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 orchestrating 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. But without a human layer, AI models produce output that sounds convincing yet contains errors (hallucinations, terminology breaks, cultural misreads). Our approach combines both: AI speed with native QA — measurably better 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 — one partner for your entire AI language cycle.
Machine translation plus post-editing — publication quality at significantly lower cost than full manual translation.
Scalable content production with AI in 225+ languages, with native QA on every AI result. Scale advantage without quality loss.
Quality control of AI-generated content: fact-check, brand voice, compliance — reviewed by native specialists.
Real-time QE scores per MT segment (MQM/BLEU/TER) via REST API — significant QA savings on large volumes.
Training data for LLMs, ASR and NER in 225+ languages by native annotators — IAA kappa >= 0.8.
Custom multilingual AI applications — chatbots, translation APIs, NLP search engines. MVP in 4 to 6 weeks.
We analyse your business case, data characteristics (volume, sensitivity, domain) and decide which AI solution is the right fit — no hammer-and-nail thinking.
LLM, NMT or NLP pipeline — the right engine per use case. Privacy-sensitive? On-premise or private cloud. Volume-critical? Domain-trained models.
Native experts where it counts: post-editors for MTPE, reviewers for AI content, annotators for training data. We do not deliver AI without a 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 (quality scores, cost, latency). Iterative improvement based on real data — model tuning, workflow optimisation, scope expansion.
The best AI language projects are not AI projects — they are hybrid projects. AI does the heavy lifting, people make it publishable. We do not build tools that promise automation; we build workflows where AI and expertise reinforce each other. Measurably better, more cost-effective, and ready for production.
From MTPE to RLHF annotation — one partner that brings together language models, workflows and linguists under one roof.
We work with DeepL Pro, OpenAI, Anthropic and Google plus Phrase TMS, memoQ and Trados Studio — as tooling, not as final product. Model selection per use case, combined with human oversight.
Every AI output is reviewed and refined by native language experts. That prevents hallucinations and quality drift that pure automation cannot catch — in line with the human oversight requirement of EU AI Act Art. 14.
From a pilot batch of a thousand words to millions per month: our AI workflows scale with the size of your project, with native QA preserved as a layer.
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.
From a GDPR-aligned process with datacenter configurable on request to 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 + proprietary knowledge base, GDPR-compliant, 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.
No-obligation — response within one hour on business days
Last updated: May 2026
