Legal
Lawyers
弁護士
Japanese law, contracts, regulations and legal reasoning.
- Legal document annotation
- Legal response verification
- Contract classification
- Legal reasoning evaluation
Japanese domain expertise
Train AI to understand Japan,
not just Japanese.
Understanding the Japanese language is not the same as understanding Japan. Laws, regulations, taxation, accounting, employment systems and business practices all require specialized local knowledge.
We help AI teams put that expertise into training data, evaluation datasets and human feedback workflows.
Domain expert annotation
When AI needs more
than language skills.
Specialized AI requires specialized human knowledge. We build annotation and evaluation teams with the domain expertise a project actually calls for — from legal and tax to accounting, labor, finance and beyond.
AI systems working in regulated fields need more than fluent Japanese. They need people who know the terminology, the regulations, the workflows and the judgment criteria of the field. We support expert-driven annotation, evaluation, verification and dataset creation for AI projects that require specialized knowledge of Japan.
Professional domains
Tax
Tax Accountants
税理士
Japanese taxation, tax procedures and professional tax knowledge.
- Tax question evaluation
- Tax document classification
- AI response validation
- Tax terminology annotation
Accounting
Certified Public Accountants
公認会計士
Accounting standards, financial statements, auditing and corporate finance.
- Financial statement interpretation
- Accounting data classification
- Accounting response review
- Audit-related dataset evaluation
Labor & HR
Labor & Social Security Specialists
社会保険労務士
Japanese Labor and Social Security Attorneys (Sharoshi)
Labor regulations, social insurance, payroll and HR compliance.
- Labor law datasets
- Employment regulation annotation
- Social insurance classification
- Payroll-related AI evaluation
Registration
Judicial Scriveners
司法書士
Corporate registration, real estate registration and legal procedures.
- Corporate registration datasets
- Real estate registration datasets
- Legal document classification
- Procedural QA evaluation
Other specialized domains
Terminology, formality and edge cases differ by industry. We build the lexicon with your team before production starts.
- Finance
- Legal
- Healthcare
- E-commerce
- Customer Support
- Travel
- Technology
We do not maintain a standing roster of licensed professionals. We build each annotation team around the expertise a project requires, and availability is assessed against its scope, volume, specialization and timeline. If a qualification cannot be sourced for your project, we will tell you before it starts.
Expertise levels
The right expertise for every task.
Expert review is not always the right answer. We match the level of expertise to what the task genuinely needs, so budget goes where judgment is actually required.
Level 01
General Annotators
For tasks that need Japanese language proficiency but no specialized professional knowledge.
Level 02
Domain-Specialized Annotators
For tasks that need practical or academic knowledge of a particular industry.
Level 03
Professional Experts
For tasks that need advanced domain knowledge, professional experience or a specific qualification.
How it fits together
Japanese expertise, turned into AI-ready data.
Domain knowledge only becomes useful to a model once it is captured as structured, reviewable data. That conversion is the service.
Japanese expertise
- Japanese language
- Laws & regulations
- Tax system
- Accounting practices
- Labor practices
- Business practices
- Professional terminology
Expert annotation & evaluation
AI-ready datasets
Use cases
Built for specialized AI.
Most of this work is generative-AI work: judging what a model produced, ranking alternatives, and finding where it fails in a domain that punishes being wrong.
LLM Training
High-quality expert feedback for training AI systems in specialized domains.
AI Response Evaluation
Evaluate generated responses for accuracy, relevance, reasoning and professional appropriateness.
RLHF / Human Feedback
Collect rankings and preference data from annotators with relevant domain knowledge.
AI Benchmarking
Create expert-reviewed benchmark datasets for specialized Japanese models.
RAG Evaluation
Judge whether retrieved passages and the answers built on them are correct and useful.
AI Agent Evaluation
Test agents running professional or industry-specific workflows end to end.
Dataset Creation
Build Japanese datasets for specialized AI applications from scratch.
Red Teaming
Surface domain-specific errors, risks, hallucinations and edge cases.
Process
How expert annotation works.
Expert projects fail on ambiguity, not on throughput. Most of the work happens before production starts.
-
01
Define Expertise Requirements
We clarify the domain, the qualifications or experience required, task complexity and quality standards.
-
02
Build the Right Team
Annotators are assembled against language ability, professional knowledge, experience and project requirements.
-
03
Design Guidelines
Annotation guidelines and evaluation criteria are written together with your team.
-
04
Pilot Annotation
A pilot batch exposes ambiguity in the guidelines before it becomes systematic error.
-
05
Production
Annotation, evaluation, ranking or verification runs against the finalized guidelines.
-
06
Quality Control
Multi-stage review, consensus evaluation or expert adjudication, depending on what the project needs.
-
07
Delivery & Iteration
Datasets are delivered in your format, and guidelines keep improving against model performance.
Quality
Quality matters more
when expertise matters.
Expert annotation cannot lean on a simple agreement score. Two qualified specialists can disagree and both be defensible, so disagreement has to be adjudicated rather than averaged away.
- Qualification-based reviewer selection
- Calibration tasks
- Multi-annotator review
- Expert adjudication
- Consensus-based evaluation
- Gold-standard datasets
- Inter-annotator agreement monitoring
- Continuous guideline improvement
Human-in-the-loop
Where the expert sits in the loop.
The expert is not the deliverable. The reviewed, structured feedback data is.
-
AI model
Your model, in its current state.
-
AI output
Generated answers, rankings or actions.
-
Domain expert
A specialist who knows what correct looks like here.
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Evaluation & correction
Scored, ranked, corrected and explained.
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Feedback data
Structured, reviewable, delivery-ready.
-
Model improvement
Back into training or evaluation.
Examples
What expert annotation projects look like.
Illustrative project shapes, not client work. Real engagements are covered by NDA.
Evaluate responses from a legal AI assistant against Japanese law and professional standards.
Review answers generated by a system handling Japanese tax questions.
Annotate financial statements and evaluate AI-generated accounting explanations.
Evaluate responses on Japanese employment regulations, social insurance and HR procedures.
Create and review datasets covering Japanese corporate registration and legal procedures.
Build expert-reviewed datasets for products targeting specialized Japanese industries.
FAQ
Expert annotation questions.
If something here is not covered, send it with your request and we will answer it directly.
Annotation, evaluation or review carried out by people with relevant professional, industry or academic knowledge, rather than by general annotators. It is used where a correct label depends on domain judgment that a fluent non-specialist cannot reliably make.
Depending on the project, we can build teams that include people with relevant qualifications or practical experience. We do not keep a standing roster, and availability depends on the specialization, volume, timeline and task requirements. We confirm what is achievable before a project starts.
Projects may involve specialists in law, taxation, accounting, labor and social security, corporate registration, finance, healthcare, engineering and other specialized industries. The mix is decided per project.
Yes. Expert annotators can assess generated responses for factual accuracy, reasoning quality, professional appropriateness, relevance and compliance with project-specific criteria.
Yes. Depending on the project we support preference ranking, response evaluation, human feedback collection and the other workflows used in LLM development and evaluation.
Yes. We support the creation and expert review of Japanese-language benchmark datasets, including sets that require domain-specific knowledge.
Yes, and we recommend it. A pilot is how annotation guidelines, qualification requirements and quality-control processes get validated before any of it is scaled.
Need Japanese expertise for your AI?
Tell us what your model
needs to understand.
We can design an annotation and evaluation workflow around your domain, data, quality requirements and scale.
From general annotation to domain-expert evaluation.