AI where it's safe. Human where it counts.
Every language vendor now claims to be AI-powered. The question worth asking is how. We use language technology where volume and speed are safe, and expert human review where accuracy, reputation or regulation are on the line, with the routing between them decided by content risk rather than by cost.
Using AI is not the same as orchestrating it.
Running everything through one engine at one throughput is not a strategy, it is a cost decision dressed up as innovation. Engine quality varies enormously by language, content type and domain, and treating them all the same is how brands end up with fluent nonsense in their smaller markets.
We classify your content at intake, route it by risk, apply the best-performing engine for that specific language, score every segment, and escalate what falls below threshold. The automation decides the route. People decide the meaning.
High-volume content: fast, consistent, controlled.
For product catalogues, knowledge-base content, app strings, patch notes and structured product data, you get efficient technology-enabled workflows. Automated checks control terminology, formatting, length and consistency. Expert reviewers intervene where risk, ambiguity or quality signals require it.
High-impact content: handled by market experts.
For customer-facing campaigns, regulated copy, brand messaging, product instructions and legal content, the work receives more human attention. In-market specialists work from an informed brief, approved language assets and a clear understanding of your commercial objective.
AI translation and quality assurance, in detail.
- Machine translation deployment
- Engine selection and configuration per language, rather than one engine across your whole footprint, reviewed as engine performance changes.
- AI post-editing (MTPE)
- Professional linguists editing machine output against your brief and termbase, at light or full depth depending on content risk.
- Risk-tiered routing
- Content classified by type, market and risk at intake, with rules that send high-volume work to throughput and high-stakes work to people.
- Custom engine training
- Engines tailored on your accumulated corpus, so quality improves as your translation memory grows.
- Automated quality estimation
- Segment-level scoring that decides what passes automatically and what escalates, calibrated on your content rather than set by default.
- Human linguistic review
- In-market reviewers for anything carrying brand, legal or compliance exposure, working to a defined error taxonomy.
AI translation and quality assurance, explained.
- Do you use machine translation or human translators?
- Both, routed by risk. Your high-volume, low-risk content moves on machine translation with automated checks. Anything with brand, legal, compliance or reputational exposure goes to in-market human specialists. The routing decides; you do not have to.
- Is AI translation good enough for my content?
- For some of it, yes, and for some of it, no. That distinction is the whole point. We deploy language technology where speed and volume are safe, and expert human review where accuracy, reputation or regulation are on the line.
- How do you keep AI output on-brand?
- Custom-trained engines work from your accumulated translation memory and a controlled termbase, and automated quality estimation scores every segment. Anything below threshold escalates to a human before it ships.
- What is MTPE?
- Machine translation post-editing: professional linguists editing machine output against your brief and terminology, at light or full depth depending on the content's risk.
Find out which of your content is safe for AI, and which is not.
Request a content audit and we will map your content by risk, and show you where automation saves money and where it would cost you.