Airion

Summary

About

About Airion

Founded in Tokyo in 2021, Airion works with factory teams on the data and software layer between industrial equipment and learned robot behaviour. Physical AI Studio connects robots, cameras, grippers, sensors and controllers, then lets users assemble demonstration recording, labelling, dataset preparation and model-validation steps with no-code blocks.

The platform is currently offered as a proof-of-concept system while training and deployment functions continue to expand. In September 2026 Airion began a project with Yazaki covering wire-harness manufacturing use cases. That initial phase links factory hardware and tests the data workflow needed to develop and validate robot policies before any broader production rollout.

PRICE
Range 1Y
Return
High
Low
Updated

Products

Supply

Supply chain relationships

  • Yazaki Corporation

    Outbound - Software - Airion supplies Physical AI Studio software for Yazaki wire-harness manufacturing use-case development

    Source

Timeline

Events

  • 2026-09-24 - Partnership - Airion provides Physical AI Studio to Yazaki

    The initial work connects robots, cameras, grippers and sensors used in wire-harness manufacturing experiments. Physical AI Studio provides no-code blocks for demonstration recording, data labelling, model validation and deployment workflows. The engagement uses the proof-of-concept version of the platform while Airion continues expanding training and deployment functions.

    Source

Context

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Shared workflows

Context tags

  • Robot learning platforms - Stack Layer - https://airion.co.jp/en/physical-ai-studio - exact company scope reviewed during general-qa-actual-enrichment-2026-09-26-batch42.
  • Physical AI data infrastructure - Stack Layer - https://airion.co.jp/en/physical-ai-studio - exact company scope reviewed during general-qa-actual-enrichment-2026-09-26-batch42.
  • Robot learning research - Workflow - https://airion.co.jp/en/physical-ai-studio - exact company scope reviewed during general-qa-actual-enrichment-2026-09-26-batch42.