100+ Languages · 30+ Dialects

Native Speaker Annotators

Every label written by someone who actually speaks the language. Across 100+ languages and 30+ dialects, with cultural fluency that crowdsourced annotation cannot fake. The opposite of marketplace-style annotation.

Get a Sample Multilingual service

Native Speakers, Not "Native-Like"

Most multilingual annotation vendors are economical with the truth here. "Native-quality" usually means a translator with bilingual proficiency. "Native speakers" usually means crowd workers in adjacent countries who scored high on a language test. The label gets done. The cultural and linguistic intuition that makes the label correct does not.

AI Taggers staffs native speakers — people who grew up in the language, who feel the difference between a slight and a compliment, who know which sentence reads as bureaucratic and which one reads as friendly. We pair them with subject-matter expertise (medical, financial, legal) when the project requires it.

Looking for a specific language? See Arabic, Hebrew, Turkish, or our multilingual hub.

100+ Languages Across 6 Regions

Arabic & MENA

Modern Standard ArabicGulf (Khaleeji)LevantineEgyptianMaghrebiIraqi

Asia-Pacific

Mandarin ChineseCantoneseJapaneseKoreanHindiUrduBengaliIndonesianVietnameseThai

Europe

GermanFrenchSpanishItalianPortugueseDutchPolishRussianUkrainianSwedish

Middle East (non-Arabic)

HebrewPersian/FarsiTurkishKurdish

Africa

SwahiliAmharicYorubaHausaZuluAfrikaans

Americas

Brazilian PortugueseMexican SpanishQuebec FrenchHaitian Creole

Don't see your language? Ask us — we cover 100+ languages, only the most-requested are listed.

What "Native Speaker" Actually Buys You

Concrete examples of native-speaker judgment vs crowdsourced alternative.

With native speaker: Catches that the user just complimented you back-handed (Khaleeji sarcasm reads completely differently to Egyptian sarcasm)

Without: Labels both as neutral

With native speaker: Knows that 'cool' in tech-Twitter Arabic means something different than 'cool' in formal MSA

Without: Treats them as synonyms

With native speaker: Recognises Arabizi (3 for ع) and code-switched mid-sentence English without breaking

Without: Drops the example as 'invalid input'

With native speaker: Catches religious or cultural sensitivities that a translation tool misses

Without: Ships datasets that get pulled by GCC enterprise clients

With native speaker: Disambiguates morphology in agglutinative languages (Turkish, Finnish, Hungarian)

Without: Splits tokens incorrectly, breaking downstream NLP

Native Speaker + Australian-led QA

Native cultural fluency at annotation. Australian project management at the QA layer. Cohen's κ reporting on every delivery. The combination that crowdsourcing cannot replicate at any price.

Free Sample · 24-48 hours

Free native-speaker pilot

Tell us your target language and use case. 25-50 record native-speaker annotated sample within 48 hours — verify cultural fluency before any commitment.