By clicking “Accept”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Cookie Policy and Privacy Policy for more information.
Article
0
min reading time

Fuglesangs And Arundo Agree To Deliver The Digitized Pumping Experience Through Cloud-Enabled Machine Learning

Arundo Q will help Fuglesangs to accelerate its ongoing focus on delivering the most reliable and highest quality products to customers in its markets.

OSLO, Norway -- Fuglesangs AS (Fuglesangs) has signed a Letter of Intent with Arundo Analytics AS (Arundo) for the digital enablement of its pumps through the Arundo Q solution

OSLO, Norway -- By enabling real-time performance monitoring in a robust and secure environment, Arundo Q will help Fuglesangs to accelerate its ongoing focus on delivering the most reliable and highest quality products to customers in its markets. Arundo Q will bring deep analytic capability to Fuglesangs' monitoring and maintenance strategy, as well as combinatory "fleet learning" models from all deployed assets across the company's serviced industries. End customers will have improved transparency into the underlying asset conditions, even as they work toward improved performance through a combination of Fuglesangs' industrial expertise and Arundo's advanced technology.

"Through a partnership with analytics expert Arundo we want to offer our clients a simple way to optimize performance, reduce power consumption, and reduce scheduled and unscheduled maintenance costs. Together, we want to convert the «Big Data» buzzword into real bottom-line results. That's truly exciting," according to Alexander Fuglesang, CEO of Fuglesangs.

"We are truly excited for this opportunity to work with a market leader aggressively bringing leading capabilities to its customers. We look forward to an exciting partnership with Fuglesangs combining our solutions to deliver world-class performance to customers operating in challenging industrial conditions," says Tor Jakob Ramsøy, Founder and CEO of Arundo.

FS-CSV21-Innvielse-sept2015-stand-Nils.jpg

Let’s connect the dots!

Join other leading industrial companies and discover how Arundo’s AI Foundation adds insight and intelligence to your operations

More articles

All articles
Article
What a Cup of Coffee Can Teach Us About AI-Driven Manufacturing
Article
Using Large Language Models and AI Agent Systems to Provide AI Assistants for Improved Industrial Operations
Article
While large language models (LLMs) may struggle to generate insights directly from time-series data, their strong pattern-recognition capabilities make them well-suited for identifying and synthesizing already existing insights. We therefore argue that an architecture featuring a separate graph-based domain model and time-series data storage is the ideal architecture for a generalized AI companion serving the heavy asset industry: An agentic AI can first identify relevant nodes (assets, sensors, and a large variety of pre-computed models) for answering a user prompt using a mixture of APIs, query language generation, semantic search, and graph traversal. Only after this filtering step is time-series data accessed and retrieved to synthesize an answer.