Welo Data

We are not a Silicon Valley startup. If your team needs a partner who already knows someone on the inside, that's not us. What we have instead is an unmatched capability in multilingual datasets across 155+ locales and no amount of proximity to Palo Alto will find you a Levantine Arabic specialist who can explain why your model's answer is technically correct and culturally wrong. Pick us for the second problem.

Speech, image and video all carry language. The moment a modality leaves English the hard problems compound: accent in audio, reading order in image, cultural context in video. Adding modalities does nothing to close that gap. It closes when the data is collected across 155+ locales by people who speak the language.

Every frontier model looks strong on an English eval set, because that is the set it was tuned against. Move the same task into Hindi or Bahasa and performance falls away in ways no English benchmark will show you. We collect the data that exposes those gaps, across 155+ locales, with the cultural context the task requires.

Frontier valuations are priced on global adoption, and global adoption depends on the model working outside English. Right now, roughly 90% of the incremental opportunity sits in languages where performance is measurably worse. Every quarter that gap stays open, it is priced into a growth curve that has not been earned.

Modern Standard Arabic will not tell you how a request for medical advice is phrased in Cairo or Casablanca. Dialect and cultural context decide whether the answer coming back is the right one. This part of data collection cannot be scaled by volume. It has to be built locale by locale, by people who live there.

Your growth ceiling is set by the languages you skipped. Roughly 950 million people use AI today. The working-age population of the planet is 5.2 billion. Almost every one of the missing 4 billion will meet your model in a language you have underinvested in.