Arundo & Worley Unveiling New AI Software Company: DataSeer

Arundo Analytics | February 09, 2021

Arundo Analytics, a software company enabling advanced analytics in heavy industries, announced a joint venture with Worley, a leading global provider of professional project and asset services in the chemicals, energy, and resources sectors.

The JV, DataSeer, will emphasis on evolving its cloud-based software that develops information extraction and digitization of industrial diagrams. Incorporating industrial engineering with ML, the DataSeer software allows end-users to extract data and digitize their industrial diagrams flawlessly.

Wayne Purboo, Executive Chair of Arundo, said “We are excited to see customers impressed with an AI application that solves real problems and increases productivity. Unlike most Industrial AI solutions that require integration and configuration, DataSeer is an out-of-the-box B2C software. We are proud of this product and think the best way to continue its growth is by having a dedicated go-to-market concept. DataSeer will continue to be an important part of the offering from Arundo.”

Geeta Thakorlal, President of Energy Transition and Digital at Worley said, “DataSeer has revolutionized the way we work with industrial diagrams, enhancing project efficiency. The software enables us to deliver better value to our customers, as we help them along the digital transformation journey,”

The DataSeer app significantly decreases the time engineers spend on searching through diagrams, while regulating data into comma-separated valued outputs and improving QA & QC. DataSeer aims to change the way engineers work.


Storage is normally the first suspect when identifying the causes of the app-data gap, but the facts tell a di¬erent story. Of issues had nothing to do with storage, resulting instead from configuration, interoperability, and other problems. Of issues were related to storage, including hardware and software issues, and occasionally performance.

Other News

Modulos Launches a Data-Centric AI Platform That Simplifies the Development of Trustworthy AI Applications

Modulos | May 23, 2022

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SmartCow Launches AIoT Device Management Platform to Simplify Deployment and Centralized Management of Edge AI Systems

SmartCow | June 21, 2022

SmartCow, an AI engineering company specializing in video analytics, AIoT devices, and smart city solutions today introduced its new AIoT Device Management Platform, FleetTrackr, that offers simplified deployment and centralized management of edge AI systems through a hybrid-cloud service. Key applications include smart cities using city surveillance and traffic management with thousands of AIoT devices connected simultaneously; smart manufacturing using AI for inventory management; large-scale surveillance and security deployments; and smart retail edge deployments. "Fleet management software will revolutionize companies' ability to digitally transform their business operations, and FleetTrackr is at the forefront of speciality AIoT device management software. "We recognize the need for responsive, flexible, and remotely-operated AI solutions as demand increases for efficient, smart technology." Ravi Kiran, SmartCow CEO Fleet management software is crucial for effective device management and large-scale AI deployments, whether that be for retailers building intelligent stores, or hospitals using AI to improve patient care and administrative workflow. Fleets of IoT devices need to be capable of running unattended with the ability to be reprogrammed with newer versions of device kernels or security updates. Instead of allocating unnecessary resources to reprogram these devices with new software updates, FleetTrackr comes with its own Firmware-Over-The-Air (FOTA) functionality that allows users to provision, manage, maintain, monitor, and update thousands of devices, entirely over-the-air. FleetTrackr's 24/7 remote management includes integrated security and privacy and offers a 30 percent reduction in maintenance, time, and labor costs. "A large number of heterogeneous devices forming an IoT network should be capable of running unattended at all times and reprogramming a group of devices is a challenging task when they cannot be done remotely. FleetTrackr allows administrators to update all the devices in a desired network with new software without spending weeks planning and executing deployment plans," continued Kiran. "FleetTrackr enables users to not only identify problems with large fleets of devices, but to perform software updates as well as backup and restore firmware when a device turns out to be faulty, making the experience seamless, efficient, and secure." The FleetTrackr Unified Dashboard The FleetTrackr Unified Dashboard (UI) enables users and teams to remotely manage tens of thousands of IoT devices that are in the field. The UI dashboard provides solutions for device management, software management, and issue management. Users can upgrade their AI solutions, add or delete applications, update system firmware and software, streamline operations and administrative tasks, and monitor the health metrics of devices spread over vast distances from a single control panel. About SmartCow Established in 2016, SmartCow is an end-to-end AI engineering company that builds hardware and software products for AI applications used by the defense industry, in smart cities and industry 4.0. Strategic partners include NVIDIA and PNY. The company is located in Malta, India and Taiwan and is expanding to Italy, France and Singapore.

