The real cost of building in-house AI translation
The model API is the cheap part. This is what it actually costs to automate AI translation, give it your company's context, learn to trust the output, and keep the system running.
Category: AI Translation
This category explores the technological frontier of language. We dive deep into how to integrate the latest LLMs (GPT, Claude) into your workflow, and the critical need for AI quality assurance and governance. Content is designed for Localization Managers and Engineers who need to leverage AI for scale while maintaining high quality, consistency, and data security. Learn to manage translation memory (TM) alongside AI output to maximize speed and cost savings.
How to get data-backed proof that AI translation works — on your own content
Here's a stat that should make every localization leader uncomfortable: 57% of localization teams say their number one barrier to using AI more is that they don't trust the quality. Not that the quality is bad. That they don't trust it. The distinction matters. AI translation has improved dramatically. Models are better, context-aware systems like
AI translation quality evaluation: Looking beyond human review
The meeting is going well. Translation costs are down, turnaround times are shorter, and AI is taking on more of the work. Then your VP asks a question: “How do you know the quality of AI translations is good enough?” The expensive part usually starts once you try to answer that question inside a system you built: the real cost of buildi
MCP for localization: How to connect AI agents to your translation workflow
You're in Cursor, working on a new feature, and you need to add a localization key. That means leaving your IDE, opening your TMS, navigating to the right project, making the change, and coming back. Then, doing this all over again the next time you need to check untranslated strings, create a task, or touch anything localization-related. Model Context Protocol (MCP) removes this context-switching loop entirely. Instead of bouncing between tools, your AI coding assistant can inter
7 AI tools your localization team needs to master in the GenAI era
GenAI isn’t just changing how translations are produced. It’s reshaping the entire localization workflow. Most teams didn’t build their workflows for this shift. They’re still relying on a mix of spreadsheets, standalone MT engines, and manual file handoffs. It’s familiar, and for a while, it worked. But it was never designed for the scale teams are dealing with now.
AI translation with glossary support: Deterministic terminology for LLMs
LLMs are fluent in generating outputs, but they're not faithful to your brand. When a general-purpose LLM translates your product UI into 15 languages, it doesn't know which terms are trademarked, which phrases have legal restrictions, or which features are deprecated. It makes a statistical guess. At scale, this guesswork can lead to major inconsistencies, compliance risks, and a post-editing overload. The challenge: AI models’ probabilistic outputs are not ideal
The 5 best tools for AI translation post-editing (MTPE)
AI translation post-editing tools promise 40-60% cost savings. In practice, you only get those savings when AI output is controlled. This means your terminology is enforced, risky segments are flagged before they go live, and linguists only touch what truly needs human attention. That’s why the best MTPE tools today aren’t standalone CAT tools or MT engines. They’re actually translation management systems (TMS) that orchestrate AI, terminology, and quality assurance in one place.
AI vs human translation cost: How to cut localization costs by up to 97%
As of 2026, the Total Cost of Ownership (TCO) for enterprise localization has shifted from a per-word human model (~$0.20/word) to an orchestrated AI model (~$0.002/word). This 100x efficiency is driven by AI Orchestration, which automates context retrieval (RAG) and eliminates manual project management overhead. This shift comes from AI orchestration. These systems combine large language models with retrieval-augmented context, terminology databases, translation memory, and automated q
Stop wasting time with manual localization tasks.
Launch global products days from now.
Case studies
Behind the scenes of localization with one of Europe’s leading digital health providers
Read more Case studiesSupport
Company
Follow
Localization workflow for your web and mobile apps, games and digital content.
©2017-2026
All Rights Reserved.