TECH OFFERS

This technology comprises two AI-powered software solutions that automate radiological image analysis to support the diagnosis and evaluation of knee osteoarthritis (OA) and lower limb alignment. One module enhances musculoskeletal diagnostics by detecting radiographic features such as joint space narrowing and osteophytes using criteria like Kellgren & Lawrence grading. It enables standardized, automated evaluations that support radiologists and orthopedic professionals in making accurate assessments. A complementary module focuses on analyzing lower limb alignment by measuring critical anatomical parameters including the Hip-Knee-Ankle angle, Joint Line Convergence Angle, and Mechanical Lateral Distal Femoral Angle. These automated assessments reduce human error and reading time while improving diagnostic accuracy and consistency. Designed for seamless integration with Picture Archiving and Communication Systems (PACS), this system fits effortlessly into existing radiology workflows. Target adopters include hospitals, imaging centers, orthopedic clinics, and telemedicine platforms seeking improved efficiency, diagnostic consistency, and enhanced musculoskeletal healthcare outcomes. The solution functions as Software as a Medical Device (SaMD), capable of receiving, analyzing, and reporting on X-ray images in DICOM format. Key components include: Image Input Module – Processes digital X-ray images using standard DICOM protocols. AI Analysis Engine – Utilizes a deep learning model to identify pathologies and quantify disease progression. Visualization & Reporting Module – Produces intuitive diagnostic visuals to support clinical decision-making. PACS Integration Interface – Ensures seamless integration with hospital IT systems via standardized protocols. By automating diagnostic workflows, the software supports earlier, more accurate diagnoses and helps optimize healthcare operations. Orthopedic & Radiology Departments: Supports image-based OA detection, severity grading, and leg alignment evaluation. Hospitals & Clinics: Enhances diagnostic workflows, reduces inter-reader variability, and facilitates second opinions. Health Screening & Telemedicine Services: Enables AI-assisted remote screening and preventative care programs. Insurance Providers: Supports value-based care through risk stratification and outcome-driven reimbursement frameworks. This technology enhances diagnostic precision, streamlines clinical workflows, and reduces cost and error through AI-powered automation: For Clinicians: Provides reliable, standardized imaging assessments, bridging expertise gaps between junior and senior staff. For Patients: Delivers early diagnosis and accessible insights, improving treatment outcomes and engagement. For Healthcare Systems: Cuts unnecessary procedures, improves resource use, and enhances productivity. By automating and standardizing musculoskeletal imaging analysis, this technology provides a scalable, cost-effective, and clinically validated solution that enhances diagnostic precision, operational efficiency, and patient care. Healthcare, Diagnostics, Medical Devices, Telehealth, Medical Software & Imaging

This non-invasive wearable integrates advanced photoplethysmography (PPG) sensing with a proprietary Pulse Shape Variability (PSV) algorithm to deliver real-time insights into stress levels linked to blood pressure fluctuations. Unlike conventional wearables that rely solely on heart rate or HRV, this technology analyzes the full morphology of the pulse waveform, capturing dynamic changes in amplitude, rise time, and contour that reflect vascular tone modulation caused by psychological stress. The result is a highly responsive and motion-tolerant stress detection platform that functions effectively in real-world conditions. By transforming microvascular signals into actionable insights, the solution enables proactive stress awareness, personalized wellness coaching, and context-aware emotional feedback, unlocking new opportunities in digital health, telemedicine, fitness, and mental wellness ecosystems. The technology owner is primarily seeking industry adopters and solution partners including medical institutions, device manufacturers, software developers, and fitness centers, who can integrate the technology into real-world applications with interest in deploying the system for use cases such as mental wellness, stress monitoring, fitness optimization, and remote healthcare. They also welcome collaboration with subject-matter experts to jointly enhance the algorithm and explore new features or application areas. This wrist-worn wearable captures and processes high-resolution biometric signals using integrated optical and motion sensors, enabling detailed physiological monitoring through advanced signal analysis. Signal Acquisition: Photoplethysmography (PPG) and Accelerometer (ACC) signals Derived Metrics: Heart Rate (HR) Blood Oxygen Saturation (SpO₂) Pulse Shape Variability (PSV) – derived from pulse waveform morphology to assess stress-related vascular responses Sleep Parameters: REM and NREM sleep stages Total Sleep Time (TST) Apnea-Hypopnea Index (AHI)  Activity Data: Step count and movement recognition System Capabilities: Real-Time Stress Detection: Continuous analysis of physiological stress signals with no need for user calibration Operates effectively during both rest and typical daily movement Motion-Tolerant Signal Processing: Proprietary algorithms reduce noise from physical activity, enabling reliable readings