Articles

What a Cup of Coffee Can Teach Us About AI-Driven Manufacturing

Using Large Language Models and AI Agent Systems to Provide AI Assistants for Improved Industrial Operations

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.

The maritime industry is essential to the worldwide economy but is also a significant and growing source of greenhouse gas (GHG) emissions.

In the race to combat climate change, the maritime industry stands at a pivotal crossroads. As one of the most energy-efficient modes of transport, maritime shipping is crucial to the global economy, yet it remains a significant and growing source of greenhouse gas (GHG) emissions.

How Large Language Models (LLMs) can power virtual maintenance assistants for enhanced equipment issue diagnosis and root-cause analysis.

The drive for data democratization and AI in heavy asset industries has led to an extensive market of Asset Reliability and Predictive Maintenance software solutions. Several vendors now provide multi-page feature comparison lists for their software offerings, aiming to stand out from the crowd, but this doesn’t make the buying process any easier.

Global market intelligence firm IoT Analytics recently highlighted Arundo's Marathon software as a market leader in moving from AI-driven insights to real prescriptive outcomes in heavy asset performance monitoring. Read about one of the product features that Marathon customers love the most.

Arundo Analytics is excited to launch an innovative solution reducing electricity costs based on forecasted electricity prices

Honored with prestigious Industry Award by CIO review: Most promising data analytics solution provider 2022. We're thrilled to share that Arundo Analytics has been awarded a prestigious industry award for our pioneering work in advanced analytics and AI-driven software for industrial operations.

Tor Jakob Ramsøy, CEO of Arundo was recently interviewed by Agnes Grutle Brande, senior manager at Sprint Consulting

machine learning, data science, arundo, analytics, sintef, sessions,

In this blog post, we’ll explain why security is so important in IIoT. We'll go through the main points we regularly discuss with our customers.

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 to attend the 10th McKinsey Innovation Forum in Singapore on October 25, 2016

Brillio Expands Capability in Big Data Analytics for Asset Intensive Industries with Arundo Investment

Plug and Play IoT Batch Five Hand-Picked by Industry Experts. Plug and Play selects Arundo as one of 24 companies to join its fifth batch of IoT startups

The Industrial Internet of Things has really taken off!

Houston’s Arundo Taps Into the Industrial Internet for Energy Firms

ABB delivers virtual flow meters powered by Arundo Analytics

Employee Roy Keyes shares insight from the data science community in Houston and details around his data science work at Arundo.

Ellie details out various parts of her journey as a particle physicist and her path to Data Science at Arundo.

Creating Business Value from Unstructured Data

Arundo and Statoil ASA enter into agreement to establish data science and big data to optimize operations