From data requirement to validated delivery.
Physical AI data is not a commodity file transfer.
Useful datasets require the right environment, operators, sensors,
quality controls, metadata and usage rights.
Requirement Definition
We begin with the downstream model requirement: target task, environment,
embodiment, modalities, collection volume, metadata structure and acceptance criteria.
The objective is to source the data the model actually needs rather than sell
generic inventory.
APAC Supplier & Environment Sourcing
Microtaka identifies potential data producers, operators, facilities and real-world
environments across China, Japan, Korea, India, Malaysia, Vietnam and the wider
Asia-Pacific region.
Custom Collection Operations
For requirements that cannot be fulfilled by existing datasets, programs can be
structured around small validation pilots before scaling. Depending on the project,
operations may include venue sourcing, participant coordination, hardware deployment,
collection SOPs and production monitoring.
Dataset Licensing
Existing physical-data assets can sometimes provide a faster route to model training.
Microtaka evaluates potential commercial licensing opportunities where ownership,
training rights, geographic permissions and redistribution terms can be established.
Quality Control
Quality requirements are defined against the buyer specification rather than generic
hour counts. Relevant controls may include task completion, trajectory integrity,
sensor synchronization, video quality, metadata completeness and rejection criteria.
Annotation & Normalization
Data can require episode segmentation, task taxonomy, English-language metadata,
success or failure labels, schema normalization and conversion into downstream
training formats before it becomes operationally useful.
Rights & Provenance
Microtaka treats data rights as part of the product. The objective is to maintain
clear provenance: who produced the data, where it was collected, what permissions
exist and how the buyer is permitted to use it.
Start small. Validate before scaling.
A well-defined pilot is more useful than purchasing thousands of hours
before downstream acceptance has been proven.