Litmus has been recognized in the Gartner® Magic Quadrant™ for three consecutive years, a Niche Player in 2024, a Challenger in 2025 and a Leader in 2026.
We believe that recognition reflects a problem manufacturers can no longer solve plant by plant: creating a consistent industrial data foundation for AI across the enterprise.
Standardisierte industrielle Datenarchitektur
Optimierte Verpackungsprozesse und Volumenverfolgung
Standortübergreifende Echtzeitüberwachung von Anlagen
Echtzeitüberwachung und Korrekturmaßnahmen
No two factories are the same. Equipment, protocols, data structures, and operating environments vary from plant to plant. Without a common foundation, the data architecture behind a successful AI use case often has to be rebuilt at the next site.
Make factory data consistent
Connect and contextualize industrial data at the source so enterprise systems and AI applications receive usable, structured data.
Make deployments repeatable
Centrally deploy and manage the same data architecture across sites instead of rebuilding it plant by plant.
Keep your AI stack open
Deliver governed industrial data to the cloud, analytics, and AI platforms you already use—or run AI at the edge when the use case requires it.
Litmus brings the capabilities manufacturers need to make industrial data usable for AI into one platform, so manufacturers can scale AI without rebuilding the underlying data architecture plant by plant.
Um KI zu skalieren, braucht es mehr als Konnektivität. Erforderlich ist ein koordiniertes System, das Anlagen verbindet, Daten strukturiert, Datenflüsse absichert und Implementierungen standortübergreifend verwaltet. Genau das leistet eine gemeinsame industrielle Datengrundlage. Bei herkömmlichen Industrial-AI-Projekten dauert es 12 bis 18 Monate bis zum produktiven Einsatz. Mit dem richtigen Datenfundament können Teams ihre Daten standardisieren und Anwendungsfälle innerhalb weniger Tage statt erst nach Monaten implementieren.
Datenkonnektivität
Industrielle Anlagen und Systeme verbinden.
Datensicherheit
Industrielle Umgebungen und Datenflüsse schützen.
Industrial DataOps
Betriebsdaten kontextualisieren und modellieren.
Data Governance
Definitionen standardisieren und dauerhaft verlässliche Daten gewährleisten.
Edge Intelligence
Analysen und KI direkt an der Edge ausführen.
Zentrales Management
Data Pipelines standortübergreifend implementieren und verwalten.
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This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Litmus
