Litmus provides an Industrial DataOps platform that unifies and standardizes operational technology (OT) data from diverse industrial assets. This enables scalable industrial AI and analytics by collecting, normalizing, and contextualizing data at the edge, accelerating digital transformation and driving data-driven automation.
Funding
$30M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
BFounders
Product
Problem
Industrial organizations struggle to unify and standardize operational technology (OT) data from disparate sources, hindering the implementation of industrial AI and advanced analytics. This data fragmentation leads to extended time-to-value for digital transformation initiatives and limits the ability to derive actionable insights from plant floor operations.
Solution
Litmus provides an Industrial DataOps platform that bridges the gap between OT and IT systems, enabling scalable industrial AI and analytics. The platform facilitates the collection, normalization, and contextualization of data from a wide array of industrial assets and protocols at the edge. This unified data layer supports real-time analytics, AI model deployment, and seamless integration with enterprise systems, accelerating the realization of ROI and driving data-driven automation. By standardizing the data journey from the edge to the cloud, Litmus empowers organizations to unlock the full potential of their operational data.
Target Audience
The primary target audience includes manufacturing, oil and gas, automotive, food and beverage, and agriculture companies seeking to implement Industry 4.0 initiatives, industrial AI, and advanced analytics by leveraging their operational technology data.
Features
- Litmus Edge: An edge data platform with hundreds of native industrial protocol drivers for connecting to PLCs, DCS, SCADA systems, and sensors.
- Automated Data Normalization and Contextualization: Standardizes raw OT data into a usable format, enriching it with contextual information for enhanced analysis.
- Edge Data Storage and Time-Series Database: Enables local data buffering and efficient querying of historical operational data.
- Edge Workflows and Alerting: Facilitates the creation of custom data processing pipelines and real-time event notifications at the edge.
- Litmus Edge Manager: A centralized platform for managing edge devices, data, applications, and ML models across multiple sites.
- Unified Namespace (UNS) Support: Simplifies data architecture by providing a structured, event-driven approach to data organization and communication.
- Edge AI/ML Orchestration: Supports the deployment and management of machine learning models for inference and predictive analytics directly at the edge.
- Open Architecture and Integrations: Offers flexibility through prebuilt integrations and REST APIs for seamless connectivity with cloud platforms and enterprise applications.
- Zero-Touch Provisioning: Streamlines the deployment and configuration of edge devices at scale.
- ISO 27001 Certification: Demonstrates a commitment to robust data security and management practices.