in dynamic conditions This wearable stress and health monitoring technology has broad applications across healthcare, wellness, fitness, and cognitive performance domains. Its ability to deliver continuous, non-invasive physiological insights makes it suitable for a wide range of use cases: Healthcare & Telehealth: Continuous patient monitoring, early detection of stress-linked health risks, and remote management of chronic conditions, particularly for mental health, cardiovascular, and sleep-related concerns. Medical Concierge & Premium Wellness Services: Personalized health monitoring for high-net-worth individuals, offering tailored stress and wellness insights, real-time biometric updates, and proactive intervention strategies. Mental Wellness & Stress Counselling: Real-time monitoring of stress indicators to support therapists, coaches, or counselors in delivering timely, personalized stress management interventions. Fitness & Recovery Optimization: Accurate tracking of heart rate and stress levels during and post workouts, enabling intelligent recommendations for training intensity, rest periods, and recovery quality. Workplace Well-being & Performance: Monitor cognitive load and emotional strain in high-performance environments, enabling preventive strategies for burnout and stress-related fatigue. Smart Devices & Platform Integration: Embedding into smartwatches, fitness trackers, or medical-grade wearables, with seamless connectivity to digital health apps, dashboards, and remote care platforms. This wrist-worn device offers a significant advancement over current health monitoring solutions by leveraging advanced photoplethysmography (PPG) technology combined with a proprietary Pulse Shape Variability (PSV) algorithm. It delivers highly accurate and continuous tracking of vital signs, including heart rate, SpO₂, and stress-related biomarkers—with minimal interference from physical movement, making it ideal for real-world, everyday use. Unlike conventional wearables that rely on basic HR or HRV metrics, this solution analyzes the full morphology of the pulse waveform to detect subtle changes in vascular tone associated with psychological stress. This allows users to correlate emotional states with verbal expressions and behavior, enabling more mindful, data-driven self-awareness and health management. The device’s motion-tolerant design, real-time data transmission, and non-invasive operation ensure consistent performance even during physical activity. Its seamless integration with health platforms and apps further enhances usability, positioning it as a versatile tool for individuals, clinicians, and wellness providers. Ultimately, this technology empowers users to make informed decisions about their stress levels, recovery, and overall well-being—bridging the gap between biometric sensing and emotional health insight in a user-friendly wearable format. Photoplethysmography, PPG, Pulse Shape Variability, PSV, Stress Detection, Wearable, Wellness, Non-Invasive Monitoring Healthcare, Diagnostics, Infocomm, Wearable Technology

The adoption of multi-functional autonomous robots is steadily increasing to support and enhance operational efficiency in the facilities management sector. This technology presents a robot integrated with advanced sensor systems, artificial intelligence (AI), and autonomous mobility to perform multiple tasks. As a digital concierge, the robot provides enhanced visitor experience with seamless 2-way communication and an integrated touchscreen to connect with site duty personnel. The same screen can double up as an announcement board for advertisements and alerts, thereby extending a virtual front-desk capability effectively. In the security domain, this robot conducts autonomous patrols with real-time video surveillance and A.I.-based anomaly detection. The security head is embedded with a "brain” to perform on-edge computing to detect security-related used cases, significantly improving safety and accuracy in complex indoor environments. For cleaning, the robot can detect over 30 types of waste with 99% accuracy. Its self-adaptive cleaning system adjusts to floor type and debris volume, while a verification mechanism ensures more effective spot-cleaning compared to conventional single-pass robots.  These Multi-Functional Autonomous Facility Management Robot can yield significant operational savings, increase patrol frequency and shorten response time to incidents. This technology offers a software that features plug-and-play solutions that be customised to specific SOPs and needs. The technology owner is looking for collaborators, such as building owners and integrated facility management companies, with use cases to test-bed AI models. Examples include but not limited to identification of suspicious baggage, illegal parking or stray supermarket trolleys.  