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Litmus Edge lets you deploy and manage containerized applications directly at the industrial edge—databases, analytics tools, ML models, and more—with centralized control across every site via Litmus Edge Manager.

Discover what it is, why it's different from enterprise data catalogs, and why manufacturers need one to scale analytics and Industrial AI. A clear, concept-first explainer.

Discover the foundation layers reshaping modern industrial data architecture and why the companies building stronger foundations today will scale Industrial AI tomorrow.

Raw tags carry no context. Litmus Edge fixes this at the source—build a Data Model (Digital Twin) at the edge and send every downstream system a structured, meaningful payload.

Learn how manufacturers can connect legacy machines, proprietary protocols, and hard-to-access OT systems to build an AI-ready industrial data foundation with Litmus.

Publish industrial data from Litmus Edge directly into Databricks with ZeroBus Ingest, creating a simpler edge-to-lakehouse path for analytics, AI, and governed OT data workflows.

Litmus Edge helps industrial teams move structured OT data into Snowflake with Snowpipe Streaming for faster dashboards, analytics, and AI workflows.

If your second AI deployment feels as hard as your first, this will explain why.

What works in one plant should scale. So why does it feel like starting over every time?

At Hannover Messe 2026, one thing became immediately clear: manufacturers are moving beyond isolated Industrial DataOps initiatives and AI pilots and focusing on scalable operational transformation across global production networks.

Why connected, contextualized industrial data is the foundation for scaling Industrial AI from pilot to production.

Bring Git-based version control, auditability, and governance to distributed industrial edge deployments with Litmus Edge Manager Git Integration

Litmus Edge Cascading introduces a scalable model for moving industrial data across sites, layers, and systems without adding architectural complexity.

AI success in manufacturing is fundamentally a data problem, not a tooling problem.

Litmus Data Catalog brings visibility, lineage, and governance to industrial data so teams can find, trust, and scale AI across sites.

If you've spent time in industrial environments, you know the problem isn't a lack of data. It's collecting it reliably, contextualizing it, and storing it at scale. Most stacks weren't built to fight all three battles.

As part of the Google Connected Platform for Manufacturing, this architecture brings together Manufacturing Connect Edge from Litmus, Google Distributed Cloud Connected, and a range of Google Cloud services to unify data across manufacturing environments.

The Litmus MCP Server Web UI lets teams operate Litmus Edge through a built-in chat interface—turning natural language into real device, data, and deployment actions.