Evaluation infrastructure
Version criteria, evidence and findings so teams can act on them.
AI data and evaluation practice / Tokyo
Build trustworthy AI and data systems for regulated and high-trust systems in Tokyo.
AI evaluation should connect dataset lineage, review criteria and model behaviour to the decisions a regulated workflow is allowed to make. In Tokyo, this connects directly to phased modernization around critical enterprise workloads.
What this practice covers
What AI Data & Evaluation covers
Production AI depends on data workflows and evaluation systems that make quality decisions repeatable, inspectable and useful to engineers.
AI Data & Evaluation priorities in Tokyo
Organizations in Japan often modernize around highly dependable existing operations, complex partner networks and strong expectations for quality and continuity.
Tokyo's concentration of headquarters, finance, technology and commerce creates deep dependencies between modern products and long-lived systems. Incremental architecture and strong observability make change safer.
Finance, technology, retail, media, transport and healthcare combine large-scale digital demand with strong continuity expectations.
AI evaluation should connect dataset lineage, review criteria and model behaviour to the decisions a regulated workflow is allowed to make.
- Data evidence
- Phased modernization around critical enterprise workloads
- Evaluation boundary
- High-reliability platforms for transaction-heavy services
- Production decision
- Clear data governance across partner and corporate boundaries
What that gives your team
- Training data with provenance
- Evaluation that can regress
- Feedback loops that improve
Training data pipelines
Prepare and transform text, image, audio and multimodal data for controlled use.
Annotation workflows
Design labeling, review, calibration and adjudication around quality criteria.
LLM and agent evaluation
Create rubrics, datasets and regression workflows for application behavior.
Human feedback
Capture comparisons, demonstrations and preference signals in structured form.
Data quality
Make disagreement, review outcomes and quality thresholds visible.
How it works
How AI data work moves
The workflow connects quality criteria to the people, data and engineering decisions that use the result.
01
Define
Translate the quality question into criteria, rubric and decision boundaries.
02
Build
Create datasets, annotation paths or evaluation runs with traceable versions.
03
Review
Calibrate reviewers, handle disagreement and inspect quality signals.
Engagement models
Choose how we work together
Work can proceed through carefully bounded stages that reduce change risk, document decisions and support handover to Japanese product and operations teams. For AI Data & Evaluation, the initial scope should stay anchored to phased modernization around critical enterprise workloads.
ConsultingADVISORY
Opinions, reviews, and focused direction.
ExploreStaff AugmentationEMBEDDED
Ongoing capacity in your engineering team.
ExploreOn-Call SupportON-CALL
Incidents, rotations, and production response.
ExploreOutsourcingDELIVERY
Roadmaps with clear delivery ownership.
ExploreSolutionsPROJECT
Plan and deliver a defined technical outcome.
ExploreSupportCARE
Ongoing engineering care and improvement.
ExploreQuestions answered
AI Data & Evaluation questions, answered
A short set of practical questions to clarify the first conversation.
How should governance and audit needs shape AI Data & Evaluation work in Tokyo?
AI evaluation should connect dataset lineage, review criteria and model behaviour to the decisions a regulated workflow is allowed to make. Scope should begin with the systems, owners and evidence connected to phased modernization around critical enterprise workloads.
Is this model training or data engineering?
The category focuses on the data, feedback and evaluation infrastructure around AI systems; model training is only in scope where it is part of that evidenced workflow.
Can you evaluate an existing LLM or agent?
The existing LLM and Agent Evaluation Systems service supports application-specific criteria, datasets, regression runs and actionable findings.
How do you handle reviewer disagreement?
Calibration, review and adjudication are explicit workflow concerns rather than hidden quality assumptions.
Is AI Data On-Call Support available?
No. The model is drafted for architectural completeness but remains nonpublic and nonroutable.
Next step
Frame the next AI data and evaluation step in Tokyo.
Start with phased modernization around critical enterprise workloads and the technical or organizational boundary that makes it difficult today.