The robot combines autonomous navigation with real-time AI processing. Its modular design allows for easy customisation based on operational needs. Key components include: Sensor suite featuring 32-beam 3D LiDAR, ultrasonic sensors & cameras for obstacle detection or environmental mapping AI-powered modules for object/person detection, thermal imaging, and anomaly alert Cloud-based dashboard for task assignment, remote monitoring, and analytics Interchangeable task modules for cleaning (e.g., vacuum/sweeper), security patrolling, and data capture Detect over 30 types of waste with 99% accuracy, with cleaning efficiency reaches up to 15,550 m²/hr Customisable module to fit specific applications This multi-functional robotic platform is ideally suited for deployment in environments that require a combination of cleanliness, security, and user interaction, particularly where operational efficiency and manpower optimization are key priorities. Industries and settings include: Commercial buildings: Automates cleaning, performs security patrols during and after hours, and assists visitors Healthcare facilities: Maintains hygiene, monitors for safety risks, and provides non-contact concierge functions Transportation hubs (airports, train stations): Enhances public safety and facility cleanliness at scale Retail complexes and malls: Supports shopper engagement, provides sanitation services, and detects anomalies Educational institutions and campuses: Ensures safe, clean, and welcoming environments Hospitality and mixed-use developments: Offers 24/7 concierge support, patrolling, and environment upkeep The global service robotics market is projected to grow by a CAGR of 30.25%, or $90.4 billion, from 2024 to 2028. This rapid growth will be driven by the continuing integration of advanced technologies such as IoT, A.I., and natural language processing into service robots. Technological advancements in machine learning, adaptive computing, and vision systems will also make service robots increasingly suitable for commercial tasks.  This autonomous multi-functional robot offers a comprehensive upgrade over current facility management solutions by integrating various functions in domains such as cleaning, security, and concierge into a single, intelligent body. This all-in-one solution delivers: Operational cost reduction through task consolidation across different functions, potentially cut cleaning and security manpower cost by 60-70% Faster response from sensing to action with integrated digital concierge, dashboard monitoring and real-time alerts Enhanced safety via advanced 3D spatial awareness Improved service quality without added manpower By unifying execution and intelligence across multiple domains, the robot transforms traditional building operations into efficient, autonomous workflows, bridging the gap between insight and action, delivering a more responsive, self-sufficient, and cost-effective solution for modern facility operations. Autonomous robotics, Integrated facility operations, cleaning automation, security surveillance, AI, robots, multi-function Green Building, Sensor, Network, Building Control & Optimisation, Infocomm, Robotics & Automation, Ambient Intelligence & Context-Aware Computing, Environment, Clean Air & Water, Sensor, Network, Monitoring & Quality Control Systems

This technology uses Machine Learning and AI algorithms to identify what appliances get plugged in to a building and when they are wasting energy. Plug Power represents 40% of the energy in a commercial building. Half of this energy is wasted with appliances left on when nobody is in the building. When wasted energy is found the plugs automatically switch off the appliances wasting energy and turn them back on before people return to the building. The technology not only saves energy and carbon emissions but makes buildings safer by detecting and preventing unsafe energy loads as well as reporting on occupancy and enabling behavioural change with occupants. The technology provider is seeking collaboration partners among businesses operating commercial buildings that utilize plug sockets — particularly those with multiple locations and high energy-consuming appliances. Potential partners include, but are not limited to, retail chains, F&B chains, the hospitality industry, healthcare facilities, education and training centres, and fitness and wellness chains. The technology consists of cloud-based software and plug socket hardware that combined together provide: Real time monitoring and control of all plug sockets Automated machine learning driven wasted energy elimination Appliance ID: Identification of what appliances are plugged in using only energy consumption Full energy, carbon, safety and occupancy reporting suite Advanced occupancy-based AI features: Away for a Day – Automatically turn off desks when nobody is there Is Active – Keep plug sockets on when somebody is still using their desk after hours In built safety system that turns off faulty appliances and overloaded plug sockets before damage is caused Capability to integrate into BMS and external systems Use existing Wi-Fi connectivity, no need to add extra hubs or connectivity hardware Without connectivity, the plugs function as regular sockets and have onboard memory to store data and on/off schedules The technology is used in a wide variety of commercial building types. It is very effective in buildings were there is a distinct pattern of occupancy and appliances are prone to get left on. For example, in commercial offices there is a wide variety of office equipment plugged in and left on with a regular working pattern. Appliances such as computers, monitors, meeting room TVs, water coolers, plug in AC’s regularly get left on or in standby overnight and at weekends. The technology finds these and automatically turns them off when not needed. The technology is currently deployed at scale in various different building types including commercial offices, construction, healthcare, hospitality, education, manufacturing, laboratories and retail. The UVP for the technology is the machine learning models that allow the identification of different appliances and automatic plug socket control to save energy and emissions. The cloud software and socket firmware allow the system to go far beyond a normal ‘smart socket’ and enable a truly intelligent building that saves energy, reduces safety risk and importantly does not require people to manage. AI, Machine Learning, Plug Power, Energy Mnagement, Building Control, Building Safety, Occupant Management Electronics, Power Management, Green Building, Sensor, Network, Building Control & Optimisation, Sustainability, Sustainable Living

