HONG KONG, Dec. 12, 2025 /PRNewswire/ -- As its business footprint expanded, The Hong Kong and China Gas Company Limited (Towngas) decided to modernize its IT infrastructure on the cloud. In collaboration with Amazon Web Services (AWS) and its core partner eCloudvalley, Towngas migrated mission‑critical systems, saving up to 40% in IT costs while ensuring 99.99% availability for core systems.
Unwire.pro interviewed Alex Wong, General Manager of Information Technology at Towngas, and Joe Kwok, Regional Manager of eCloudvalley Hong Kong, to explore how cloud modernization drives transformation and how the two organizations are partnering to leverage data and generative AI to deliver better service experiences.
From "Back‑Office Maintenance" to "All‑Around Stewardship": A Proactive Partnership
Speaking about the collaboration, Joe from eCloudvalley shared: "What we are most proud of in the past two years is that 'nothing major happened.'" Rather than acting as Towngas' back‑office support, eCloudvalley functions more like an embedded steward—providing 24/7 monitoring, developing ongoing cost‑optimization strategies, and tailoring every solution to Towngas' needs.
eCloudvalley oversees full‑spectrum management and alerts—from cloud architecture security and backup mechanisms to cost‑efficiency planning—allowing Towngas' IT team to focus on business innovation and application development without worrying about infrastructure stability. "eCloudvalley feels like part of our internal team," said Alex Wong. "They anticipate our needs, optimize costs, and help us achieve maximum return on our AWS investments."
Reducing IT Costs by 30–40% and Achieving 99.99% Availability — Faster Market Response from Months to Days
With eCloudvalley's support in modernizing on AWS, Towngas now operates with greater efficiency, agility, and resilience—directly reflected in faster business rollout and scalability. The migration reduced IT costs by 30–40%, cut routine management time, and maintained 99.99% availability for mission‑critical systems, enabling Towngas to deliver consistent and reliable services more rapidly.
After adopting a microservices architecture, Towngas decoupled applications into independently functioning modules, allowing individual feature enhancements without disrupting the whole system. Joe explained that initiatives such as loyalty programs or promotional campaigns can now be adjusted weekly—or even daily—enhancing customer experience and empowering the company with real‑time responsiveness.
Looking Ahead: AI and Security Compliance Take Center Stage
As AI adoption accelerates, Towngas has already begun building AI‑driven services to enhance customer support and streamline operations. For example, leveraging Amazon Bedrock to develop generative AI tools, enabling legal and supply chain teams to quickly validate customer contracts and terms, while also exploring Agentic AI to support employees in handling complex, multi‑step tasks. Joe believes that only when internal processes are optimized and employees learn how to "work with AI" can enterprises effectively apply AI to core decision‑making and business innovation.
Looking ahead, with Hong Kong implementing critical infrastructure regulations by 2026 and AI applications becoming more widespread, data security and regulatory compliance will be top priorities in the partnership. In the AI era, data‑leakage risks take on new forms—so eCloudvalley will continue helping Towngas strengthen protective frameworks, including endpoint security, data governance, and regulation‑aligned backup mechanisms. For enterprises still hesitant about cloud or AI adoption, Joe offers simple advice: start by solving an immediate pain point, build confidence through small‑scale success, and progress step by step toward full transformation.
About eCloudvalley
Founded in 2013, eCloudvalley is a leading cloud service provider in the Asia-Pacific region, serving over 2,400 customers with tailored cloud, data, and security solutions. As an award-winning AWS Premier Partner, the company combines global expertise with local insight to support enterprises through every stage of digital transformation.
Read more: https://www.ecloudvalley.com/en/event/hk-case-sharing-towngas
** The press release content is from PR Newswire. Bastille Post is not involved in its creation. **
Towngas Saves Up to 40% in IT Costs -- How eCloudvalley Enabled Zero‑Incident Cloud Modernization
SAN MATEO, Calif., Dec. 13, 2025 /PRNewswire/ -- AI infrastructure company EverMind has recently released EverMemOS, an open-source Memory Operating System designed to address one of artificial intelligence's most profound challenges: equipping machines with scalable, long-term memory.
