Lower costs
We perform pre-annotation with a trained model adjusted by engineers. By automating the zero-based labeling process, we suppress cost growth that would otherwise scale with data volume.
Transform the data creation that accounts for 80% of AI projectsAnnotation Integration Solution
By bringing together AI, engineers, and annotators, we optimize annotation and quality control. We help you improve cost, quality, and lead time at the same time, even for large-scale data.
AI development projects
700+
Companies served
100+
CHALLENGES
Challenges We Solve
- 1
Labeling costs have ballooned to 2-3x, putting serious pressure on budgets.
- 2
Even with more data, performance does not improve and accuracy reaches a ceiling.
- 3
Quality concerns force re-checks, causing delays in development schedules.
People and AI work together to streamline annotation and quality management
Continuous project optimization solves all three challenges at once
SOLUTION
Approach to Solving the Challenges
At Nextremer, we take a Human-in-the-Loop approach where AI, engineers, and annotators work in close coordination. Rather than handing the annotation process over to AI alone, we create a cycle in which people and AI give feedback to each other. This can reduce data creation effort by up to 40%, enabling us to quickly build large datasets that meet high quality standards.
Better quality
AI detects inconsistencies and noise in labeled data and quantifies risk. Annotators then focus on correcting high-risk areas, improving accuracy and reducing variation.
Shorter lead times
Collaboration between AI, engineers, and annotators speeds up the cycle of data creation and model improvement. That significantly shortens the time from project launch to target accuracy.
*The reduction rate varies depending on project size and environment.
REASONS
Why Clients Choose Us
01Quality assurance backed by joint research with a national university
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 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
- ・Organize requirements for collecting raw data that reflects real operating scenarios.
- ・Create a collection plan that considers copyright, privacy, and compliance.
- ・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
Project case studies that led to successful AI development
Completed segmentation of tens of thousands of large, highly complex image data items in just a few months
A project that delivered large-scale segmentation on a short timeline to improve the accuracy of image recognition AI. We processed a complex and difficult volume of image data quickly and with high precision through technical process redesign and a robust operating system, successfully balancing quality, scale, and speed at a high standard.
Supported AI development from the upstream phase by designing specification documents from scratch with domain expertise
A project that supported crop detection improvement from specification design through annotation. Working closely with the client, we defined crop growth stages based on the state of each organ (petals, fruits, sepals, and more) and participated from the specification design stage with domain expertise, significantly improving detection accuracy.
Supported environment setup including research, selection, and customization of annotation tools
A project that supported AI development including research, environment setup, and customization of annotation tools. To meet the requirement of annotating from multiple perspectives according to multiple specification documents, our engineers implemented a proprietary tool with adjusted settings and specs, significantly reducing effort and successfully delivering a high-density dataset.
CONTACT
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