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As demand for on-premises computing rises, Spingence partners with Japanese AI startup FSR to support the digital transformation of Japanese industries

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TAIPEI, Feb. 12, 2025 /PRNewswire/ — Spingence, a Taiwan-based artificial intelligence startup, has announced a strategic alliance with ForceSteed Robotics (FSR) to promote the adoption of on-premises large language models (LLMs) in Japan and develop solutions that prioritize privacy protection, data security, and high-performance computing. FSR, a Japan-based startup specializing in artificial consciousness (AC), will represent Spingence in Japan, the two companies will establish a long-term partnership in sales and technology development, aiming to address the key challenges Japanese companies face in AI adoption. The alliance marks an important milestone for Spingence’s entry into the Japanese AI market.

Strategic collaboration launched: Meeting the demands of AI in the Japanese market

Research shows that demand for AI applications in the Japanese market is rising, particularly for LLMs, which have become a key technology for businesses across multiple sectors. As cloud-based services, which rely on the internet, struggle with data privacy and system stability, many companies are now turning to on-premises solutions.

“For businesses, Spingence’s on-premises LLM services are just as effective as cloud-based solutions, offering high privacy and security while maintaining strong performance,” said Hiroyuki OHSAWA, FSR’s founder and CEO. “By optimizing SSDs with DRAM, Spingence has developed a cost-effective solution to addressing memory limitations during large-scale AI model training, ensuring platform and application stability. We believe that on-premises services based on this technology will confer considerable benefits in the future.”

Furthermore, Spingence’s past success in the Taiwanese market serves as a strong foundation for expanding its business in Japan. The firm’s expertise in AI defect detection and edge AI will make its products highly competitive in the Japanese market.

Facing existing competitors in the Japanese market, OHSAWA believes that Spingence’s products hold advantages in price, maintenance efficiency, and server performance. “The emergence of local competitors in Japan indicates a sharp rise in demand for on-premises servers recently,” OHSAWA said.

Spingence and FSR have seen an opportunity to help small and medium-sized companies that struggle to find AI solutions tailored to their needs. Many solutions in the market now are expensive, come with high maintenance costs, and are typically designed for larger companies. Some solutions often fall short in terms of server performance and specifications. This leaves a gap in the market for comprehensive solutions that can address the unique challenges facing small and medium-sized enterprises.

Working together: Driving digital transformation in the Japanese market

This collaboration was initiated after Spingence gained valuable market insights at CEATEC 2024. “Japanese companies have been actively developing LLM applications and exploring various solutions, most of which rely on cloud technology,” said Jesse Chen, founder and CEO of Spingence. “The market potential of on-premises LLMs became clear as the companies identified the limitations of cloud services.” OHSAWA shared the same observation, and the mutual understanding quickly led to the formation of the partnership.

In the early stages of the collaboration, Chen said the focus would be on product-market validation to ensure that Spingence’s existing edge AI solutions meet market demands. “Based on current research and observations, it’s clear that Taiwan and Japan are in vastly different stages of development and application,” he said. “To succeed in both markets, Spingence needs to be more flexible and constantly validate market assumptions. This requires close collaboration with FSR.”

Spingence and FSR agree that on-premises LLMs have significant growth potential in the Japanese market. To ensure the effective implementation of the technology and support the digital transformation of Japanese industries, the short-term goals for the two companies include market promotion, customer education, and application case validation.

Looking ahead: Achieving technological co-creation and promoting localized product applications

In the next three to five years, Spingence and FSR will continue to promote the application of on-premises LLMs in the Japanese market and explore possibilities for product implementation through technological integration.

FSR will first focus on expanding enterprise applications, offering reasonably priced and highly secure products for companies in specialized fields. Based on experience, OHSAWA pointed out, “Whether it’s schools, factories, or other verticals, there’s skepticism about cloud computing, which has significantly increased the demand for edge AI.”

In the short term, FSR plans to strengthen local technical support and provide superior customer service. Spingence will focus on enhancing the localization of its LLM platform. By optimizing application and management tools, the company aims to provide more intuitive interfaces, customizable configuration options, and seamless integration features, all tailored to the needs of Japanese businesses, thus improving the convenience and efficiency of enterprise applications.

Technical integration is another key aspect of the collaboration. FSR will represent Spingence in Japan and act as a partner in co-creating technology, driving the deep integration of edge AI and AI technologies to develop smarter and more efficient applications.

The collaboration between Spingence and FSR represents the joint efforts of Taiwanese and Japanese AI startups to create new opportunities. Through the synergy of cutting-edge technology and market expertise, Spingence and FSR are committed to driving AI-powered digital transformation, setting new standards for on-premises AI solutions in Japan.

Media Contact
Spingence / Xenia Huang / xenia.huang@spingence.com
Forcesteed Robotics / contact@forcesteed.com

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SOURCE Spingence Technology Co., Ltd.

