TECHINNOVATION TECH OFFERS

Critical facilities and infrastructures face growing risks from equipment failures, costly downtime, and safety hazards, while traditional inspections often lack the speed and accuracy required to address these challenges. This innovation combines physical non-destructive testing (NDT) with AIoT-driven predictive analytics to deliver continuous, real-time monitoring that enhances safety, efficiency, and resilience. Engineered with business continuity and rapid incident response at its core, the system detects early anomalies, prioritizes risks, and enables proactive maintenance to reduce disruptions and ensure compliance. Its key advantage lies in a proprietary dataset of over 10 million hours of real-world operational data from HVAC, motor, and pump systems in metropolitan environments, enriched with expert domain labelling. This unique resource powers machine learning models with superior accuracy, outperforming conventional predictive tools that lack real-world grounding. The platform is also the first industrial transformer-based multimodal AI system, integrating diverse sensing modalities with unmatched precision. Its scalable, modular design supports multi-sensing, multi-modal applications across diverse sectors. By shifting from reactive or scheduled maintenance to predictive, condition-based asset management, the solution bridges gaps left by inspections and supervisory control and data acquisition (SCADA) systems, resulting in safer operations, optimized resource use, and measurable ROI. The technology owner is seeking collaboration with industrial partners, including property owners, operators of power plants and utilities, transportation providers, government agencies, and industrial facility managers, who aim to minimize downtime, extend asset lifecycles, and strengthen resilience against failures.

Bioplastics have emerged as a sustainable alternative to conventional petroleum-based plastics, offering biodegradability and reduced carbon footprint. However, their use in high-performance applications remains limited because of inherent material weaknesses. A key challenge is their poor barrier properties, particularly against water vapour and gases such as oxygen and carbon dioxide. This limitation prevents bioplastics from being widely adopted in packaging applications that demand strong protective qualities, such as food products, pharmaceuticals, and sensitive electronic components. In most cases, bioplastics are restricted to low-demand items like disposable bags or cutlery, where barrier performance is not critical. This technology addresses the key challenge of poor barrier properties by introducing a plant-waste-derived additive that enhances barrier properties of bioplastics. Incorporated directly during melt processing, the additive reduces the water vapour transmission rate (WVTR), enabling bioplastics to provide effective moisture protection. Because the additive is derived from upcycling of plant waste, it reinforces the sustainability narrative while aligning with circular economy principles. This technology also functions as a drop-in solution compatible with existing manufacturing processes, allowing packaging producers to adopt the technology without costly modifications. The technology owner is interested in co-development R&D opportunities and out-licensing of the developed IP with companies developing sustainable bioplastic products with enhanced barrier properties.

In today’s rapidly evolving business landscape, digital transformation has become a strategic imperative for companies across industries. At the core of this transformation lies Artificial Intelligence (AI)—a technology that is increasingly recognized as a key enabler of innovation, operational efficiency, and competitive advantage. However, despite its transformative potential, AI adoption remains a challenge for many organizations. High development costs, specialized expertise requirements, and complex deployment pipelines often limit AI accessibility to large enterprises with dedicated AI engineering teams. Vision-based AI models, in particular, require extensive training, fine-tuning, and maintenance. Even after initial deployment, continuous retraining are necessary to ensure consistent performance, resulting in substantial costs and resource demands. To overcome these challenges, the technology owner has developed a suite of pre-trained, customisable, and continuously learning AI models that enable rapid deployment for automated visual inspection. Delivered through a modular AI platform, the solution empowers customers to build, customise, deploy and scale AI inspection solutions cost-effectively, without requiring deep AI expertise. The AI models can process both video footage and static images from conventional camera systems, transforming them into intelligent, AI-powered inspection tools adaptable to diverse use cases. The technology is available for R&D collaboration, licensing, and test-bedding with industry partners, including system integrators, manufacturers, and inspection service providers.

The global push for sustainability is driving demand for innovative solutions to reduce waste and conserve resources. While the focus has often been on synthetic materials like plastics, millions of tons of agricultural waste remain underutilized. Instead of being landfilled or incinerated, this renewable feedstock offers a major opportunity to support a circular economy and lessen dependence on virgin resources. This technology is a proprietary, chemical-free process that converts agricultural by-products into durable, eco-friendly materials. By harnessing diverse agricultural waste streams, this process yields thin plates and modular elements that can replace conventional raw materials in applications such as roofing, flooring, furniture surfaces, and wall furnishings. Designed for circularity, these materials can be broken down and reintroduced as feedstock at the end of their lifecycle, minimising waste and maximising resource efficiency. The technology owner is actively seeking R&D co-development and out-licensing of the developed IP to companies intersted in advancing sustainable materials and scaling this circular economy solution. 