Manual built environment inspection suffers from multiple issues such as shortage of manpower, human error and miscommunication. To overcome these issues, there is a need for an automated and centralized inspection system capable of detecting multiple defects of interest and presenting the inspection results in an easy to access format. The technology presented uses data acquired from LiDAR and Cameras mounted on an autonomous robot to inspect building interiors and external facades. The system utilizes an AI engine and can accurately detect defects such as cracks, holes, and other built imperfections stated in building quality guidelines such as CONQUAS. Defect reports can be autonomously generated after the acquired image and LiDAR data has been processed by the AI analytics engine.  The system is composed of an autonomous robot with a mounted camera and LiDAR and has the following features - Support for multiple hardware platforms such as wheeled robots and drones to allow the use of most suitable means of inspection. AI based defect detection for cracks, holes, stains, cornerness, and other structural and visual defects at >3mm unevenness. Capability to inspect for air quality, hazard detection and safety monitoring (PPE Detection). Simple and intuitive user interface with customizable repots minimizing possibility of miscommunication. Generation of defect reports compliant to Singapore Building and Construction Authority recommendations for Built Environment - CONQUAS. Defect and user management system. Capability to integrate with external Building Information Management (BIM) systems and third party apps. The system can be used for digitalization and autonomous inspection of built environments. It covers both the indoor and outdoor inspection by allowing use of multiple robot platforms. With the removal of manual inspection requirements, the system helps improve the consistency and objectiveness of inspections and helps in increasing productivity. The technology is applicaple for tasks related to management of a building from construction to maintenance. By automating and centralizing the built environment inspection, the system improves productivity, significantly reduces the time required by the inspection process, and improves the safetly of the personnel involved. The solution is useful during the entire lifecycle from construction to maintainence and provides automated reports compliant to recommendations of the Singapore Building and Construction Authority. The solution can potentially cut down the time required for inspection by several hours per residential or commercial unit. Building Indoor and Facade Inspection, Built Environment Inspection, CONQUAS, CIS-7, Quality Assessment Electronics, Sensors & Instrumentation, Infocomm, Video/Image Analysis & Computer Vision, Artificial Intelligence

Vision-based Artificial Intelligence (AI) models require substantial time to train, fine-tune and deploy in production. After production, this process is still required when performance degrades and re-training on a new dataset becomes necessary; this maintenance process exists throughout the model's lifetime to ensure optimal performance. Rather than embarking on the time-consuming and painful process of collecting/acquiring data to train and tune the AI model, many organisations have turned to the use of pre-trained models to accelerate the AI model development process. This technology consists of a suite of pre-trained models that are intended to detect food, human behaviours, facial features and count people. These AI models are operable on video footage and static images obtained from cameras. Models are tuned and trained on various use-cases and are accessible via API calls or embedded within software as a Software Development Kit (SDK) library. These models can be deployed as AI as a Service on Microservices platform providing customer data protection with blockchain technology. With customer protection enhanced with blockchain technology, AI Model performance can further be enhanced to meet customer requirement.   The technology consists of a suite of pre-trained AI models that provide high accuracy (over 80%) and can be further customised to improve accuracy and adapted to different use-case scenarios. Models can be integrated in the following ways:  Installed library package embedded within software on-device/on-premise HTTP-based Application Programming Interface (API) calls with video/image data to cloud-installed library package The following are the features for various AI models: Abnormal Behaviour Recognition Continuous monitoring and detection of abnormal human behaviours e.g. fighting, loitering Event Detection Recognises a variety of subjects and events e.g. sports day, graduation, wedding, festival, Christmas, from video footage Optimised for lightweight compute capability (Intel OpenVino) Food (Fresh and Packaged) Recognition Real-time detection of fresh and packaged foods Detects abnormal fresh food or defective packaged food