Japanese language & cultural context
Native Japanese judgment captures politeness levels (keigo), slang, local conventions, and social context that literal labels miss.
Japan-Specific Data AnnotationPhysical AI & Japanese Language & Culture
Annotated in Japan. Japan-based annotators bring human judgment to complex multimodal data, Japanese language, and cultural context.
AI development projects
700+
Companies served
100+
WHAT SETS US APART
Annotation shaped by Japanese language, culture, and context.
Multimodal annotation for Physical AI
We can annotate video, audio, facial expressions, movement, and human interaction on a shared timeline, for Physical AI projects where behavior only makes sense in context.
Flexible Human-in-the-Loop quality
From specification and guideline design to annotation and review, we shape the workflow and the quality checks around your data, model, and project requirements.
OUR APPROACH
Human judgment where context matters.
Our Human-in-the-Loop approach brings AI, engineers, and Japan-based annotators together. Automation accelerates the repetitive work, while people review the nuance, context, ambiguity, and edge cases that models alone can miss. We adapt the balance of automation, human review, and quality control to each project, rather than forcing every dataset into the same process.
AI
Engineers
Annotators
Pre-annotation
Detect bad data
Effort reductionup to40%
- Create labeled data
- Infer with trained model
- Compute match rate
- Extract high-risk group
REASONS
Why Clients Choose Us
01Quality assurance based on joint research with the University of Tsukuba
To improve the reliability of annotation work, we conducted joint research with the University of Tsukuba. Based on findings around standardization, guideline development, and reproducibility, we have built a quality management system that combines academic insight with field practice.
02Comprehensive support from upstream planning onward
We do more than handle labeling. We can join from upstream tasks such as specification design and environment/tool preparation. With a consistent process that advances each phase step by step, we provide solutions optimized for the customer's business challenges.
03Flexible response to new requirements and spec changes
We adapt flexibly to unexpected cases that arise during annotation work. Instead of simply continuing with the original plan, we continuously update the specification documents and guidelines so that even special data can be used effectively.
Safely manage your data assets
Limited use of data
Data and deliverables entrusted to us are used only for the relevant project. We do not repurpose them for our own products or services.
Access restrictions
By limiting access to the minimum necessary people and monitoring logs, we store data securely and prevent information leaks.
Model disposal
Models trained on the relevant data, intermediate artifacts, and data no longer needed are promptly discarded once the project is complete.
PROCESS
Our delivery process
STEP01
Requirements definition
- ・Clarify the business goals and the purpose of AI use, then define the project's success criteria.
- ・Develop a project plan after determining the best balance of cost, quality, and development lead time.
STEP02
Data collection
- ・Where a project needs it, we can start from planning and sourcing raw data that reflects real operating scenarios.
- ・Create a collection plan that takes copyright, privacy, and compliance into account.
- ・Source raw data that covers edge cases and diverse environments.
STEP03
Specification design
- ・Organize requirements such as data type, volume, accuracy, coverage, and label structure.
- ・Interview client experts and decision criteria, then organize the information structurally.
- ・Define quantitative metrics and produce a specification document that reflects the interview findings precisely.
STEP04
Team setup
- ・Assign the right people according to the project's characteristics and clarify roles and responsibilities.
- ・Provide short-term training to help the team quickly acquire industry-specific knowledge and skills.
STEP05
Environment setup
- ・Build a workspace with AI models incorporated on the assumption that pre-annotation will be used.
- ・Research, select, and customize annotation tools.
- ・Connect systems to visualize workload, progress, quality, and error rates.
STEP06
Annotation
- ・Validate the specification and environment settings through a small pre-check using a limited data sample.
- ・After AI pre-annotation, operators review and correct the labels.
- ・Quality is maintained by combining AI anomaly detection with human random sampling.
CASE STUDIES
Japan-specific annotation in practice.
In-Cabin Multi-Track Annotation
- Case category
- Physical AI / Automotive
- Target Client
- Automotive & Physical AI Developers
- Challenge
- Understanding what happens inside a vehicle means reading several signals at once: what people say, how they say it, what they do with their hands, and how they feel from moment to moment.
- Solution
- Japan-based annotators labeled in-cabin video of two occupants on synchronized tracks: actions, Japanese dialogue with English translation, and emotion, all aligned on a single timeline.
- Impact
- Training data in which speech, behavior, and emotion can be read together rather than as separate labels, for driver monitoring and human-machine interaction in Japanese in-cabin settings.
Evaluation Annotation for Japanese Cultural Depictions
- Case category
- Japanese Language & Culture
- Target Client
- Global LLM & Generative AI Companies
- Challenge
- An AI-generated description of a traditional Japanese meal (ichiju-sansai) named the dishes correctly but got the etiquette wrong, such as where the rice and the soup should sit. Errors like these are invisible without cultural knowledge.
- Solution
- Native Japanese annotators reviewed each description, judged it correct or incorrect, highlighted every wrong statement where it stands, and wrote down why it is wrong.
- Impact
- Specific, explained corrections instead of a single score, so model developers can improve outputs for cultural accuracy and appropriateness for Japanese users.
Frequently asked questions
What types of data can you annotate?
We can annotate images, video, audio, text, and 3D point clouds. Typical tasks include bounding boxes, polygons, segmentation, and skeletal keypoints for images; action and event segments for video; transcription and emotion labels for audio; and tagging and relation labels for text. We also write and correct image and video descriptions used to train vision-language models (VLMs), and evaluate generative AI output. Japan-based annotators handle tasks that depend on Japanese language or cultural context.
Can you deliver in the format we need?
Yes. We deliver in the format your training framework reads, such as COCO, YOLO, or Pascal VOC, or as CSV or JSON. The format is fixed in the specification before work begins, so no conversion is needed midway.
How is pricing determined?
Pricing depends on the data type, annotation method, volume, number of objects per item, and the level of quality review you need. We build the quote from the per-item work rate plus specification design, project management, and review. We usually confirm the rate in a small pilot before quoting the full project.
Can we start with a small volume?
Yes. You can start with a pilot on a small set of data. The pilot checks that the specification and work environment are sound and measures work time and quality per item. We then propose a quote and team for the full project.
How long does a project take?
It depends on the data volume and the complexity of the specification. We propose a team size and schedule based on the work time measured in the pilot. For example, we completed segmentation of tens of thousands of complex images within a few months. Tight deadlines can be met by ramping up the team earlier, which may raise the unit rate.
Can we ask you without a specification or guideline?
Yes. We interview you about the AI use case and decision criteria, and support you from drafting the specification and annotation guideline. When unexpected cases appear during the work, we keep updating the specification and guideline as we go.
How is our data handled?
Data and deliverables you entrust to us are used only for the relevant project and are never repurposed for our own products or services. Access is limited to the minimum necessary people and logs are monitored. Models trained on the data, intermediate artifacts, and data no longer needed are promptly discarded once the project is complete.
Tell us what you're building.
Physical AI, Japanese language, or both, get in touch with us about your annotation needs.