EXPERT COURSE

Building AI-Driven Localization Pipelines

Past the Hype, Into the Pipeline

Machine translation is not new. What is new is everything around it. Retrieval, agentic orchestration, premium large language models, automated quality estimation, and zero-touch publishing have turned translation from a single step into a multi-component pipeline. According to CSA Research, the language services market grew to USD 76.8 billion in 2024, and the Nimdzi 100 confirms that AI is reshaping the entire stack - from CAT tools to LSP service portfolios.

Two Hours, One Mental Model

Most failed AI projects in localization share the same root cause: a great model bolted onto a brittle process. Slator reports that 2024 was the year the industry moved from MT plus post-editing to AI-assisted, end-to-end production - but only the teams that treated AI as a pipeline problem captured the upside.

This session gives you a mental model in one focused sitting. It is built around four pillars that consistently separate working deployments from expensive prototypes:

  1. Deep customization - domain, customer, product, and brand voice
  2. Multi-system integration - how the pieces wire together
  3. Processes over technology - automation, retrieval, and orchestration
  4. Human in control - AI-assisted quality and governance

You leave with a one-page framework you can use the next morning to assess any AI proposal landing on your desk - whether it comes from a vendor, an internal team, or your own backlog.

This is a build-as-you-go session. Bring a real workflow you are running or overseeing. You will leave with a practical framework, not just theory.

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  • This webinar includes:
  •    Expert panellist: Jourik Ciesielski, Chief Technology Officer at ELAN Languages 
  •      Live activities
  •  Lifetime access to the course and extra contents
  •  In English
  •  Certificate of completion
  •  Course duration: 2 h (approximately)
  •  When: 6 October 2026 at 16.00 CET
Course created in collaboration with:

Free for Crowdin Enterprise Users

CREATING AI-Driven Localization Pipelines

A 2-hour session on designing intelligent AI-driven localization pipelines:
context, guardrails, quality estimation, automated post-editing, and agentic automation

Session Breakdown

One session, two hours. Structured as four short blocks of roughly 25 minutes each, with a Q&A wrap at the end.

1 SESSION

Building AI-Driven Localization Pipelines

120 minutes
  • ▸ From MT plus post-editing to AI-driven pipelines: what actually changed (15 min)
  • ▸ Pillar 1 - Deep customization: domain, customer, product, and brand voice (20 min)
  • ▸ Pillar 2 - Multi-system integration: how the pieces wire together (25 min)
  • ▸ Pillar 3 - Processes over technology: automation, retrieval, and orchestration (25 min)
  • ▸ Pillar 4 - Human in control: AI-assisted quality, governance, and the decisions that stay human (25 min)
  • ▸ Live Q&A and pipeline blueprint takeaway (10 min)
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Course Description

What Is This Course About

Machine translation is not new in localization, but fully AI-driven pipelines that go beyond automated translation are still uncharted territory for many buyers and service providers. This session gives participants a structured way to design and assess such pipelines in a single focused sitting.

Across two hours, students unpack the building blocks of an AI-driven localization pipeline: process automation, context retrieval, premium machine translation and LLM-based translation, and AI-assisted quality management. The session is built around four guiding pillars that consistently separate working deployments from failed prototypes: deep customization, multi-system integration, processes over technology, and human control at every critical step.

Course Format

One live session, two hours. Structured as four short blocks of roughly 25 minutes each, with a Q&A wrap at the end. Each block ties back to one of the four pillars. Delivered live in a single focused session.

Key insight: Most failed AI projects in localization share the same root cause - a great model bolted onto a brittle process. The teams that treat AI as a pipeline problem, not a model problem, capture the upside.

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Who Is This For

This 2-hour intensive session is designed for professionals who want to move beyond AI hype and build practical, working localization pipelines.

🎯

Localization PMs & Operations Managers

Professionals overseeing translation workflows who need a structured framework to evaluate and integrate AI solutions without disrupting ongoing operations.

💻

Language Technology Specialists

Engineers and architects evaluating MT, LLM, and automation tools who want to understand how these components integrate into end-to-end pipelines.

🏢

LSP Owners & Decision Makers

Agency founders and senior leaders who need to decide which AI investments will yield real ROI and which are distractions.

🔍

Enterprise Buyers of Language Services

In-house managers procuring localization who want to ask the right questions and evaluate vendor AI proposals with confidence.

🎓

Freelance Linguists & Translators

Independent professionals who want to understand the AI landscape and position themselves strategically as the industry evolves toward pipeline-based workflows.

📊

AI & Innovation Consultants

Professionals advising language businesses who need a clear, practical framework to structure their AI recommendations and avoid the hype trap.

Prerequisite: Participants should have basic familiarity with translation workflows and CAT tools. No technical AI or coding experience required - this session focuses on concepts, architecture, and decision frameworks, not implementation details.

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What You Will Learn

✓

Design an AI-driven localization pipeline that goes beyond MT plus post-editing

✓

Recognize where retrieval-augmented generation (RAG) belongs in a translation flow, and where it does not

✓

Place automated quality estimation (MTQE) and AI checks without displacing human review where it matters

✓

Identify the integration points between TMS, MT, LLM gateways, terminology systems, and review tools

✓

Spot the governance gaps that turn a promising AI deployment into a risk

✓

Apply a one-page assessment framework to evaluate any AI proposal - vendor, internal, or personal

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Requirements & Setup

Equipment

  • ▸ Computer with a stable internet connection
  • ▸ Webcam and microphone (recommended for Q&A)
  • ▸ Modern web browser (Chrome, Firefox, or Edge)

Preparation

  • ▸ Familiarity with basic translation workflows and CAT tools
  • ▸ Bring a real workflow or project you are running or overseeing
  • ▸ No coding or AI technical experience required

Optional Reading

  • ▸ CSA Research - Language Services Market 2024
  • ▸ Slator - 2024 Language Industry Market Report
  • ▸ Nimdzi Insights - Nimdzi 100: Top LSPs

Pro tip: Come with a real workflow challenge you are facing. The session is designed so you leave with a practical framework you can apply immediately, not just theoretical knowledge.

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Jourik Ciesielski

| Chief Technology Officer at ELAN Languages
| Co-Founder C-Jay International
| Nimdzi Consultant & Researcher
About JOURIK
Jourik Ciesielski is a seasoned professional with extensive experience in the localization and technology sector. He is currently CTO at Elan Languages where he leads the company efforts in implementing AI and innovative solutions.
He is also the Co-founder of C-Jay International offering consulting and localization engineering services for buyers and LSPs.
His expertise spans university lecturing, consulting, localization engineering and strategic planning.
Jourik is known for his innovative approach to problem-solving and his ability to drive technological solutions.