EXPERT COURSE

AI Systems in Localization

From isolated AI tools to connected, scalable localization systems

Two Companies, the Same AI Model, Opposite Results

Two companies adopt the same AI translation model. For one, the output needs heavy rework every time: slow, costly, frustrating. For the other, it needs only a light touch: fast, consistent, scalable from day one. The model is identical. The difference is everything that happens before the model runs. That is the part most teams never design, and it is where this course lives.

Most Teams Use 10% of What AI Can Do

According to Nimdzi Insights, the language services market exceeds $68 billion, and AI is now woven through almost all of it. Yet most teams automate translation and stop there, using about 10 percent of what AI can actually do. The other 90 percent — content routing, context generation, terminology, source review and quality at scale — sits unused. Unlocking it is not a tooling problem, it is a system-design problem, and that is a learnable skill.

A Hands-On Session Where You Build Your Own System

This is a practical, build-as-you-go course. Across two sessions you will map your current localization stack, redesign a real workflow around AI, and assemble your first complete localization system: one you can take back and start using the following week.


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  • This webinar includes:
  •     Expert tutor: Nicola Calabrese, Founder at Undertow. AI Language Operations Consultant and Trainer
  •  Lifetime access to the course and extra contents
  •  In English
  •  TranslaStars Certificate of Completion
  •  Course duration: 4 h (approximately)
  •  When: 14-15 October 2026 (18.00 CET)

AI SYSTEMS IN LOCALIZATION

Build an AI localization system, not a pile of tools. Map your stack,
design AI workflows, and turn linguists into language intelligence leads

Session Breakdown

01 Session

From Outputs to Decisions

2 hours
  • ▸ What bad localization really costs, and the localization paradox of "replace it all" vs "resist it all"
  • ▸ Localization as system design: why it is a workflow problem, not a translation problem
  • ▸ The four-pillar framework: strategy, people, technology and process, in the right order
  • ▸ Loop, not pipeline: the four components of an AI localization system and how they connect
  • ▸ The AI silos problem, and how to centralize by design rather than by prohibition
  • ▸ The 10 percent problem: putting AI to work beyond translation
  • ▸ Exercise: build your own system map (stack, connections, gaps, shadow tools, feedback loop)
02 Session

Designing the Workflow

2 hours
  • ▸ Chaining the workflow: source assets, AI translation, quality review and localized output
  • ▸ The three control levers: glossaries, style guide and prompts
  • ▸ Machine-ready, not human-ready: preparing content and assets so AI performs
  • ▸ Engine vs fuel: why your assets matter more than which model you choose
  • ▸ What AI gets wrong: tone, culture and humour, source-content problems and low-resource languages
  • ▸ The 80/20 rule and the shift from post-editor to language intelligence lead
  • ▸ Human intervention layers: where humans add the most value, upstream over downstream
  • ▸ Exercises: redesign one workflow, then assemble your complete system

Course Description

What Is This Course About?

Most localization teams do not have a translation problem — they have a workflow problem. Quality rarely breaks at the moment of translation; it breaks in the system around it: in how content enters the pipeline, how context is passed, and how feedback loops back. Across two hands-on sessions, you will learn to treat localization as system design, then build your own: a system map, a redesigned AI workflow, and the control levers that keep quality high as you scale.

What You Will Build

By the end of this course, you will have built a complete AI localization system from scratch. You will create a system map of your current stack, redesign a real workflow around AI-augmented processes, and establish the control levers — glossaries, style guides and prompts — that keep quality consistent as you scale.

Course Format

This is a hands-on, live course delivered as two 2-hour sessions. Each session combines live demonstrations with interactive exercises where you build system maps and redesign workflows in real time. The course is delivered in English, and no technical background is required.

Why AI Systems?

Organizations that treat AI as a system-design problem instead of a tool-selection problem achieve better speed, consistency, and quality at scale. AI amplifies your foundations — good or bad — which means the real work is designing the system around the model, not choosing the model itself. This course gives you the system-first mental model that separates working deployments from expensive experiments.

Key Insight: The difference between a failed AI deployment and a successful one is rarely the model. It is the system around it. This course gives you the system-first mental model that separates working deployments from expensive prototypes.

Who Is This For

🏢

Localization Managers & Operations Leads

Building or fixing an AI-assisted program who want a coherent system, not a pile of disconnected tools.

🎯

Localization Program Owners & Strategists

Who need to decide where AI leads, where humans intervene, and how the whole pipeline connects.

🏗️

LSP Owners & Team Leads

Moving from manual, tool-by-tool processes to a centralized, scalable system.

📋

In-House Localization & Content Leads

Tired of shadow tools, terminology drift and constant rework, who want one workflow that holds together.

🌱

Linguists & Post-Editors

Growing into strategy who want to work upstream on the system itself, not just fix AI output downstream.

🔗

Technology Managers

Deciding where AI fits in their localization stack and how to integrate it without fragmentation.

Note: Delivered in English across two 2-hour sessions. No technical background is required. Both sessions are interactive through the live chat, and a system-map template plus a workflow-redesign worksheet are provided.

What You Will Learn

✓

Map any localization operation against the four pillars of an AI-powered system: strategy, people, technology and process, built in the right order

✓

Build your own system map: identify your current stack, gaps, and shadow tools

✓

Recognize why AI amplifies your foundations — good or bad — and what that means for where you start

✓

Apply the three control levers: glossaries, style guides and prompts, and why your assets beat the model

✓

Design machine-ready content and anticipate predictable AI failure modes

✓

Build reusable workflow lanes with a feedback loop that fixes errors at the source

✓

Create a complete localization system you can take back and start using the following week

Requirements & Setup

Computer Requirements

Any modern computer with speakers and a stable internet connection will work. No software installation is required beyond a web browser and a Zoom account.

What You Will Need

  • ▸ A computer with speakers and a stable internet connection
  • ▸ A Zoom account (free tier is sufficient)
  • ▸ A text editor or a notebook and pen

Preparation (Optional but Recommended)

  • ▸ A localization workflow you currently run or oversee, sketched out at a high level
  • ▸ Real examples are used in the exercises, and anonymized or open data is perfect

Prerequisite Knowledge

This course assumes:

  • ▸ Basic understanding of localization workflows and how content moves through a team
  • ▸ Familiarity with localization concepts such as translation, quality review, and terminology management
  • ▸ No prior experience with AI tools or technical background is required

Tip: A system-map template and a workflow-redesign worksheet are provided to complete during the sessions. You can attend both sessions live from wherever you are, and lifetime access to the recordings and session materials is included with your place.

Nicola Calabrese

CEO at Undertow
About NICOLA
As the founder of Undertow, Nicola helps companies expand internationally by developing an effective localization program to translate, adapt, and create multilingual content so they can personalize their global user experience and increase their revenue and ROI.
He has incorporated AI from the start to help in translation processes, content creation and to provide technical and localization engineering solutions.