Rising energy consumption and electricity costs pose significant challenges for businesses across all sectors, from light commercial operations to heavy industries. Moreover, sustainability has become a crucial component of corporate strategy. Electrical energy optimisation is not only about cost savings but also about resource conservation, power stability, equipment protection, and long-term sustainable development. The technology owner has developed a transformer-based voltage optimisation solution to reduce energy consumption and billing costs, optimise electrical power supply, extend equipment lifespan, and lower carbon emissions. This technology allows electrical equipment to run at an optimal voltage level while keeping the current within the optimum range for best efficiency, providing an immediate and practical way to reduce consumption and delivering energy savings. Industrial Internet of Things (IIoT) is integrated within the equipment to capture data and users have 24/7 access to a cloud-based platform to monitor, evaluate and make informed decisions on their power and energy usage as well as perform carbon reporting. The technology owner is keen to collaborate with industrial partners such as building management, property owners, industrial facility management in manufacturing sectors, equipment builders, energy consultants etc. The technology is also available for licensing to OEM partners to further co-develop by integrating into building management systems (BMS) and other solutions.

With the rapid growth of electric vehicles, renewable energy storage, and high-power electronics, the demand for reliable battery thermal management systems (BTMS) is surging. Increasing energy densities in lithium-ion batteries intensify risks of overheating, safety hazards, and reduced lifespan, underscoring the need for advanced cooling solutions. To address these challenges, a novel fabrication technique has been developed to produce a flexible, leak-proof, thermally conductive, and electrically insulating composite. This material combines a polymer matrix, phase change material (PCM), and thermally conductive fillers. Unlike conventional approaches and passive cooling methods, the technology employs a low-temperature solvent evaporation process using styrene-butadiene-styrene (SBS), paraffin (PA), and expanded graphite (EG), resulting a thermally enhanced flexible composite phase change material (FPCM) designed for external thermal management of Li-ion batteries. This process enables improved dispersion, strong interfacial compatibility, and structural integrity while significantly reducing energy consumption during fabrication. The optimized FPCMs demonstrate enhanced thermal conductivity (up to 1.38 W/m·K), robust flexibility under mechanical deformation and excellent phase change stability. Thermal performance tests on lithium-ion batteries under various charge–discharge conditions showed up to 17 °C reductions in peak battery temperature and improved capacity retention at high C-rates. It proved the FPCM’s reliability, scalability, and energy efficiency for advanced BTMS applications, particularly in environments demanding mechanical adaptability and high safety standards. The technology is available for R&D collaboration, licensing, and test-bedding with industry partners such as battery manufacturers, suppliers, and BTMS system integrators.

This technology is an AI-powered logistics orchestration platform designed to optimize warehouse management, order fulfilment, and transportation across distributed networks. It integrates advanced algorithms for demand forecasting, route optimization, and real-time inventory visibility. By connecting multiple warehouse operators and transportation providers into a unified system, the platform enables businesses to access flexible, on-demand logistics capacity without the need for owning physical assets. The system is built with modular APIs, allowing seamless integration with e-commerce platforms, ERP systems, and third-party logistics providers. Its scalable architecture supports rapid deployment in diverse market environments, making it suitable for businesses facing seasonal demand fluctuations or seeking cross-border operational efficiency. 

Conventional solar monitoring at the inverter level is reactive and labour-intensive, with issues like shading, soiling, mismatch, or degrdation often detected only after energy losses occur. Troubleshooting requires manual checks, slowing response times and driving up costs.  This technonlogy provides a digital O&M control plane that delivers real-time, module-level visibility and control. Coupling module-level power electronics with advanced software, it captures second-by-second data on voltage, current, temperature, and faults from each module. The platform integrates with existing inverter and SCADA systems, is inverter- agnostic, and can be retrofitted selectively where risks or losses are the highest.  By detecting anomalies early, quantifying avoided losses, and prioritizing interventions by ROI, the technology turns monitoring into proactive operations. Operators can execute remote module-level actions, such as isolating or restoring modules - before sending crews, reducing downtime and cost. Built-in safety features also meet rapid shutdown requirements and generate auditable compliance records.  By closing the loop from sensing to analytics to remote action, this technology maximizes energy yield, accelerates response, and lowers the total cost of solarplant operations. In real-world deployments, the system has achieved up to a 50% reduction in management and O&M costs, and a 4–15% increase in overall power generation efficiency, while significantly strengthening fire-safety assurance through rapid isolation of faulty panels or hotspots. The technology owner is seeking collaborations with asset owners, O&M service providers, and EPCs/developers for test-bedding and licensing. 

Enterprises today face mounting challenges from legacy systems, fragmented data silos, and complex integration issues that slow down digital and AI initiatives. This AI-powered automation platform transforms integration from a technical bottleneck into a strategic accelerator, enabling organizations to unlock trapped data assets worth billions. By automating up to 70% of manual coding, the platform accelerates processing speeds by up to 10x while reducing operational costs. Its visual drag-and-drop interface democratizes data access, addressing the $5.5 trillion global skills gap by removing the need for specialized coding expertise. Enterprises can cut data preparation efforts from 80% to 20% of project cycles, connecting legacy mainframes seamlessly with modern cloud applications. The urgency is clear: the data integration market is projected to reach $47.6 billion by 2034, yet 95% of IT leaders cite integration as the main barrier to AI adoption. Meanwhile, silos cost enterprises hundreds of thousands annually, and up to 80% of data projects fail due to poor preparation. This platform empowers organizations to process millions of records in minutes across hybrid, cloud, and on-premises environments. Early adopters already report 171–295% ROI within three years, six-month payback periods, and over $2.4 million in productivity gains. With 70% of new applications expected to leverage low-code/no-code by 2026, this solution positions enterprises to lead the next wave of intelligent automation.