6

Data Categories

50+

Languages Supported

200+

Professional Performers

10+

Industry Verticals

1.

Data Categories

Each category captures a distinct dimension of the Yuragi layer — the meaningful fluctuations that exist in real-world human behavior but are absent from conventional AI training data.

Speech & Voice

Professional Speech Data

Emotion-controlled speech recordings with natural prosody variation, produced by professional performers from anime, film, and broadcast industries.

Formats: WAV, FLAC, MP3 + metadata JSON
Sampling: 48kHz / 24bit standard
Languages: Japanese (native), 50+ via translation pipeline

Decision & Judgment

Human Decision Trace

Structured records of how professionals make decisions at boundary conditions — where rules end and judgment begins. Captures the fluctuation zone where identical situations produce different outcomes.

Format: Structured JSON with scenario-decision-factor triples
Domains: Healthcare, Retail, Legal, Enterprise, Education

Behavioral Variability

Decision Variability Data

Quantified variation patterns in operational decision-making — how the same person's decisions shift based on implicit contextual factors that are never documented in standard procedures.

Format: Structured datasets with variability metrics
Cross-industry coverage with domain-specific tagging

Social Context

Persona-based Lived Reality

Social dynamics and implicit behavioral rules derived from real-world persona analysis across industries. Captures unspoken agreements, cultural patterns, and environmental adaptations.

Format: Structured persona profiles with behavioral annotations
10+ years of cross-industry pattern accumulation

Non-Verbal

Non-Verbal Interaction Data

Pause timing, gesture patterns, spatial cues, and other non-verbal signals that determine whether AI understands intent or just words. Designed for multimodal AI systems.

Format: Time-coded annotations + audio/visual reference
Applicable to: Robotics, conversational AI, embodied agents

Environmental

Environmental Language Data

Structured descriptions of implicit environmental assumptions — the unspoken conditions that enable stable human behavior in specific contexts but have never been expressed in language.

Format: Framework-based structured descriptions
Foundation: Environmental Language (proprietary framework)

2.

Quality Standards

Quality is not post-hoc filtering. It is designed into the data architecture from the first specification.

Standard Specification
Accuracy 95%+ annotation accuracy across all data categories, verified through multi-pass review
Reproducibility Full condition documentation enabling dataset regeneration under identical parameters
Rights Clearance 100% rights-cleared with documented consent chains — no scraping, no synthetic persona substitution
Bias Management Domain-specific bias documentation and mitigation protocols included with every delivery
Metadata Complete provenance metadata: source, conditions, equipment, environment, performer attribution
Compliance GDPR-aware data handling, ethical sourcing with fair compensation for all contributors

Every performer is compensated fairly. Every consent is documented. Every source is traceable. This is not optional — it is how all data should be produced.

3.

Delivery &
Integration

Data is delivered in formats designed for direct integration into existing AI training pipelines — no conversion required.

Aspect Details
Formats JSON, JSONL, CSV, WAV, FLAC, MP3 — standard formats compatible with major ML frameworks
Delivery Secure transfer via cloud storage (AWS S3, GCS) or direct delivery
Licensing Commercial license, research license, or custom terms — structured per use case
Scale From targeted datasets (hundreds of records) to production-scale collections (configurable)
Custom Orders On-demand data generation to specification — we design and produce data you need, not inventory
Documentation Data dictionary, annotation guidelines, condition documentation, and usage recommendations included
4.

Application
Domains

Yuragi data is designed for AI systems that must operate in the real world — where conditions are never ideal and human behavior is never fully predictable.

Domain Yuragi Data Contribution
Physical AI & Robotics Human behavior patterns, social navigation rules, and implicit environmental assumptions for robots operating among people
Autonomous Systems Non-deterministic human decision patterns for sim-to-real transfer, reducing the gap between simulated and real-world conditions
Foundation Models Implicit knowledge data for training LLMs and multimodal models on the unwritten logic behind human behavior
Conversational AI Prosody variation, contextual speech patterns, and social dynamics for natural human-AI interaction
World Model Enhancement The Yuragi layer — human reality data that bridges the gap between physical simulation and real-world deployment
5.

Detailed Specifications

This page provides a public overview of M9 STUDIO's data capabilities. The following materials are available upon request, subject to mutual NDA:

Available Under NDA

Detailed data schemas and field definitions · Sample datasets with representative records · Annotation design methodology and guidelines · Data generation process documentation · Custom integration specifications · Pricing and volume structures

We believe the value of AI data lies not just in the data itself, but in the design methodology behind it. Our detailed specifications reflect years of cross-industry pattern accumulation and proprietary frameworks that cannot be replicated from public descriptions alone.

Request Detailed Specifications

Tell us about your AI development goals. We will provide relevant specifications, sample data access, and integration guidance under NDA.

Contact Us Read the Positioning Paper