01 / INTENT
Understand what is actually meant
Interpret implied meaning, emphasis and conversational intent rather than treating every phrase literally.
Early-stage multilingual AI product
Say what you mean, not just what you translated.
OHBE is building an AI localization copilot for multilingual teams. It helps preserve intent, tone, idioms and cultural context instead of translating words in isolation.
Context-aware localizationConcept preview
Original · Turkish
“Oh be, sonunda bitti.”
Recommended English
“Finally — what a relief.”
Casual
“Thank God, it’s finally done.”
Professional
“Glad we finally got that wrapped up.”
Cultural context
“Oh be” expresses relief after tension or frustration. A literal translation loses much of that emotional meaning.
The product
Translation gets the words right. Communication requires more.
OHBE is designed for the layer ordinary translation often misses: what the speaker intends, how the message feels, and what a phrase means inside its cultural context.
02 / TONE
Preserve how it should feel
Adapt formality, warmth, humor and directness for the target audience and communication setting.
03 / CONTEXT
Recognize cultural nuance
Surface idioms, culturally specific expressions and references that need more than a dictionary equivalent.
04 / OPTIONS
Offer useful alternatives
Generate casual, professional, customer-facing and marketing-ready versions with clear differences in tone.
05 / EXPLAIN
Show why wording changes
Explain where literal translation falls short and what cultural or tonal decision shaped the recommendation.
06 / CONSISTENCY
Keep a voice across content
Support repeated localization while respecting product terminology, audience expectations and style guidance.
Why “OHBE”?
A name that explains the problem.
“Oh be” is a Turkish expression of relief that rarely maps cleanly to a single English phrase. Its meaning depends on tone, situation and cultural context.
That is exactly the kind of gap OHBE is built to handle: preserving what people mean when word-for-word translation is not enough.
Meaning before literal translationInitial focus
Turkish ↔ English first.
The first MVP intentionally focuses on one language pair so we can evaluate quality deeply before expanding.
Early use cases include product localization, customer communication, marketing copy and internal team messaging.
Early MVP · 2026How we're building
Claude-first prototyping for contextual language reasoning.
OHBE is evaluating Claude as the core model platform for meaning and intent extraction, tone-aware rewriting, idiom detection, cultural-context explanation and audience-aware localization. The product is being developed with human review at the center of the workflow.
MVP / INPUT
Text + audience context
Users provide the source text, target language, audience and communication setting.
MVP / REASON
Interpret beyond vocabulary
Claude analyzes intent, tone, idioms and relevant cultural signals before producing a localized result.
MVP / REVIEW
Compare and choose
Users see a recommended version, alternatives and concise context notes so they remain in control.
Early development
Localization that keeps the meaning intact.
OHBE is currently developing and testing its first Turkish ↔ English localization workflows. If you work across languages and care about tone, intent and cultural nuance, we'd like to hear from you.
ohbe@ohbe.org