ConveyMeaning-Based Translation vs. Word-for-Word
Every day the world gets more connected, and people still struggle to say what they actually mean across a language barrier — not because the words don't translate, but because the meaning behind them doesn't survive a literal, word-for-word swap. That gap is the entire reason Convey exists.
What word-for-word actually gets you
A literal translation tool maps words to words. It gets you understood at a basic level — someone can figure out roughly what you're saying. What it doesn't do is carry tone, intent, or register: a joke translated word-for-word can read as an insult, a warm message can read as cold and formal, sarcasm can read as a literal statement. The words are "correct" and the message still lands wrong.
What meaning-based translation does differently
Convey starts from a different question: not "what do these words mean individually" but "what is this person actually trying to say, and how do they want it to feel." Picking a mood — casual, formal, playful, sarcastic, flirty, and more — before translating means the AI is translating your intent, not just your vocabulary. The same sentence comes out differently depending on the mood you pick, because tone is part of the meaning, not decoration added on top of it afterward.
Why this is the fundamental approach, not a feature
Mood selection isn't an add-on bolted onto a normal translator — it's the mechanism. Every translation Convey produces is conditioned on the mood you chose, plus context flags for idioms and culturally-specific phrases that don't cross languages literally. That's a different pipeline than "translate then maybe adjust," and it's why a mood-based translation and a literal one can diverge meaningfully on the exact same input sentence.
Who this actually matters for
Anyone having a real conversation, not just looking up a phrase — texting a partner, working through something serious with a colleague, trying to be funny with a friend who speaks a different language. The stakes of "technically correct but emotionally wrong" are highest exactly where the conversation matters most.
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