Country-specific expert workflows for frontier models
Frontier models can pass US-centric evals and still fail in local professional workflows. Localize AI adds Asia expert signal alongside your existing English eval, RLHF, and post-training runs - producing localized results, failure taxonomies, preference labels, and training data within the same research cycle.
- Korea·
- Japan·
- Taiwan·
- Singapore·
- Hong Kong
Add localized expert sidecar to your current eval run
Localize AI attaches country-specific expert workflows to your existing eval process: matched task families, vetted local panels, comparable rubrics, calibration, adjudication, and structured outputs your eval or post-training team can use immediately.
Comparable Eval Results
Run local expert panels against matched task families, rubrics, or annotation schemas so results remain comparable across markets.
Local Failure Taxonomies
Identify failures caused by local workflows, documentation standards, regulations, professional norms, terminology, and domain-specific practice differences.
Preference and Reward Signal
Collect expert rankings, critiques, pairwise preference labels, and model-output comparisons for RLHF, reward modeling, and evaluation workflows.
Training-Ready Local Data
Produce expert demonstrations, corrected outputs, rubrics, rationales, and annotations that preserve local context while fitting SFT, RLHF, and post-training pipelines.
Why Localize AI
High-quality localized evals require more than expert recruitment. They require representative panels, market-aware vetting, locally informed data operations, rubric calibration, adjudication, and operators who understand how work is actually performed in each market.
A localized expert sidecar that runs alongside your eval
Localize AI plugs into existing eval harnesses, annotation workflows, RLHF pipelines, and post-training data operations. Your core eval keeps running as-is; the localized sidecar runs alongside it, translating local expert work into comparable outputs within the same research cycle.
Local expert panels
Korea · Japan · Taiwan · Singapore · Hong Kong
runs in parallelCoverage across high-stakes domains throughout South Korea, Japan, Taiwan, Singapore, and Hong Kong.
Expert panels can support rubric-based evals, domain-specific annotation, model-output comparison, preference labeling, red-teaming, and post-training data generation.
- Technology & Software
- Medicine & Healthcare
- Law & Compliance
- Finance & Banking
- Management Consulting
Representative institutions across Asia
Where experts in our network have studied, trained, or worked.
Add local workflow signal to your next eval or training cycle
Run country-specific expert work alongside your existing evaluation, RLHF, and post-training workflows - without waiting for a separate localization cycle.