AI where it earns its place, and nowhere else.
Every language vendor now claims to be AI-powered. The question worth asking is not whether they use AI but how: whether it is a shortcut to cheaper output, or an orchestrated system with routing logic, terminology built in, and a quality layer that feeds back into the work.
The difference between using AI and 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, by content type and by domain, and treating them all the same is how brands end up with fluent nonsense in their smaller markets.
We classify 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.
AI & Machine Translation 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 post-editing depth depending on the content risk.
- Risk-tiered AI routing
- Content classified by type, market and risk at intake, with routing rules that send high-volume work to throughput and high-stakes work to people.
- Custom engine training
- Engines tailored on your own accumulated corpus, so quality improves as your translation memory grows rather than staying static.
- Automated quality estimation
- Segment-level scoring that decides what passes automatically and what escalates, with thresholds calibrated on your content rather than set by default.
Common starting points.
This usually begins when volume has outgrown the budget: a content set that is growing faster than the team translating it, or a machine translation deployment that saved money and quietly cost quality.
Whichever it is, we begin by measuring rather than proposing. The market signal audit establishes a baseline so that any change we make can be shown to have worked.
Per-language engine selection with documented reasoning
Routing rules encoded once and applied consistently, rather than negotiated per project
Quality thresholds calibrated on your content and reviewed on evidence
Custom engines trained on your corpus as it accumulates
Reporting on what was automated, what escalated, and what it saved
Six service families, one operation.
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