Classifies food types e.g. lotus, spinach, cucumber, radish etc. Optimised for low compute capability Privacy-Preserving Person Recognition Privacy preserved people detection, counting and human activity recognition Images are blurred to preserve private information that can lead to personal identification (irreversible) Optimised for lightweight edge computing Free (Empty) Space Recognition Semantic segmentation to identify empty spaces Customisable for any free-space detection scenario High accuracy in night scenes Safety Monitoring Object detection with prohibited and allowed zones (e.g. person or forklift) Detects and identifies safety risks associated with safety distances Enables audible alarm systems of abnormal situations Wellbeing and Safety Detection Automatically detects and classifies nudity images from images  Enables alerts to be delivered to parent/caregiver's device Customisable to detect new categories of inappropriate content This technology offer comprises a suite of AI models for the following applications: Abnormal Behaviour Recognition Public areas or areas where social order needs to be maintained e.g. food & beverage, entertainment establishments Event Detection Automatic creation and/or organisation of media content i.e. photo classification Automated adjustment of device hardware parameters e.g. audio, colour, brightness when displaying specific types of content e.g. sports Food (Fresh and Packaged) Recognition Food stock level detection, food inventory management Automatic detection of fresh/packaged goods within a constrained area Privacy-Preserving Person Recognition Privacy protection of visual information, in high traffic areas, without deterioration of video quality Free (Empty) Space Recognition Vehicle position localisation on roads Navigation (free-space localisation) in partial/fully self-driving automotive vehicles Identification of free storage spaces in the logistics industry Safety Monitoring Automated compliance checks Workplace safety analysis and tracking Wellbeing and Safety Detection  Parental control in browsers, smartphones or other image storage devices e.g. Network Attached Storage (NAS), Solid State Drives (SSD) AI Models were rigorously tested in the fields of different scenarios. The microservice platform where AI Model ingest the visual data streams offers a secure customer data protection and privacy using blockchain technology. Making this Microservice platform capable of tracking customer’s data usage and thus ensure privacy when AI model operating on the platform are simultaneously improved using unique customer data captured on customer’s premise. Accelerate AI development - eliminate the need for dataset creation, annotation, tuning and testing Customisable AI models - fine-tuned to environment and condition Operational support to continuously improve AI accuracy from newly collected data   event detection, abnormal human behaviour recognition, safety monitoring, food package detection, food freshness, nudity detection, empty space Infocomm, Video/Image Analysis & Computer Vision, Video/Image Processing, Artificial Intelligence

The Make Invisible Muscle Visible (MIMV) System is a non-invasive platform designed for full-spectrum muscle activity monitoring with high spatial and temporal precision. At its core lies a 20-channel high-density surface electromyography (HD-EMG) sensor array that wraps around a limb—such as the arm or leg—like a flexible brace, enabling comprehensive capture of both superficial and deep muscle activity. Real-time amplification and analog-to-digital conversion are embedded within the wearable unit, eliminating signal degradation and motion artifacts. This allows dynamic, wireless monitoring via Wi-Fi during actual movement, setting a new benchmark for muscle analytics in both research and applied settings. What sets the MIMV System apart is its ability to estimate deep muscle activation from surface-level data, enabled by a proprietary model-based signal processing algorithm. This makes the technology exceptionally versatile for diverse use cases—from analyzing intricate muscle coordination in elite athletes, to diagnosing and rehabilitating patients with neurological or movement disorders, to preserving and transmitting expert motor skills in traditional craftsmanship and skilled labor training. To maximize its potential impact, the technology owner is actively seeking collaborative partners such as clinical researchers in neurology and rehabilitation, companies developing wearable medical or sports performance technologies, and organizations dedicated to skilled labor education and movement training. These partners will play a critical role in co-developing real-world applications, validating performance in clinical and athletic contexts, and accelerating market deployment across healthcare, sports science, and vocational training sectors. 