AI with human specialists — AI translation, AI content, dubbing, data annotation and verification in 225+ languages
AI language models combined with human specialists: from MTPE and AI content creation to data annotation, dubbing and quality verification. We work with DeepL Pro, OpenAI, Anthropic and Google plus Phrase TMS, memoQ and Trados Studio — as tooling, not as final product. One partner orchestrating your entire AI language cycle, with a GDPR-aligned process and in line with the EU AI Act.
AI language models combined with human specialists: from MTPE and quality estimation to data annotation and AI chatbots. One partner orchestrates your entire AI language cycle, with a GDPR-aligned process and native QA, in line with the EU AI Act.
AI scales where people cannot. Without a human layer, however, AI models produce output that sounds convincing yet contains errors: hallucinations, terminology breaks, cultural misreads. Our approach combines AI speed with native QA and delivers better results than either on its own.
From core EU languages to low-resource markets: AI pipelines with native QA per language.
From MTPE and AI content creation to LLM annotation and multilingual AI applications, handled by a single team.
Machine translation plus post-editing: publication quality at significantly lower cost than full manual translation.
Scalable content production with AI in 225+ languages. Native QA reviews every AI result, so scale does not come at the expense of quality.
Native specialists review AI-generated content for factual accuracy, brand voice and compliance.
Real-time QE scores per MT segment (MQM/BLEU/TER) via REST API, which cuts QA effort on large volumes.
Training data for LLMs, ASR and NER in 225+ languages, produced by native annotators with IAA kappa >= 0.8.
Custom multilingual AI applications such as chatbots, translation APIs and NLP search engines. MVP in 4 to 6 weeks.
We analyse your business case and data characteristics (volume, sensitivity, domain), then decide which AI solution actually fits the problem.
We select the right engine per use case: LLM, NMT or NLP pipeline. Privacy-sensitive data runs on-premise or in a private cloud; high volumes call for domain-trained models.
Native experts where it counts: post-editors for MTPE, reviewers for AI content, annotators for training data. Every delivery includes that human layer.
Integration into your workflow: REST API, TMS connector, CMS integration. For new AI applications: production deployment on EU cloud with monitoring and a service agreement.
Performance monitoring on quality scores, cost and latency. We improve iteratively based on real data: model tuning, workflow optimisation, scope expansion.
The best AI language projects are hybrid projects. AI does the heavy lifting, people make it publishable. We build workflows in which AI and human expertise reinforce each other, and that combination is what makes the result fit for production.
From MTPE to RLHF annotation: we bring language models, workflows and linguists together under one roof.
We work with DeepL Pro, OpenAI, Anthropic and Google, plus Phrase TMS, memoQ and Trados Studio. These are tools in the workflow, never the final product: we select the model per use case and keep human oversight on the output.
Every AI output is reviewed and refined by native language experts, in line with the human oversight requirement of EU AI Act Art. 14. This review catches hallucinations and quality drift that pure automation misses.
From a pilot batch of a thousand words to millions per month: our AI workflows grow with your project while native QA stays in place.
GDPR-aligned process with datacenter location configurable on customer request for supported tools (typically EU). With commercial vendor subscriptions, customer data is not used for model training. Data processing agreements on request.
A GDPR-aligned process, datacenter location configurable on request and native QA: the foundation of a reliable AI language pipeline.
From enterprise content pipelines to pharma LLM fine-tuning and banking chatbots.
Enterprise · AI pipeline A multinational built an AI content pipeline for 14 languages: AI generation + QE + post-editing + verification. Throughput significantly higher than manual, quality on a par.
Pharma · LLM A pharmaceutical company had 200k medical examples annotated in 12 languages for LLM fine-tuning. Native medically trained annotators, GDPR-aligned process. Measurable improvement of model quality on internal benchmarks.
Finance · Chatbot A bank launched a customer-service chatbot in 8 markets: LLM plus proprietary knowledge base, GDPR-aligned setup, fallback to human agents. MVP in 5 weeks, high self-service rate achieved.
AI solutions for translation scale, content production, quality assurance and custom applications.
What clients say about working with Ecrivus, from AI startups to enterprise ML teams.
Certified translations for our international cases are delivered quickly and carefully. Our project manager knows our account inside out.
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