Four steps. Before and after every translation.
Other tools translate. Flixu analyses, retrieves, translates — and scores. Every time.
Input is analysed for culture, domain, formality, visual context, and complexity. The optimal model is selected.
TM is searched for fuzzy matches. Glossary terms are extracted. Reranking applied depending on plan.
All parameters and Brand Voice are passed to the chosen LLM. TM matches serve as context, not as raw output.
A separate analysis LLM scores the output: terminology, brand voice, fluency, context alignment. Score is returned with the translation.
No other tool in this segment returns a context-based quality score after every translation.
Traditional Machine Translation fails because it lacks context, translating sentence by sentence. Flixu orchestrates 7 layers of context—from glossaries to brand voice and geometric layout—to ensure every translation is stylistically consistent, accurate, and completely native to the target audience.
What Flixu understands.
Cultural Awareness
Target country defines context — not just language. EN→JP (Tokyo) ≠ EN→JP (Osaka).
Domain Awareness
Flixu detects whether your text is Legal, Medical, Tech, or Marketing — automatically. The right model is selected.
Document Awareness
Long documents are read in full before a single segment is translated. No more sentence-by-sentence guessing.
Image Context
Attach a UI screenshot as visual reference. The model reads the layout and translates with spatial precision.
Formality Awareness
Sie or Du? Tu or Vous? Flixu infers the correct register from your Brand Voice and target market.
Brand Voice Awareness
Your configured Brand Voice is embedded into every translation. Consistent across all clients, automatically.
TM + Glossary Awareness
Fuzzy TM matches are passed as context to the LLM — not served as the raw output like other tools. The result is a complete, context-correct translation.
Frequently Asked Questions
What is the biggest problem in machine translation?
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Polysemy. Words have multiple meanings based on context (e.g. 'Bank' as a river or a financial institution). Legacy MT translates word-by-word leading to errors.
How does Flixu solve polysemy?
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Flixu uses Neural Attention Mechanisms and a 7-layer Context Orchestration pipeline (including Glossary, Translation Memory, and Brand Voice) to evaluate the whole document simultaneously, effectively solving polysemy.
What formats does Flixu support preserving context for?
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Flixu natively supports software infrastructure (JSON, YAML, strings), multimedia subtitles (SRT, VTT), and complex document layouts (IDML, DOCX).
How do you ensure data security during the context analysis?
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Flixu is built for the enterprise. We enforce strict data sovereignty, offer dedicated VPC deployments, and never use your private corporate context to train public AI models.
What happens if the source text itself is ambiguous?
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Our engine relies on the 7-layer context pipeline. If a standalone string like 'Home' lacks surrounding words, the engine checks its metadata (e.g., UI component vs marketing page) to correctly output either a navigational element or a residential noun.