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AlphaICs Begins Global Sampling of 'Gluon - Deep Learning Co-Processor' for Vision AI With Superior FPS/Watt Performance

AlphaICs | February 15, 2022

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PyTorch Lightning Creator, Lightning AI, Launches Open-Source Platform and Raises $40 Million Series B to Reinvent the Way AI is Built

Lightning AI | June 18, 2022

Lightning AI today unveiled the groundbreaking Lightning AI platform, backed by a $40 million Series B funding round, that completely reimagines how Artificial Intelligence (AI) products, services, and experiences are built. With Lightning AI’s new platform and framework, the company is introducing a frictionless way to build AI-powered products and services as apps composed of modular components that are seamlessly integrated and connected. Serving as the “operating system for AI,” Lightning AI’s platform ushers in a new era of accessibility and sophistication in the field of AI technology. The platform and underlying framework introduce a novel way to build AI by providing a unified experience that accelerates the deployment of AI technology across academic and enterprise use cases. Amid a fragmented machine learning ecosystem, Lightning AI’s suite of extensible open-source components and apps simplifies the underserved space and helps advance the widespread adoption of AI technology. “Launching this platform is a vital step for our company and the industry. “From day one, I wanted to reimagine the experience of building artificial intelligence beyond the current surfeit of tools and systems. Until now, there hasn’t been a way to build production-grade AI apps that takes into account the entire pipeline from development to production. Current options are limiting, highly prescriptive, and lack the flexibility needed to leverage AI in real-world scenarios. Imagine wanting to make a phone call and you’re simply handed the disparate parts that make up a working telephone, hoping that one day you’ll be able to make a phone call. Lightning AI takes the principles that have made PyTorch Lightning one of the fastest-growing open-source projects in history - simplicity, modularity, and sustainability - and applies them to the task of unifying the entire AI development and infrastructure lifecycle.” William Falcon, Lightning AI co-founder and CEO Lightning AI is the culmination of work that began in 2018 at the New York University CILVR Lab (Computational Intelligence, Learning, Vision, and Robotics) and Facebook AI Research. As a Ph.D. student working alongside advisers Kyunghyun Cho and Yann LeCun, William Falcon created and open-sourced the popular PyTorch Lightning framework. The explosive growth of the project within the AI community led Falcon to build a platform and company focused on eliminating barriers to widespread AI adoption. Grounded in the success of the PyTorch Lightning framework, the company built Grid, a platform for developing and training machine learning models on the cloud. 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The partnership will bring further ease-of-use to customers and fit well with AWS’s industry, use-case and business problem driven approach,” said Dr. Kristof Schum, Global Segment Leader of Machine Learning at Amazon Web Services. $40M Series B Fuels Lightning AI Platform and Community Growth This $40 million Series B funding round, led by Coatue with participation from Index, Bain, First Minute Capital, and the Chainsmokers’ Mantis VC, brings the total raised to date to $58.6 million. Coatue General Partner Caryn Marooney has joined the board of directors, which also includes Index Ventures Partner Bryan Offutt. The capital will fuel further technology innovation, fund new AI research, and be invested back in the company’s growing user community and ecosystem. Lightning AI’s mission is to lower the barriers to AI adoption as the global AI market is skyrocketing and on track to exceed $500 billion by 2024. “As more companies across every sector leverage AI for crucial functions, they need a solution that makes it simple to consume, train, and use AI while also building applications free from vendor lock-in and specialized AI experts,” said Caryn Marooney, General Partner, Coatue. “Coatue immediately saw the potential and significance of Lightning AI’s mission to democratize AI. We look forward to supporting William and his team as they write a new playbook for AI deployment.” The Lightning-Fast Way to Build AI Apps Lightning AI allows researchers, data scientists, and software engineers to build, share and iterate on highly scalable, production-ready AI apps using the tools and technologies of their choice, regardless of their expertise. To solve any kind of AI problem from research to deployment and production-ready pipelines, users can simply group components of their choice into a Lightning App and customize the underlying code as needed. Lightning Apps can then be republished back into the community for future use, or kept private in users’ personal libraries. Lightning AI combines a wide variety of extant tools into a modular, intuitive platform for building AI applications in research, enterprise and personal contexts. It is the foundation of the growing Lightning ecosystem, which provides developers with a suite of ready-to-use tools and required infrastructure and compute resources, as well as community support for building AI applications. The Lightning AI platform is available now and includes: The new Lightning framework, which extends PyTorch Lightning’s simple, modular, and flexible design principles to the entire app development process A collection of tools and functionalities relevant to machine learning, including workflow scheduling for distributed computing, infrastructure-as-code, and connecting web UIs A gallery of AI apps, curated by the Lightning team, which can be used instantly or further built upon A library of components that add functionalities to users’ apps, such as extracting data from streaming video A hosting platform for running and maintaining private and public AI apps on the cloud The ability to build and run Lightning Apps on private cloud infrastructure or in an on-prem enterprise environment About Lightning AI Lightning AI is the company reimagining the way AI is built. After creating and releasing PyTorch Lightning in 2019, William Falcon launched Lightning AI to reshape the development of artificial intelligence products for commercial and academic use. Focusing on simplicity, sustainability, modularity, and extensibility, Lightning AI streamlines the lifecycle of machine learning development to expand widespread AI adoption. Its aim is to enable individual and enterprise users to build deployment-ready AI tools without having to hire experts or sink resources into in-house infrastructure.

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Storage is normally the first suspect when identifying the causes of the app-data gap, but the facts tell a di¬erent story. Of issues had nothing to do with storage, resulting instead from configuration, interoperability, and other problems. Of issues were related to storage, including hardware and software issues, and occasionally performance.