Multilingual Evaluation
Multilingual output evaluation for a global consumer AI
Native-speaker linguistic and cultural evaluation across 18 languages — beyond back-translation, beyond BLEU.
18
Languages evaluated
400+
Native-speaker specialists
96%
On-time SLA delivery
12
Regional dialects covered
Client context
A global consumer brand had launched an AI-powered assistant in 18 markets, but internal red-flag reports showed the model producing culturally awkward, occasionally offensive, or region-inappropriate output — issues invisible to their English-first eval pipeline and to standard back-translation checks.
Challenge
Stand up native, near-native, and dialect-competent evaluators across 18 languages within four weeks, capable of judging AI output for linguistic accuracy AND cultural fit — not just surface translation quality — with structured rubrics the client's ML team could ingest as regression tests.
What we delivered
Step 1
Deployed 400+ native-speaker evaluators across French, Spanish (LatAm & EU), German, Japanese, Korean, Arabic (MSA + regional), Portuguese (BR + PT), Hindi, Mandarin, Italian, Dutch, Polish, Turkish, Vietnamese, Thai, Indonesian, and Swahili
Built culture-specific rubrics per market
honorifics, taboos, sensitive topics, brand-voice fit — co-designed with the client's regional marketing leads
Step 3
12 regional dialect variations covered with dedicated senior reviewers (e.g., Egyptian vs. Gulf Arabic; European vs. Brazilian Portuguese)
Step 4
Weekly failure-mode reports with severity-ranked findings and reproducible test cases delivered directly into the client's eval framework
Outcome
18
Languages evaluated
400+
Native-speaker specialists
96%
On-time SLA delivery
12
Regional dialects covered
The client identified and fixed 340+ locale-specific quality gaps in the first quarter. Cultural-fit scores across the 18 markets rose from an average of 6.4 → 8.2 out of 10, and the engagement is now the standard pre-release gate for every new market launch.
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