The Memory Bottleneck
For years, large language models (LLMs) have been constrained by fixed context windows, a limitation that causes "forgetfulness" in long-term tasks. This results in broken context, factual inconsistencies, and an inability to deliver deep personalization or maintain knowledge coherence. The issue extends beyond technical hurdles; it represents an evolutionary bottleneck for AI. An entity without memory cannot exhibit behavioral consistency or initiative, let alone achieve self-evolution. Personalization, consistency, and proactivity, which are considered the hallmarks of intelligence, all depend on a robust memory system.
There is a consensus that memory is becoming the core competitive edge and defining boundary of future AI. Yet existing solutions, such as Retrieval-Augmented Generation (RAG) and fragmented memory systems, remain limited in scope, failing to support both 1-on-1 companion use cases and complex multi-agent enterprise collaboration. Few meet the standard of precision, speed, usability, and adaptability required for widespread adoption. Equipping large models with a high-performance, pluggable memory module remains a core unmet demand across AI applications.
Discoverative Intelligence
"Discoverative Intelligence" is a concept proposed in late 2025 by entrepreneur and philanthropist Chen Tianqiao. Unlike generative AI, which mimics human output by processing existing data, Discoverative Intelligence describes an advanced AI form that actively asks questions, forms testable hypotheses, and discovers new scientific principles. It prioritizes understanding causality and underlying principles over statistical patterns, a shift Chen argues is essential to achieving Artificial General Intelligence (AGI).
Chen contrasted two dominant AI development paths: the "Scaling Path," which relies on expanding parameters, data, and compute power to extrapolate within a search space, and the "Structural Path," which focuses on the "cognitive anatomy" of intelligence and how systems operate over time.
Discoverative Intelligence falls into the latter category, built on a brain-inspired model called Structured Temporal Intelligence (STI) that requires five core capabilities in a closed loop: neural dynamics (sustained, self-organizing activity to keep systems "alive"), long-term memory (storing and selectively forgetting experiences to build knowledge), causal reasoning (inferring "why" events occur), world modeling (an internal simulation of reality for prediction), and metacognition & intrinsic motivation (curiosity-driven exploration, not just external rewards).
Among these capabilities, long-term memory serves as the vital link between time and intelligence, highlighting its indispensable role in the path toward achieving true AGI.
EverMind's Answer
EverMemOS is EverMind's answer to this need: an open-source Memory Operating System designed as foundational technology for Discoverative Intelligence. Inspired by the hierarchical organization of the human memory system, EverMemOS features a four-layer architecture analogous to key brain regions: an Agentic Layer (task planning, mirroring the prefrontal cortex), a Memory Layer (long-term storage, like cortical networks), an Index Layer (associative retrieval, drawing from the hippocampus), and an API/MCP Interface Layer (external integration, serving as AI's "sensory interface").
The system delivers breakthroughs in both scenario coverage and technical performance. It is the first memory system capable of supporting both 1-on-1 conversation use cases and complex multi-agent enterprise collaboration. On technical benchmarks, EverMemOS achieved 92.3% accuracy on LoCoMo (a long-context memory evaluation) and 82% on LongMemEval-S (a suite for assessing long-term memory retention), significantly surpassing prior state-of-the-art results and setting a new industry standard.
The open-source version of EverMemOS is now available on GitHub, with a cloud service version to be launched late this year. The dual-track model, combining open collaboration with managed cloud services, aims to drive industry-wide evolution in long-term memory technology, inviting developers, enterprises, and researchers to contribute to and benefit from the system.
About EverMind
EverMind is redefining the future of AI by solving one of its most fundamental limitations: long-term memory. Its flagship platform, EverMemOS, introduces a breakthrough architecture for scalable and customizable memory systems, enabling AI to operate with extended context, maintain behavioral consistency, and improve through continuous interaction.
To learn more about EverMind and EverMemOS, please visit:
Website: https://evermind.ai/
GitHub: https://github.com/EverMind-AI/EverMemOS
X: https://x.com/EverMindAI
Reddit: https://www.reddit.com/r/EverMindAI/
** The press release content is from PR Newswire. Bastille Post is not involved in its creation. **
AI Infrastructure Company EverMind Released EverMemOS, Responding to Profound Challenges in AI
