Six technology companies showcased solutions for financial research, wealth management, consumer insight and security to investors and institutions.
Updated
November 14, 2026 11:54 PM
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Arnold Lau and King Leung (back row, fourth and fifth from left), Associate Directors-General of Investment Promotion at InvestHK, join the Ventureport team and representatives from the six technology companies. PHOTO: VENTUREPORT
Ventureport hosted the soft launch of InvestHK’s Fast-Track Connect at its AI+ Tech+ Finance+ gathering on 30 October, bringing more than 100 guests to Pier 1929 in Wan Chai.
Sponsored by InvestHK, the event brought investors, institutional representatives and technology companies together to explore investment and business partnerships. Six companies took the stage to showcase software solutions for financial professionals, consumer research, local AI computing and digital asset security.
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The Acting Head of Startups, at InvestHK, Chin-yung (CY), and the Senior Vice President (Fintech), Pauline Fan, introduced Fast-Track Connect. They explained how the agency’s international network connects it with technology companies seeking investment, customers and business opportunities in Hong Kong.
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Their remarks also highlight the need for a more structured approach to build these connections. Investors need easier ways to identify companies that match their interests, while institutions seeking new technology need access to businesses whose products could support their operations or benefit their customers.
To address this need, Fast-Track Connect provides an online platform where technology companies can publish profiles outlining their products and funding needs. Investors and corporate participants can identify relevant businesses and contact them to explore potential investments or collaborations. Service providers and community partners are also part of the platform. Alongside investors and corporate participants, these groups are designated as Champions on the platform.
Curated by InvestHK and supported by the Financial Services and the Treasury Bureau, Fast-Track Connect builds on the Global Fast Track Programme. It aims to help local enterprises expand into Chinese Mainland, Southeast Asia and other markets.
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AlphaPai, an AI-powered investment research platform developed by financial technology company RabyteTech, presented tools for financial research, including gathering data, monitoring financial instruments and comparing information from corporate earnings calls.
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At the event, the team explained how users can give research instructions in everyday language, while the software retrieves and processes information to prepare reports and presentation materials. Examples included tracking market developments and comparing company statements over time. The team also emphasised the importance of traceable sources, allowing analysts to verify the information behind their research.
A2B2.AI, a fintech startup, presented AI- powered tools designed to support investors and wealth advisers. Its specialist AI agents—software designed to carry out particular tasks—help prepare client meeting briefings, monitor financial products approaching maturity and identify market developments relevant to individual clients, according to the team.
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For investors, the tool can help make sense of lengthy documents provided by advisers and bring information from different accounts together to assess risk exposure. For wealth professionals, the tools support preparation and follow-up around client meetings.
The team described its approach as combining AI with human expertise to support financial decision- making. It also clarified that the platform does not execute investment transactions on behalf of clients, leaving those decisions and actions in their hands.
Preferences AI, an AI startup specialising in synthetic consumer research, presented simulated consumer profiles that businesses can use to explore how different audiences might respond to products, pricing and marketing messages. These profiles generate modelled responses rather than feedback from people directly interviewed or surveyed.
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The company described that its profiles are grounded in population data and account for differences between local audiences Businesses can use them to explore pricing or packaging options, as well as ask follow-up questions to understand the reasons behind a particular response. The team also discussed testing its models and working with academics to help validate the approach. It said the technology could help businesses explore consumer preferences more quickly.
ByteFuture, an AI infrastructure startup, focused on running AI on users’ own hardware. It introduced Olares, its operating system, and Olares One, a device designed to run AI models and applications locally.
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Its applications included personal assistance, financial research and content creation. The team argued that users handling private documents or proprietary trading strategies may prefer to retain that material on equipment they control.
A demonstration of AI-generated video illustrated creative work the team said could run locally. It also described applications and agents available through its software marketplace.
PureGlobal, a fintech company, addressed digital asset custody: the systems used to safeguard access to digital assets. Its team described technology for protecting access credentials and supporting offline transactions without a conventional internet connection.
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The presentation focused on infrastructure for institutions holding and transferring digital assets, including security under different operating conditions.
SlowMist, a blockchain security firm, examined the risks of giving AI agents permission to read information and perform tasks. Its examples included malicious instructions concealed in material an agent might encounter and attacks exploiting access granted to software.
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The team discussed security auditing, monitoring and investigations alongside risks arising from third-party tools and an agent’s stored information. One scenario involved exposing a wallet’s recovery phrase, which can give someone control over its assets.
Keep Reading
Redefining sensor performance with advanced physical AI and signal processing.
Updated
January 8, 2026 6:32 PM
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Robot with human features, equipped with a visual sensor. PHOTO: UNSPLASH
Atomathic, the company once known as Neural Propulsion Systems, is stepping into the spotlight with a bold claim: its new AI platforms can help machines “see the invisible”. With the commercial launch of AIDAR™ and AISIR™, the company says it is opening a new chapter for physical AI, AI sensing and advanced sensor technology across automotive, aviation, defense, robotics and semiconductor manufacturing.
The idea behind these platforms is simple yet ambitious. Machines gather enormous amounts of signal data, yet they still struggle to understand the faint, fast or hidden details that matter most when making decisions. Atomathic says its software closes that gap. By applying AI signal processing directly to raw physical signals, the company aims to help sensors pick up subtle patterns that traditional systems miss, enabling faster reactions and more confident autonomous system performance.
"To realize the promise of physical AI, machines must achieve greater autonomy, precision and real-time decision-making—and Atomathic is defining that future," said Dr. Behrooz Rezvani, Founder and CEO of Atomathic. "We make the invisible visible. Our technology fuses the rigor of mathematics with the power of AI to transform how sensors and machines interact with the world—unlocking capabilities once thought to be theoretical. What can be imagined mathematically can now be realized physically."
This technical shift is powered by Atomathic’s deeper mathematical framework. The core of its approach is a method called hyperdefinition technology, which uses the Atomic Norm and fast computational techniques to map sparse physical signals. In simple terms, it pulls clarity out of chaos. This enables ultra-high-resolution signal visualization in real time—something the company claims has never been achieved at this scale in real-time sensing.
AIDAR and AISIR are already being trialled and integrated across multiple sectors and they’re designed to work with a broad range of hardware. That hardware-agnostic design is poised to matter even more as industries shift toward richer, more detailed sensing. Analysts expect the automotive sensor market to surge in the coming years, with radar imaging, next-gen ADAS systems and high-precision machine perception playing increasingly central roles.
Atomathic’s technology comes from a tight-knit team with deep roots in mathematics, machine intelligence and AI research, drawing talent from institutions such as Caltech, UCLA, Stanford and the Technical University of Munich. After seven years of development, the company is ready to show its progress publicly, starting with demonstrations at CES 2026 in Las Vegas.
Suppose the future of autonomy depends on machines perceiving the world with far greater fidelity. In that case, Atomathic is betting that the next leap forward won’t come from more hardware, but from rethinking the math behind the signal—and redefining what physical AI can do.