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
.jpg)
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.
.jpg)
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.
.jpg)
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.
.jpg)
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.
.jpg)
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.
.jpg)
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.
.jpg)
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.
.jpg)
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.
.jpg)
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.
.jpg)
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
A new safety layer aims to help robots sense people in real time without slowing production
Updated
March 17, 2026 1:02 AM
An industrial robot in a factory. PHOTO: UNSPLASH
Algorized has raised US$13 million in a Series A round to advance its AI-powered safety and sensing technology for factories and warehouses. The California- and Switzerland-based robotics startup says the funding will help expand a system designed to transform how robots interact with people. The round was led by Run Ventures, with participation from the Amazon Industrial Innovation Fund and Acrobator Ventures, alongside continued backing from existing investors.
At its core, Algorized is building what it calls an intelligence layer for “physical AI” — industrial robots and autonomous machines that function in real-world settings such as factories and warehouses. While generative AI has transformed software and digital workflows, bringing AI into physical environments presents a different challenge. In these settings, machines must not only complete tasks efficiently but also move safely around human workers.
This is where a clear gap exists. Today, most industrial robots rely on camera-based monitoring systems or predefined safety zones. For instance, when a worker steps into a marked area near a robotic arm, the system is programmed to slow down or stop the machine completely. This approach reduces the risk of accidents. However, it also means production lines can pause frequently, even when there is no immediate danger. In high-speed manufacturing environments, those repeated slowdowns can add up to significant productivity losses.
Algorized’s technology is designed to reduce that trade-off between safety and efficiency. Instead of relying solely on cameras, the company utilizes wireless signals — including Ultra-Wideband (UWB), mmWave, and Wi-Fi — to detect movement and human presence. By analysing small changes in these radio signals, the system can detect motion and breathing patterns in a space. This helps machines determine where people are and how they are moving, even in conditions where cameras may struggle, such as poor lighting, dust or visual obstruction.
Importantly, this data is processed locally at the facility itself — not sent to a remote cloud server for analysis. In practical terms, this means decisions are made on-site, within milliseconds. Reducing this delay, or latency, allows robots to adjust their movements immediately instead of defaulting to a full stop. The aim is to create machines that can respond smoothly and continuously, rather than reacting in a binary stop-or-go manner.
With the new funding, Algorized plans to scale commercial deployments of its platform, known as the Predictive Safety Engine. The company will also invest in refining its intent-recognition models, which are designed to anticipate how humans are likely to move within a workspace. In parallel, it intends to expand its engineering and support teams across Europe and the United States. These efforts build on earlier public demonstrations and ongoing collaborations with manufacturing partners, particularly in the automotive and industrial sectors.
For investors, the appeal goes beyond safety compliance. As factories become more automated, even small improvements in uptime and workflow continuity can translate into meaningful financial gains. Because Algorized’s system works with existing wireless infrastructure, manufacturers may be able to upgrade machine awareness without overhauling their entire hardware setup.
More broadly, the company is addressing a structural limitation in industrial automation. Robotics has advanced rapidly in precision and power, yet human-robot collaboration is still governed by rigid safety systems that prioritise stopping over adapting. By combining wireless sensing with edge-based AI models, Algorized is attempting to give machines a more continuous awareness of their surroundings from the start.