists. But that is not the full picture. A growing number of translators and post-editors are earning more than before, not less. They have figured out something that most of the industry has not caught up with yet: AI creates new quality problems that only skilled linguists can solve, and the people who understand those problems are becoming the most valuable part of the workflow.

This 6-hour live course takes you from understanding why AI quality matters to building the practical skills that set you apart. Across three sessions, you will learn to spot and classify AI error patterns using a structured taxonomy, rewrite glossaries and style guides so AI actually follows them, write quality reports that drive upstream improvements, and position yourself as a quality specialist rather than a post-editor.

The demand for AI language quality skills is real, and growing

According to Nimdzi Insights, the language services market exceeds $68 billion, and AI-enabled workflows have become core to multilingual content delivery. But the quality gap is widening: companies adopting AI translation are finding that the output requires a new kind of linguistic oversight that most traditional workflows were not designed for. The linguists who understand this gap are stepping into quality specialist roles that did not exist two years ago.


This course is grounded in real workflows and real client needs. It is not a motivational talk about embracing change. It is a practical, hands-on programme about what is working right now and how to position yourself on the right side of it.

Complete all three sessions to earn the AI Quality Specialist Certificate.


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  • This course includes:
  • Expert coach:
    Nicola Calabrese, Founder at Undertow, AI Language Operations Consultant & Trainer. Host of The Multilingual Content Podcast
  •  Interactive activities
  •  Life access to contents 
  •  Downloadable course program
  •  In English
  •  Completion certificate
  •  Money back guarantee
  • Become an AI quality specialist. Learn the AI language skills to position yourself beyond post-editing
  • When: 25-27 May 2026 (18.00 CET)
  • Duration: 6 h approx.

Course and Instructor Description

You will learn what companies running AI-assisted localization actually need from linguists (it is not faster post-editing), how to spot and classify AI errors using a structured taxonomy, how to write quality reports that drive real improvements, and how to rewrite glossaries and style guides so AI actually follows them.


The course covers:

● Why AI output fails in predictable patterns and why understanding them matters more than fixing individual errors

● What clients and employers actually need from linguists in AI-assisted workflows

● The four-pillar framework for AI language quality oversight: high-impact review, linguistic assets, error analysis, and stakeholder reporting

● How to rewrite glossaries and style guides for machine use and see the AI output improve in real time

● How to position yourself as a quality specialist, not just a post-editor, and frame your value to clients and employers


The course is taught by Nicola Calabrese, founder of Undertow and an AI language operations consultant who works with B2B SaaS companies on building structured AI-assisted localization programmes. Nicola brings real-world frameworks from enterprise quality programmes into a format designed for freelancers and in-house linguists who want to move into quality leadership roles.


Who is this course for?

This course is designed for language professionals who want to understand how AI is changing quality expectations and learn the practical skills to thrive in this new landscape:
● Freelance translators and post-editors who work with MT output and want to move beyond basic post-editing into higher-value quality roles, learning to spot error patterns, write structured reports, and shape the instructions that improve AI output at the source
● In-house linguists and reviewers at LSPs or enterprise localization teams who see AI being adopted around them and want to understand what structured AI quality oversight looks like, so they can lead it rather than be sidelined by it
● Localization project managers who need to understand what AI quality oversight actually involves so they can build it into their workflows, set realistic expectations, and work effectively with quality specialists
● Translation students and early-career linguists who want to enter the profession with a clear-eyed view of where the opportunities are and the practical toolkit to pursue them from day one
● Anyone curious about the intersection of AI and language quality who wants practical insight rather than hype or fear 

Delivered in English. No technical background required. The course is designed for linguists, not engineers.


Session 1: The Quality Advantage
Session 2: Building Your AI Quality Toolkit
Session 3: Positioning Yourself as a Quality Specialist
Session 1

The Quality Advantage

Session 1. The Quality Advantage

1.1 The AI quality landscape: how AI is changing translation work, rates, and expectations
1.2 Real examples of AI output failures: tone drift, over-literal translations, cultural misses
1.3 The four-pillar framework for AI language quality roles
1.4 The control lever: why quality gains come from changing instructions, not models
1.5 Quick demo: how one rewritten glossary entry changes AI output
1.6 What this means for you: positioning moves you can start using this week
Session 2

Building Your
AI Quality Toolkit

Session 2. Building Your AI Quality Toolkit

2.1 The AI error taxonomy: tone/register drift, over-literal output, gender/number errors, context blindness, terminology misuse, hallucinated content
2.2 Live error-spotting exercise: classifying real AI outputs using the taxonomy
2.3 From errors to reports: structured quality reporting that drives upstream change
2.4 Reporting in practice: how categorised errors lead to prompt adjustments, glossary rewrites, and workflow changes
SESSION 3

Positioning Yourself
as a Quality Specialist

Session 3. Positioning Yourself as a Quality Specialist

3.1 Why most glossaries and style guides fail with AI: the gap between human-readable and machine-readable
3.2 Hands-on rewriting workshop: restructuring glossary entries for machine use with before-and-after AI output
3.3 Participant exercise: rewrite a glossary entry from your own domain
3.4 Prompt-shaping: how linguists write AI instructions that improve output quality
3.5 Positioning yourself: framing quality work as a service, rate strategies, what to put on your profile
3.6 Action plan and Q&A: commitments for this week, this month, and beyond

Nicola Calabrese

| Founder at Undertow
| AI Language Operations Consultant & Trainer
| Host of The Multilingual Content Podcast
About NICOLA
Nicola Calabrese is the founder of Undertow, a boutique localization consultancy that helps B2B SaaS companies scale internationally with AI-powered localization workflows. With 15 years of experience in the language industry, Nicola has worked across the full spectrum of localization, from translation and transcreation to software localization, SEO localization, and localization engineering.

Today, Nicola specialises in the intersection of AI and language quality. He consults with enterprise teams on building structured quality oversight into their AI-assisted localization programs, helping companies move beyond basic post-editing toward scalable, linguist-led quality management. His training programs have focused on AI error taxonomy, machine-readable glossary design, and the evolving role of linguists in AI-enabled workflows.

Nicola is also the host of The Multilingual Content Podcast, powered by Undertow, where he interviews localization leaders, tech leaders, and language technology experts about the real challenges of building multilingual products.

He is an instructor at TranslaStars'Localization Management Academy, where he delivers sessions on the economics of the localization industry, transcreation management, and vendor management, and at the TranslaStars' Master in AI and Innovation, where he delivers sessions on AI agents and workflow automations for language professionals.

When he is not building localization programs or training linguists, Nicola is working on making language technology more accessible to the professionals who need it most, because he believes the future of localization belongs to linguists who understand both language and the systems around it.