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  • Industrial AI

    The Industrial Data Foundation for Industrial AI

    Deploy, scale, and govern Industrial AI across every asset, production line, and plant on connected machines and contextualized industrial data that models can actually trust.

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  • 製造データとAIの活用を拡大している製造業から信頼されています

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  • AI IN MANUFACTURING

    What is Industrial AI?

  • Industrial AI applies machine learning, computer vision, language models, and AI agents to manufacturing operations. It runs on operational technology (OT) data from PLCs, SCADA systems, and historians —data that

  • must be connected, contextualized, and governed before any model output can be trusted. Litmus builds that Industrial Data Foundation once, then activates AI on it at the asset, site, and enterprise levels.

  • Proven in production environments

    From multi-site deployments to measurable operational gains, Litmus helps manufacturers turn fragmented data into scalable execution. 

  • 95拠点に18週間でデプロイ完了

    製造データのアーキテクチャを標準化

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  • 月300万ドル削減

    梱包の最適化と出荷量の追跡

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  • 126のエッジ展開

    製造データのアーキテクチャを標準化

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  • ダウンタイムの削減

    リアルタイムの監視と是正措置

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  • AI is no longer the hard part. Activating industrial data is.

  • The economics of Industrial AI changed in 2026. LLM inference costs fell far enough that use cases which once cost millions of dollars a year to run now cost thousands, and models can hold context across long, multi-step tasks.
     
    The plant has not changed nearly as fast. OT data still sits across PLCs, SCADA systems, historians, and other control systems. It arrives without enough context, and its structure varies from site to site.
     
    Litmus closes that gap. Connect, contextualize, and govern OT data once in the Industrial Data Foundation, then activate AI on it anywhere—at the machine, at the site, or across the enterprise.

  • TWO TYPES OF AI

    Industrial AI comes in two types, and manufacturers need both

  • Industrial AI divides into two types with different latency budgets, deployment models, and data requirements: Operational AI, which runs at the edge in real time, and Enterprise AI, which runs in the cloud across sites.

  • The LLM shift added the first without replacing the second. Most stalled AI programs picked one and assumed it covered everything.

  • OPERATIONAL AI

    Real-time, localized, prompt-driven

    Runs at the edge, next to the machine

    • No cloud round trip—decisions resolve locally
    • Conversational and on-demand for operators and technicians
    • Works in restricted, offline, and air-gapped environments
  • ENTERPRISE AI

    Continuous, high-compute, inference-driven

    Runs in the cloud across all sites

    • Finds patterns and benchmarks across plants
    • Centrally governed and version-controlled
    • Trains and redistributes models to the fleet
  • The two types need different data, in different shapes, delivered to different systems—from the same governed source. That source is the Industrial Data Foundation.

  • ASSET · SITE · ENTERPRISE 

    Choose the layer before the model

  • Industrial AI operates across three layers—asset, site, and enterprise—each with different users, latency requirements, and data needs.

  • Choosing an AI strategy means choosing which layer you are solving for first. Most manufacturers will eventually need all three.

  •  choose the layer before model image

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