Arundo Analytics

Arundo Analytics provides industrial AI software for asset-heavy and process-intensive sectors, combining operational data, time-series information, knowledge graphs, machine learning, and AI agents to support equipment monitoring, optimization, and operational decision-making. Its current portfolio combines the Arundo AI Foundation, AI Companion, cloud applications, and tailored industrial AI solutions.

1. Industrial analytics: Arundo AI Foundation connects existing industrial data sources, represents assets, sensors, models, and documents through a knowledge graph, stores time-series data, and orchestrates analytical and machine-learning models. Foundation Studio also lets teams inspect data provenance and lineage and configure the industrial domain.
2. AI assistance: AI Companion lets operations teams ask natural-language questions across sensor telemetry, maintenance records, equipment manuals, knowledge-graph relationships, and model outputs. Responses can include graphical and tabular results, while source review and trace-back show which underlying data contributed to an answer.
3. Operational fit: ArundoEdge streams IoT sensor data from industrial control systems to the cloud, while EdgeManager handles deployment and monitoring of machine-learning models. Current specialized applications include Marathon for equipment monitoring and prescriptive maintenance, CarbonPath for maritime Scope 3 emissions management, and Energy Optimizer for optimizing industrial electricity consumption and cost.
4. Commercial considerations: Arundo offers both cloud-delivered SaaS applications and tailored AI solutions built on its Foundation technology. Public list pricing is not provided, so buyers should evaluate the required applications, data integrations, deployment architecture, industrial modeling scope, and implementation services when assessing total cost and project complexity.

Arundo is particularly relevant to industrial organizations that need AI grounded in interconnected assets, sensor histories, engineering documents, and operational models rather than general-purpose business analytics or standalone generative AI.