Country-specific expert workflows for frontier models

Frontier models can pass US-centric evals and still fail in local professional workflows. Add localized expert signal, failure taxonomies, preference labels, and post-training data from Asia within the same research cycle.

Markets

Korea·
Japan·
Taiwan·
Singapore·
Hong Kong

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Add localized expert sidecar to your current eval run

We plug in country-specific sidecar to your current eval process: matched task families, vetted local expert panels, comparable rubrics, calibration, adjudication, and structured outputs your eval or post-training team can use immediately.

Eval

Matched Eval Results

Run expert panels against the same task family, rubric, or annotation schema so results remain comparable across markets.

Analysis

Locale-Specific Failure Taxonomies

Identify failures caused by local workflows, documentation standards, regulations, professional norms, terminology, and domain-specific practice differences.

RLHF

Preference and Reward Signal

Collect expert rankings, critiques, pairwise preference labels, and model-output comparisons for RLHF, reward modeling, and evaluation workflows.

Post-Training

Training-Ready Datasets

Produce expert demonstrations, corrected outputs, rubrics, rationales, and annotations for SFT, RLHF, and post-training pipelines.

Why Localize AI

High-quality localized evals require more than expert recruitment. They require representative expert panels, market-aware vetting, task QA, rubric calibration, adjudication, and operational judgment from people 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 eval keeps running exactly as it does today - the localized expert sidecar attaches alongside it and merges localized signal into the same outputs, within the same research cycle.

Research Objective

Existing English EvalYour English-language experts

+ Localize AI · Localized Expert Sidecar

Asia expert panels

Korea · Japan · Taiwan · Singapore · Hong Kong

Comparable, adjudicated eval results

SFT / RLHF / Post-Training Data

Coverage 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.