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JAMS Launches AI for Enterprise Job Scheduling: JAX and JAMS MCP, on the Model You Choose

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July 24, 2026

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A new AI agent and an open-standard connector let IT teams query, diagnose, and manage automation in plain language, on the model they choose, with operational data staying inside their own network

LONDON, July 24, 2026 /PRNewswire/ — JAMS Software, an orchestration solution for scheduled and event-driven automation, today announced the general availability of two AI capabilities for enterprise job scheduling: JAX, an AI agent built into the JAMS Web Client, and JAMS MCP, a connector built on the open Model Context Protocol standard that brings JAMS into external AI coding tools. Both capabilities ship at no additional cost as part of JAMS Web.

Automation environments grow faster than the teams that run them. Jobs multiply across SQL Server, Azure Data Factory, Airflow, SAP, JDE, and Banner, and when one fails, finding the root cause often means searching several consoles at once, frequently outside business hours. At the same time, IT leaders carry pressure to adopt AI while staying accountable for where operational data goes. JAX and JAMS MCP close both gaps together.

Full details on how JAX and JAMS MCP work, including the control model behind every action, are available at jamsscheduler.com/product/ai.

JAX is an AI agent that runs inside the JAMS Web Client. It finds jobs, troubleshoots failures, and answers how-to questions in plain language, with each response grounded in the JAMS user guide and checked against a built-in glossary. JAX acts only when a user asks it to. Reads flow freely, and every write action pauses for the user’s explicit approval before it runs. JAX does not learn between sessions, and conversations are not retained on the server.

JAMS MCP is a connector, built on the open Model Context Protocol standard, that brings JAMS into the AI tools engineering teams already use, including Cursor, VS Code with Copilot, Claude Code, Claude Desktop, and Codex. Users query jobs, investigate failures, and manage runs in plain language without leaving their tool.

Both capabilities run inside the customer’s own network and act as the signed-in user, with that user’s exact JAMS permissions. There is no elevated AI account: whatever a user cannot do in the JAMS interface, JAX and JAMS MCP cannot do on that user’s behalf. Every JAX and MCP operation is recorded in its own dedicated log, and changes made through the JAMS API land in the JAMS audit trail like any other change. Customers choose their own AI model, whether a commercial provider such as OpenAI or Anthropic or a model running entirely on their own hardware, and JAMS never trains on customer data. In the current release, neither feature edits or deletes a job, folder, schedule, or agent definition. For teams that must keep operational data within a defined boundary, JAX runs on a local model entirely inside the customer’s own network, so nothing leaves at all.

“Adopting AI usually means giving something up, most often visibility into where your data goes,” said Pete Hegland, Chief Executive Officer of JAMS Software. “We built JAX and JAMS MCP so that trade does not have to happen. Every action runs as the signed-in user, every change waits for approval, and the model itself can run entirely inside your own network.”

“IT teams across the United Kingdom and EMEA tell us the same thing: they want the benefit of AI without losing sight of where their data goes,” said Greg McLaughlin, Account Executive for EMEA at JAMS Software. “JAX and JAMS MCP let them keep operational data inside their own network and still get answers in plain language. That combination is what makes this practical for the teams I work with.”

JAX and JAMS MCP are available now to all JAMS Web customers across the United Kingdom and EMEA, with no separate licence, SKU, or additional cost. AI-assisted creation of new jobs and workflows from a plain-language description is on the roadmap for a future release, gated by the same approvals and permissions as every other action.

Learn how JAX and JAMS MCP work at https://jamsscheduler.com/product/ai.

Fast facts

JAX is an AI agent built into the JAMS Web Client for job scheduling and workflow automation.JAMS MCP is a connector built on the open Model Context Protocol standard, for Cursor, VS Code with Copilot, Claude Code, Claude Desktop, and Codex.Both act as the signed-in user, with that user’s exact JAMS permissions, and there is no elevated AI account.Customers choose the AI model, including a local model that runs entirely inside their own network.JAMS never trains on customer data.Both are available now at no additional cost as part of JAMS Web.

About JAMS Software

Founded in 1987, JAMS Software is an orchestration solution that helps IT teams centralize, automate, and manage scheduled and event-driven jobs across complex, hybrid environments. Over 850 customers rely on JAMS to run their automated workloads. JAMS Software, LLC is headquartered at 108 Patriot Drive, Suite A, Middletown, DE 19709.