20-channel EMG sensor array adaptable to limbs (arm, leg, thigh) - enabling the measurement of muscle activity from all directions  Real-time signal amplification and A/D conversion within the wearable device Wireless transmission via Wi-Fi for untethered, high-fidelity monitoring Model-based estimation algorithm reconstructs deep muscle activity Compact, lightweight, and suitable for dynamic use in clinical, athletic, and training environments Sports and Fitness: Integrated into wearable performance tech to optimize elite athletic training and reduce injury risk Healthcare and Rehabilitation: Diagnostic and rehabilitative use for neurological and musculoskeletal conditions such as stroke, dystonia, and Parkinson’s disease Craftsmanship and Skilled Labor: Capture and documentation of expert motor skills for training, simulation, and education The growing global focus on athletic performance, movement disorders, and remote diagnostics is driving demand for intelligent, wearable muscle assessment tools. The MIMV System offers a scalable solution that aligns with healthcare digitalization, the quantified-self movement in sports, and industry 4.0 training platforms. The MIMV System offers a breakthrough in muscle activity monitoring by enabling full-circumference, high-resolution measurement of both superficial and deep muscles in real time. Unlike traditional EMG systems that are limited to surface data and often rely on wired setups, the MIMV System features a 20-channel wearable sensor array with integrated amplification and wireless transmission. This allows it to capture clean, synchronized data even during dynamic movement. Its model-based algorithm reconstructs muscle activation patterns based on anatomical structure, enabling non-invasive estimation of deep muscle activity—something conventional systems cannot achieve. With its compact, user-friendly design and comprehensive data output, the MIMV System is ideally suited for applications in sports science, clinical rehabilitation, diagnostics, and the transfer of skilled motor techniques. Why the MIMV System Stands Out: Captures both surface and deep muscle activity in real time Wearable and wireless, suitable for movement and dynamic use Accurate and clean signals with built-in processing (even during complex motion)  Enables non-invasive insight into deep muscle activation patterns (not achievable with traditional methods) Supports sports training, clinical rehab, diagnostics, and skill learning Muscle activity monitoring, EMG, Noninvasive Full-spectrum Muscle activity monitoring, Neuromuscular disorders, High-density surface electromyography (HD-EMG), Sports Science Energy, Sensor, Network, Power Conversion, Power Quality & Energy Management, Healthcare, Diagnostics

The Internet has become the de-facto medium for many enterprises to carry out their business functions. By relying on public-key encryption to ensure confidentiality and authenticity of data, employees and customers are able to use a variety of public channels via web browsers, emails and mobile apps to send and receive sensitive information securely. However, this promise of confidentiality and authenticity is being compromised with the advent of quantum computers. With the potential rise of exponentially powerful quantum computing, current data encryption algorithms are not resilient enough for such hidden quantum cyberattacks, specifically harvest-now decrypt-later (HNDL) attacks, resulting in data leaks and undermining privacy. The technology owner has leveraged on their proprietary post-quantum cryptography (PQC) implementation to develop a software module to provide and enhance existing end-to-end data encryption, ensuring resilience to quantum cyberattacks while maintaining confidentiality and authenticity of data. By utilising Key Encapsulation Mechanism (KEM) and JavaScript, it is compliant with evolving cybersecurity standards while being lightweight and dynamic enough to be loaded and executed without installation or configuration. This enables the technology solution to be flexible, scalable and user-friendly. The technology owner is currently working with an organisation to further develop industrial applicable solutions. The technology owner is seeking collaboration partners, such as system integrators, independent software vendors, solution providers and end-users, who require an enhanced and compliant data encryption for the finance, government and healthcare industries. The technology solution, in a form of a software module, leverages on their proprietary PQC cryptographic implementation to secure confidentiality and authenticity of data. Some specifications of the module include: Compliance to National Institute of Standards and Technology (NIST) PQC encryption method Utilises combination of ML-KEM-768 (FIPS 203) and AES FIPS 197 algorithms for secure key establishment Client-side JavaScript library with possible enterprise integration with hardware security modules (KSM), e.g. OTP device No installation or configuration required Optimised to run dynamically on devices and platforms With the above specifications, this quantum-resistant software solution has the following features: Supports and enhances existing end-to-end data encryption to be quantum resilient User friendly with processes being executed at backend. Does not require technical expertise to deploy, use and maintain Flexibility in integration to new or existing infrastructures Lightweight and scalable for fast and rapid deployment With the potential rise of quantum cyberattacks, especially HNDL attacks, private information and data can potentially be vulnerable and compromised. Hence, any application transmitting sensitive data via web browser or mobile applications requiring heightened cybersecurity will greatly benefit from it. Examples of such applications include, but not limited to: Digital interaction and conversations with customers Customer portal for transaction and filing purposes Secure file transfer and email to via internet or intranet Digital entry requiring sensitive information Online transaction requiring private information The software module protects against