Machine translation consulting
Neural machine translation can cut translation cost and turnaround, or it can add risk and rework. We help you decide where MT fits, which engine to use, and how to tune, evaluate and roll it out, so your translation process gets faster without losing the quality you are known for.
Who it is for
Localization and translation teams adding MT for the first time.
Language service providers building an MT offering.
Companies with multilingual content, such as product catalogs, that need to scale it.
See our e-commerce MT case study
What you get
Nine deliverables, each answering one question.
- Business case for neural MT
- Is it worth it for us, and by how much?
- Implementation guide
- Which processes, systems, people, training, quality evaluation and rollout plan do we need?
- Risk assessment
- What are the security and privacy risks of using MT, and how do we handle them?
- Competitor analysis
- How do industry leaders use and sell MT: as a separate service, or inside existing services at a discount?
- Pricing and pay models
- What do we pay post-editors and translators, and what do we charge clients?
- Tool recommendations
- Which translation management systems work well with MT engines?
- Post-editor training
- How do we train our editors? We advise and, if needed, develop the program.
- Engine selection and integration
- Which MT engines suit our use cases, and how hard is the integration with our current TMS?
- Tuning and quality evaluation
- How do we adapt an engine to our domain, and how do we measure whether it is good enough?
How we work
Four steps, and the plan is yours to run.
Understand your content and goals.
Languages, content types, volumes, quality bar and risk.
Build the business case and the risk view.
Expected savings and speed, and the security and privacy questions to settle.
Choose engines and tools.
Engines matched to your use cases, with the integration effort for your TMS.
Plan the rollout.
Process, pay models, post-editor training and quality evaluation, in a plan your team can run.
Advice from people who have built MT, not only bought it
Our consultants are MT practitioners. We have developed neural MT systems end to end, from data collection to quality evaluation.
Maxim Khalilov, who co-founded NLPPeople, holds a PhD in Statistical Machine Translation and has published 40+ papers. His co-founder Alexander Raginsky builds production systems across conversational AI, search and machine translation.
Meet the foundersWe do not sell an MT engine, so we recommend the one that fits, and sometimes the answer is that MT is not right for this content.
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Frequently asked questions
Related
Planning an MT program? Talk to us.
Tell us your languages, content types and volumes. We will tell you where MT is likely to pay off and what we would do first.