Native-language data creation
Create prompts, responses, conversations, domain text and culturally grounded examples directly in the target language.
Build multilingual AI
Build AI that speaks your users’ language.
Create, translate, localize and evaluate language data across markets with native-speaker review, locale-aware rubrics and domain-specific linguistic expertise.
Locale intelligencecoverage map
Locale
context
context
EN-US readyfr-FR reviewhi-IN dialectar-SA terms
Terminology
Naturalness
Evaluation
Native-languageCreate in context
Terminology-ledKeep meaning consistent
Market-readyEvaluate the locale
Built around the real work
Capabilities that move a model from promising to useful.
Choose the parts of the data and quality workflow that match the behavior, market or modality you are building for.
BUILD
Designed for the next decision, not the last task.
Every capability is shaped around downstream use: what the model needs to learn, what reviewers need to judge and what your team needs to ship with confidence.
Talk through your workflow01
Translation & localization
Adapt terminology, names, examples, register and cultural references to the target locale.
Multilingual SFT & preference data
Build instruction-response pairs, rankings, critiques and rewrites with native-language experts.
Linguistic model evaluation
Evaluate meaning preservation, instruction following, grammar, fluency, style, terminology and locale appropriateness.
Cultural & dialect evaluation
Test regional variants, dialect authenticity, code-switching and local conventions that generic metrics miss.
Speech translation & voice AI
Pair localized scripts with speech collection, transcription and human evaluation for multilingual assistants.
Locale workbenchContext preserved
Localefr-FREuropean French
TerminologyRetail supportapproved glossary
Human reviewNative linguistcalibration required
Evaluate more than fluency
Treat each locale as a product requirement.
A translation can sound polished and still be wrong for the market. Build a coverage matrix based on user volume, strategic markets, language risk and available evaluation quality.
- Semantic fidelity and instruction following
- Terminology, register and named entities
- Locale, dialect and cultural appropriateness
The operating model
A repeatable multilingual rollout.
Keep terminology, rubrics and reviewer calibration consistent as languages scale.
Prioritize
Rank locales by business importance, model weakness, risk and user coverage.
Localize
Build terminology, style guidance, domain context and acceptable variants per locale.
Create
Produce native or translated training and evaluation data with qualified linguists.
Evaluate
Run blind scoring, pairwise rankings, error tagging and adjudication.
Improve
Convert failure cases and expert rewrites into targeted data for the next version.
Quality that holds up in production
Make language quality visible before launch.
Language coverage is more than a list of codes. The quality system should show where meaning, tone, terminology or local authenticity change by market.
- Reference translations and native-language creation
- Blind pairwise comparison and scalar ratings
- Reusable locale regression sets and adjudication
MeaningSemantic fidelity, instruction following and unsupported additions
LanguageGrammar, fluency, naturalness, register and tone
Market fitTerminology, named entities, numerals and local conventions
Human methodError annotation, post-editing, pairwise review and adjudication
What arrives at the end
Deliverables your team can use.
Clear outputs, stable structure and the context needed to move the work into training, evaluation or production.
01Native-language prompt and response data
02Localized training and evaluation content
03Multilingual preference and critique records
04Terminology and style-guide fields
05Locale and dialect evaluation results
06Error annotations and human rewrites
07Reusable regression test sets
08Speech translation and voice evaluation assets
Where it fits
Multilingual SFT
Teach helpfulness, safety and instruction following per locale.
Global voice products
Localize scripts, collect native speech and test end-to-end conversation.
Search and RAG
Build query intent, relevance judgments and localized ranking data.
Culture-aware vision
Evaluate captioning, VQA, OCR and image-text context by market.
Start with the outcome
Build for the market, not just the language code.
Tell us the locales, product surface and model behavior you need to improve. We’ll design the training, translation and evaluation workflow around them.