quantum threats, such as HNDL attacks, by leveraging on their proprietary PQC encryption implementation, ensuring secure end-to-end encryption of data between web browser and organisation. Utilising a hybrid combination of ML-KEM-768 and AES FIPS 197 algorithms, it provides compliance to NIST standards to be quantum resilient. With the solution deployed on JavaScript, it is dynamic and integrates seamlessly with existing infrastructure backend, maintaining existing end-user experience. The software is efficient and resource light while being scalable. With the adoption of such technology solution, it enables organisation to pre-emptively fortify their digital security for an incoming post-quantum world. Post-Quantum Cryptography (PQC), Quantum Cyberattacks, Cyberattacks, Cryptographic Encryption, Web Page Security, Harvest-Now Decrypt-Later (HNDL), Key Encapsulation Mechanism (KEM), JavaScript Infocomm, Security & Privacy

Reimagining how healthcare data is analyzed, this platform enables advanced data science without moving a single byte of patient data. Designed from the ground up to comply with global privacy regulations, it allows hospitals and research institutions to develop powerful AI models using real-world clinical data—while keeping that data entirely on-site under governance. Powered by proprietary federated learning and a modular AI/ML development toolkit, the platform solves one of healthcare’s biggest bottlenecks: secure access to data for innovation. With seamless integration into existing IT systems and end-to-end compliance with GDPR and HIPAA, this is the infrastructure for the future of privacy-first, multi-institutional healthcare data analytics and AI/ML. The technology owner is actively seeking collaboration with healthcare providers, research institutions, clinical research organizations, and data-centric health-tech companies eager to scale innovation—without compromising privacy or control. The platform is an advanced, modular software framework comprising secure data integration pipelines, federated learning capabilities and real-time analytics tools. Its key components include: Federated Learning Infrastructure: Enabling multi-centre data collaboration without transferring sensitive data. AI/ML Model Development Suite: Customizable tools for predictive analytics tailored to healthcare needs. Interoperability APIs: Seamless integration with existing health IT systems. Advanced Security Protocols: End-to-end encryption and compliance with global data protection standards. The platform is a versatile platform purpose-built to power a broad spectrum of data-driven healthcare applications. It supports not only advanced medical research, but also the development of AI/ML solutions for diagnostics and clinical decision support across any medical domain, as well as AI-powered population health management and precision medicine initiatives. Its capabilities extend to clinical trial analytics—such as simulation, and optimization — and the integration of complex healthcare datasets, including genomic and real-world data. With its privacy-first federated learning architecture, the platform enables secure, multi-institutional collaboration without transferring or centralizing sensitive data. This makes it especially valuable for high-impact use cases like rare disease research, where combining insights from multiple centers across the globe can overcome single center data limitations and drive meaningful discoveries. The platform has been successfully deployed at Seoul National University Bundang Hospital (SNUBH) to support the development of real-world clinical AI models—demonstrating its readiness for mission-critical healthcare environments. One key application was in building an AI model to predict comorbidity risks in patients with type 2 diabetes, enabling proactive clinical intervention for conditions such as cardiovascular disease, kidney failure, and diabetic retinopathy. In a separate initiative, the platform powered the development of an AI-based antibiotic stewardship model—replacing what is typically a manual, labor-intensive process. Traditionally, stewardship programs require clinicians to retrospectively review patient records to detect inappropriate antibiotic use. With the platform, diverse clinical datasets were securely integrated and analyzed on-site, allowing the AI to flag high-risk patients in near real time. The result was a significant reduction in manual workload, faster clinical decision-making, and improved program scalability. These real-world deployments underscore the platform’s ability to accelerate healthcare innovation while maintaining full data privacy and governance.   Privacy-Preserving by Design: Enables AI development and collaboration without transferring or centralizing patient data — ensuring full compliance and trust. Built for Real-World Healthcare with Flexibility and Scalability: Deployable on cloud or on-prem, Enobase adapts to diverse data environments and project needs — scaling effortlessly to support anything from single-site pilots to multi-institutional collaborations. Accelerates Scalable Innovation: Powers rapid development and deployment of AI for diagnostics, population health, and clinical decision support — across institutions and borders.     Healthcare Data Science, Federated Learning, AI/ML, Data Privacy, Medical Analytics, Secure Data Infrastructure, Data Platform, Cloud Agnostic Healthcare, Medical Devices, Telehealth, Medical Software & Imaging, Infocomm, Healthcare ICT