Latest articles about translation technology
Configuration reached the database by two paths that never met. Half our templates drifted — in both directions at once — and nothing could see it. A case study.
A TM the machine writes to decays — one bad entry comes back forever as an exact match. Why we let ours start empty, and what that cost.
On a declaration, a wrong digit costs more than any style error — and no quality metric sees the difference. How to check numeric integrity, and how we got it wrong.
A second LLM pass over everything costs far more than it returns. Quality estimation picks the segments worth editing — here is how to set the threshold.
GEMBA V2, MetricX-25 and a two-tier setup that runs on paid APIs. Plus the failure that makes quality estimation look like it works when it doesn't.
A translator-reviewer-editor loop costs about five times the tokens; iterative agents fifteen. Here is what the research says you get back, and what we did with ours.
Two ways to get fields out of a scan. We run both in one product. Here is what we measured, where each wins, and the probe that quietly lies to you.
Registry codes, transliterated names, form serials, seal text, back-side marks, signature certificates. A checklist for certified document translation, with what breaks.
An official document is a form with numbered boxes. Translating it as prose loses the boxes. Here is what template-based document translation does instead.
LLM field extraction fails on format, not comprehension: invented key names, almost-JSON, missing fields. Three layers of defence, and when each one earns its cost.
One explicit instruction in the prompt — use the glossary term exactly, keep it consistent across the document — buys 12 percentage points of terminology accuracy.
A reasoning model spends the same token budget on thinking as on the answer. At 4096 tokens it thinks itself into silence — and your fallback hides it.
Explore how agentic AI transforms translation pipelines with specialized agents for translation, post-editing, terminology, and QA scoring.
Learn how AI transforms terminology management with smart extraction, disambiguation, and living glossaries. Step-by-step guide to building an AI-powered workflow.
A deep dive into Chinese localization quality challenges — from Simplified vs Traditional to internet slang, censorship, and why Qwen-MT still needs human QA.
Explore how consensus-based AI translation uses multiple engines to reduce errors by 22%. Architecture, cost analysis, and practical implementation guide included.
The revised ISO 18587 now covers LLM-generated translations. Learn about updated requirements, compliance strategies, and how TQA platforms help.
Automated metrics miss LLM hallucinations. Learn how MQM-based evaluation catches what COMET and MetricX cannot, with practical scoring examples.
Compare GPT-5, Claude 4, Qwen-MT, and DeepSeek-V3 for translation. Learn when to use each model and how hybrid routing maximizes quality and cuts costs.
Discover why translating keywords fails for international SEO. Learn quality-first multilingual SEO with transcreation, hreflang, GEO/AEO, and TQA-driven strategies.
Learn how to build a translation quality culture with continuous monitoring, MQM metrics, and vendor benchmarks. A practical 3-month roadmap for quality transformation.
How the translator profession has evolved in 2026. From linguist to AI quality supervisor — skills, salaries, career paths, and why quality assessment is your career anchor.
Understand the differences between translation memory and glossary. Learn when to use each, how they work together, and best practices for combining TM and termbase.
Learn how to create and manage translation glossaries. Discover terminology management best practices, tools, and how glossaries improve translation quality.
Learn how translation memory works, its benefits, best practices, and how to implement TM in your localization workflow. Includes real examples and ROI data.
A practical guide to deciding between building custom AI translation quality tools or buying existing solutions. Cost analysis, technical requirements, and decision framework.
How AI is changing the approach to translation quality assessment and which tools to use for best results
Learn how companies achieve 70% cost reduction in translation quality assurance through AI automation. Practical guide with ROI calculations and implementation steps.
Explore how Large Language Models (LLMs) are revolutionizing translation quality assessment. Learn about GPT-4, Claude, and other LLMs for LQA with practical implementation guidance.
Learn how to classify and categorize translation errors using MQM and ISO 5060 standards. Comprehensive guide to error types, severity levels, and quality scoring.
Compare AI LQA and MTQE for translation quality assessment. Learn when to use each approach, their strengths and limitations, and how to build a hybrid workflow.
Learn about ISO 5060:2024, the first international standard for evaluating translation quality. Covers MQM integration, implementation steps, and compliance requirements.
Learn what Linguistic Quality Assurance (LQA) is, how it works, and why it's essential for professional translation. Includes LQA process, metrics, and AI-powered LQA tools.
Comprehensive comparison of the best LLMs for translation in 2025. We analyze Claude 3.5, GPT-4, DeepL, and other AI translation tools to help you choose the right one.
Learn everything about Multidimensional Quality Metrics (MQM) - the ISO-backed framework for measuring translation quality. Understand error categories, scoring models, and how to implement MQM in your workflow.
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