Media Contact
Bobby Schmidt, Vice President of Marketing
press@jamssoftware.com
800.261.4267

 

 

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View original content:https://www.prnewswire.co.uk/news-releases/jams-launches-ai-for-enterprise-job-scheduling-jax-and-jams-mcp-on-the-model-you-choose-302833908.html

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Video: CNPC offers green chemical answer

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July 24, 2026

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BEIJING, July 24, 2026 /PRNewswire/ — A news report from chinadaily.com.cn:

Located on the edge of the Taklamakan Desert in Northwest China’s Xinjiang Uygur autonomous region, the Tarim 1.2 MTA Phase II Ethylene Project and its supporting green and low-carbon demonstration facility of PetroChina Dushanzi Petrochemical Company, a subsidiary of China National Petroleum Corporation, are offering a new example of China’s low-carbon industrial transformation.

Watch the video to discover how CNPC is exploring a cleaner and more circular future for the industry.

View original content to download multimedia:https://www.prnewswire.com/apac/news-releases/video-cnpc-offers-green-chemical-answer-302834036.html

SOURCE chinadaily.com.cn

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Shanghai Electric showcases embodied intelligence robot matrix and AI-native smart factory solutions at WAIC 2026

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July 24, 2026

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Featuring humanoid robots with 41 degrees of freedom, pipe‑inspection robots with ±1mm positioning accuracy, and 51 industrial‑grade AI agents

SHANGHAI, July 24, 2026 /PRNewswire/ — Operations in high-end equipment manufacturing often involve confined spaces, complex objects, and fine manipulation tasks that demand sustained and stable precision. At the recent 2026 World Artificial Intelligence Conference and High-Level Meeting on Global AI Governance (WAIC 2026), Shanghai Electric (SEHK: 02727, SSE: 601727) showcased its comprehensive portfolio of embodied intelligence solutions tailored to a range of industrial scenarios.

Themed “AI for All: Smart Squad, Shining Without Limits,” Shanghai Electric highlighted its capabilities across embodied AI robots, robot core components, and AI-native smart factory solutions, demonstrating end-to-end capabilities spanning complete robot systems, critical parts, industrial software, and smart factory architecture.

“The true value of embodied intelligence lies in understanding real industrial tasks: combining the strength, precision, and stability of machines with human experience and judgment to drive a genuine paradigm of ‘machine-assisted, human-machine collaboration,'” said Wang Chunlei, deputy general manager of the Robotics Business Unit at Shanghai Electric Automation Group.

Shanghai Electric’s robotics portfolio covers five key industrial scenarios: connector insertion, electrical operations, flexible sorting, intelligent assembly, and pipe processing. Highlights include:

“SUYUAN” bipedal humanoid robot: With 41 degrees of freedom for enhanced mobility, it is equipped with a multimodal visual sensing system on the head and torso, along with a dual-battery hot-swap system. It is well-suited for inspection, material handling, and assembly tasks.”TUOYUAN” industrial wheeled humanoid robot: Powered by an embodied intelligence foundation model and force-position hybrid control, it is capable of multi-spec connector insertion, material sorting, and loading/unloading of automotive sheet metal parts.”Mermaid” bionic wheeled humanoid robot: Capable of autonomously identifying buttons, knobs, and air switches, it generates real-time operation paths.Autonomous pipe inner-wall chamfering robot: Designed for confined spaces, it can position and process thousands of hole edges with accuracy within 1 millimeter while transmitting data in real time.

Shanghai Electric also showcased its portfolio of core components ranging from power-output to end effectors. Among them, the planetary roller screw offers more than three times the load capacity of traditional ball screws, while the DexHand dexterous hand is designed to meet diverse gripping and manipulation requirements.

Shanghai Electric launched 51 AI models and agents under its “StarCloud Intelligent Manufacturing” series across three domains: R&D and design, production and manufacturing, and operations and maintenance—covering critical equipment processes such as process optimization and wind power facility maintenance.

These industrial agents are embedded in robotic decision-making systems and the operational logic of AI-native smart factories, transforming industrial expertise into digitized, reusable capabilities. They support production-line scheduling, quality inspection, and predictive maintenance, driving the evolution of manufacturing systems from experience-driven to data-driven operations.

Shanghai Electric also released the “AI-Native Smart Factory Technology White Paper,” proposing an active evolution architecture that enables real‑time, closed‑loop optimization of production data, giving the factory self‑perception, self‑decision, and self‑execution capabilities. Built on First Principles, the AI‑native smart factory vertically integrates process flows, industrial software, agents, and smart equipment to dismantle traditional hierarchies while horizontally bridging data silos. The architecture features three core layers: the AI factory brain as the “control center,” industrial agents and embodied robots as the “execution network,” and the physical twin as the “digital mirror.”

Leveraging its deep industrial expertise and comprehensive solution capabilities, Shanghai Electric will continue to drive the implementation of AI in industrial settings, tackle technical challenges facing embodied intelligence in complex scenarios, accelerate the large‑scale deployment of AI‑native smart factories, and deliver replicable solutions across diverse manufacturing environments.

SOURCE Shanghai Electric

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