YAZAKI Image Annotation Services

High-quality annotation for complex and specialized projects,
backed by over 80 years of quality management expertise.

In AI development and computer vision projects, annotation is more than just labeling—it is a critical process that directly impacts quality, project timelines, and development costs.
However, relying on in-house teams or crowdsourcing often results in inconsistent quality and limited resources, preventing AI projects from progressing beyond the proof-of-concept (PoC) stage or producing training data suitable for production environments.
YAZAKI Image Annotation Services solve the following three key challenges, delivering high-quality training data that helps accelerate AI development.

Challenge 1

Inconsistent Annotation Quality

When multiple annotators work on the same project, inconsistencies in labeling criteria can lead to unstable training data quality. As a result, image recognition models fail to achieve the desired accuracy, causing frequent false detections and misclassifications that make production deployment difficult. In many cases, only development time and costs continue to increase.

Challenge 2

No Partner for Complex Data

Specialized data such as satellite imagery, medical images, and agricultural datasets require domain expertise that only a limited number of annotation providers possess. Even when outsourced, internal teams often spend significant time reviewing and correcting annotations, preventing engineers and researchers from focusing on AI development and causing substantial project delays.

Challenge 3

Limited Resources for Large Projects

While internal teams may be able to handle small datasets for PoC projects, production deployment requires dedicated resources capable of annotating thousands to hundreds of thousands of images. Even when using crowdsourcing, inconsistent quality and delivery delays caused by varying skill levels can negatively impact the success of the entire AI project.

POINT1

Consistently High-Quality Annotation

Consistently delivering high-quality annotation through trained in-house teams and 100% inspection.

Annotation performed by highly trained in-house employees.
Real-time standardization of labeling criteria in a dedicated secure workspace.
Enhanced quality through a triple-check process, including 100% inspection.
Preventing rework through rigorous quality control workflows.

POINT2

Expertise in 8 Industries

Supporting complex annotation projects that require specialized expertise, including satellite imagery, medical imaging, and agriculture.

Our experts accurately annotate data by fully understanding the meaning and context of target objects within specialized domains.
We can also provide value-added recommendations by leveraging our extensive industry expertise.
Supporting challenging cases that other providers struggle to handle, including unclear images and rare edge cases.
Support from requirements definition onward

POINT3

80+ Years of Industry Expertise

A robust operational framework that reliably supports annotation projects from PoC to full-scale production.

Leveraging the stable operational infrastructure and quality management expertise built over more than 80 years in the manufacturing industry.
Supporting projects ranging from small PoC datasets to large-scale production datasets with hundreds of thousands of annotations.
Dedicated resources to manage multiple concurrent projects and ongoing annotation operations.
Real-time visualization and reporting of project progress.

Verify Quality in Advance

You can evaluate how our quality management system meets your quality requirements for even the most challenging image datasets by reviewing actual deliverables before placing an order.

Reliable Cost Estimates

By estimating the workload using actual sample data, we can provide a more accurate quotation that reflects the real scope of work while eliminating unnecessary costs.

Requirements Definition Support

Even if your annotation rules have not yet been finalized, we help refine the specifications through trial production, reducing rework after the project begins.