Business 06
This is not a business of collecting and aligning language, speech, image, and video. We provide integrated input structures where multiple modalities are generated under the same timeline, spatial conditions, and intent — enabling AI to learn perception, interpretation, and judgment coherently.
The root cause of most multimodal AI failures in real deployment is that premise conditions don't match between modalities.
Common problems in multimodal data construction:
Each modality collected for different purposes
Time synchronization done pseudo-retrospectively in post-processing
Vision is "appearance," speech is "waveform," language is "text" — siloed
Non-verbal and spatial conditions ignored
Semantic consistency after integration not verified
As a result, models become:
Working during training
Failing in real environments
Unusable for robotics
M9 STUDIO operates on the principle that multimodal integration happens in the design phase, not in post-processing.
Therefore, we determine first:
Which modalities to include
How they perceive the same event
Which information is primary, which is auxiliary
Which conditions must be shared across all modalities
We do not collect data without deciding these first.
Principle
These are generated under the same event and same conditions.
This is the core of the business.
4.1 Timeline Integration
Definition of common timeline
Unification of sampling premises
Explicit documentation of allowable drift
Structuring of asynchronous events
Data with time misalignment cannot be "semantically aligned" after the fact.
4.2 Semantic Consistency Design
For the same event, we design at the design stage:
What is expressed in language
How it is uttered in speech
What is visible in vision
How it is behaved non-verbally
This results in a data structure that:
Has no contradictions between modalities
Converges easily during learning
Doesn't learn spurious correlations
One decisive difference: we treat vision not as "aesthetics" but as "perceptual input."
Design Targets
Viewpoint: Camera position, height
Composition: Framing
Motion: Speed, direction
Attention Focus: Where attention is directed
Occlusion: What is cut off from view
These are not aesthetic elements — they are variables that directly affect AI judgment.
6. Non-Verbal Integration
In multimodal integration, non-verbal is not "glue" — it is structure.
Gaze before and after utterance
Synchronization of backchannels and gestures
Changes in pauses and spatial distance
These are integrated as event structures.
7. Metadata & Annotation
Common event ID
Time intervals
Condition ID
Per-modality references
Relationships made explicit
Multimodal Foundation Models
Dialogue Agents
Robotics Perception & Control
Interactive AI
Real-Environment AI Evaluation
Pre-Generation Integration
Can design integration before generation
Non-Verbal & Spatial
Can include non-verbal and spatial elements
Vision as Perception
Can treat vision as perceptual variables
Aligned New Generation
Can generate new data with aligned conditions
Robotics Premise
Built with robotics implementation as premise
M9 STUDIO's multimodal integrated data business is not about adding modalities — it is about enabling AI to understand the world coherently.
SPECIALIZED CHAPTER
Data for robots that don't misunderstand humans
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