
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.
I'm Sasho, Engineering Manager at Lokalise where I manage the AI team, which mostly means I get to argue about evaluation metrics for a living and occasionally ship something that translates the internet slightly better.
Before this I spent a bunch of years in clinical NLP, including a PhD at Sussex teaching computers to read doctors' notes — which back then took years of research and is now roughly a weekend and a good prompt. Somewhere in there I picked up a handful of papers and patents, mostly on things like evaluating medical note generation and measuring how similar two sentences really are.
These days at Lokalise I help build the AI behind translation quality — RAG-based translation, evaluation pipelines, the unglamorous plumbing that keeps the AI-harness in check. It's going fine, mostly.
I studied Computational Linguistics at the University of Tübingen before my PhD at the University of Sussex, and I've spent 14 years across NLP research and AI engineering, with stops in academia, clinical AI, and now localization.
You can also find me at sasho.io and on Google Scholar.

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.

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

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