EveryLanguageMatters
Our mission is to build foundational multilingual AI infrastructure serving every community around the world.
Creating a scalable multilingual data foundation to power AI across all languages. This ensures every community, in every region and language, can benefit from accessible and inclusive technology.
Multilingual Annotation Studio
Where the missing data gets made.
A model drafts, a speaker corrects, a second speaker clears it. Communities hold their own projects and decide what is fit to release.
Supporting underserved languages to become AI-ready
EveryLanguageMatters builds the data foundation that lets communities across Africa and the Global South own, correct and govern the language data their models are trained on.
How the studio works
Nothing reaches a dataset on one person's say-so.
Most annotation tools hand one item to one person and lock everyone else out. Here a prompt has a lane per language, and people work them at the same time.
Two-stage community review
one speaker corrects, another clears it or returns it with a reason
Parallel language lanes
a prompt is unfinished until every target language has something in it
Phrase-level judgement, kept
highlight a span, record what it should be, and the reasoning travels with the data
Releases that cannot overstate
review state is derived from the rows, never chosen at export
What a project collects
One workspace, whatever the task is.
A project declares its objective when it opens. The interface stays the same either way — a source, one or more outputs, and the judgements people make about them.
Translation
Parallel text in any direction, including pairs that never pass through English.
Sequence to sequence
Rewriting, simplification, structured extraction, instruction following.
Sentiment & classification
Labelled by speakers, not translated from an English-labelled set — which is how idiom gets lost.
Summarisation
Reduced in the same language, with reviewers checking for the overstatement it invites.
Question answering & NER
Annotated against conventions the community agrees for its own names and places.
Response & RAG evaluation
Preference between two answers, and whether a retrieved passage actually supports one.
Text, speech and image projects share one review workflow — learn it once.
Data & AI APIs
Models trained on reviewed data, behind a key.
One integration rather than a research project, so a clinic, a ministry or a two-person team can serve people in their own language without rebuilding any of this.
curl https://www.everylanguagematters.com/api/v1/generate \
-H "Authorization: Bearer $ELM_KEY" \
-H "Content-Type: application/json" \
-d '{"task": "translate",
"text": "Take one tablet after meals.",
"source": "eng",
"targets": ["bem", "swa", "hau"]}'
Translation, any direction
with the measured quality of every language published openly
Sentiment & classification
trained on judgements from speakers rather than a translated label set
Sequence to sequence
one prompt into one or many languages in a single call
Scoped keys, free tier
usage returned with every response, not from a second endpoint
77 languages and growing
Every language on this list has somebody behind it.
A speaker who decided it was worth the trouble, a community that took it on, or a dataset that was built and released. More are added as evaluation sets are built for them — a language is listed when it can be measured, not before.
Not on the list yet — tell us what to add next.
Showing of 77 languages
Get started with the
Multilingual Annotation Studio.
Create a free account, open a project in your language, and start building the training data that frontier models are missing. No setup, no minimum commitment — a single corrected sentence is already a contribution.
EveryLanguageMatters — foundational multilingual AI infrastructure for every community, in every region and language.