TECH OFFERS

Agarwood (Aquilaria malaccensis) is a fragrant dark resinous wood used in the production of oud oil through distillation. Agarwood and its by-products are associated with many cultures and religious purposes in Middle East, China, Japan, India, etc. They are widely used to produce incense, perfume, medicine, TCM medicine, teas, complimentary health products and more. This technology offer encompasses a distillation process that provides the highest quality of oud oil in terms of purity and density from agarwood. The technology owner is interested to do R&D collaboration and co-development activities with partners from various application fields, e.g., the development of novel complementary health products containing oud in the areas of wellness, medicinal, cultural, lifestyle, and product innovation relating to its by-products or waste. With this distillation process, the agarwood is able to deliver a higher level of agarotetrol. Agarotetrol is the main active constituents of agarwood which contributes to the fragrance of agarwood. In a recent study, comparing the sample from the same region and result stated in Chinese Pharmacopoeia 2015, this technology was found to have 6.4 times higher agarotetrol concentration. The positive finding entices more strategic collaborators for oud oil related product development. The technology provider is currently working with research institute on developing the validation methods, certification, and standards for agarwood products. This platform assures ongoing consultation between the tech provider and partners so as to achieve consistent and quality products that the consumers desire. The primary application area of this technology will be for medicinal purposes. Agarwood and oud oil has long been known for its healing powers in TCM and traditional healing ingredients for heart-saving pills, anti-stress, impactful healing effects to skins, organs and mental wellness. The technology can be deployed for the following applications: Cosmetics, Personal Care Products Dietary Supplements Teas Nutraceuticals Versatile applications and at the same time to obtain multiple benefits in one product Ready and consistent supply of agarwood chips with exclusive licensing rights Proprietary distillation process for high purity oud oil Sustainable farming through reforestation and low-carbon emission Personal Care, Cosmetics & Hair, Healthcare, Pharmaceuticals & Therapeutics

Image annotation is a critical step in developing computer vision and image recognition systems. Image annotation can be used in applications in a spectrum of deep-tech pillars such as Healthcare/Medical (detecting and diagnosing diseases from radiology or pathology images), Manufacturing (defect detection from image scans), Agritech (plant/crop health check via images and photos), and more. As a result, image annotation is critical in developing Artificial Intelligence/ Machine Learning (AI/ML) models in a variety of fields. The role of image annotation in deep learning has changed over time. Today, image annotation has become more important for object recognition with new characteristics and capabilities in real-world settings. However, manual labeling of complex objects continues to be time-consuming, tedious, and error-prone - additionally, outsourcing these labeling tasks might not always be the best way due to domain-expertise required in labeling complex image data e.g. radiography images or surface defects on semiconductors. This technology offer is an AI-assisted image labelling tool that enables technical teams to collaboratively, quickly, and easily label large image datasets with pixel-level accuracy masks. As the tool is industry agnostic, it can be used by any industry with a minimal learning curve. The technology owner is keen to work with companies who are looking to build out datasets / ground-truths / and labels for building deep-learning experiments and capabilities, through R&D collaboration and licensing opportunities. The features of this technology are as follows: AI-Assisted Labelling Most datasets are huge with complex geometries. The AI-Assisted labelling feature within the tool uses a mixture of contour analysis methods and deep-learning to label these datasets within a few clicks per image with pixel-level accuracy. Users just need to click on the area of interest and the corresponding masks around the object of interest will be automatically generated. Industry Agnostic The image labelling process is industry agnostic and works with most 2-dimensional (2D) RGB image types (JPG, PNG, etc) - the tool has already been used in computational pathology, manufacturing, and inspection use-cases. General Specifications Works on 2D RGB Images (or converted from other spectrums) Supports Polygon, Bounding Box, Mask labels Exportable to major annotation formats e.g. COCO JSON, LabelMe, PascalVOC, COCO MASK, CSV Width-Height, etc Supports immediate model training with MaskRCNN, DeepLabV3 with "One-Click Train" feature One of the issues faced by researchers, machine learning, and data scientists is that labeling data can be tedious and time-consuming. The tool seeks to help them label data much faster - using only a few clicks to generate near pixel-perfect masks for your data.  Teams have been able to label thousands of medical images within a week using our automated segmentation algorithm and fully-online tool to improve collaboration amongst the labeling team and supervisors.  Computational Pathology and Medical Imaging Disease Detection and Identification (Tumour Lesion, Fracture, Foreign Objects) from X-Ray / MRI Machines Anomaly Detection in Blood Cell Scans / Pathology Scans Manufacturing  Multi-Class Materials Defect Detection (Semiconductors, Materials, Fabrication Quality Assurance, etc) Assembly Parts Identification and Counting Material Defects Labeling  Cracks / Rust / Anomaly Labelling from X-Ray / RGB Images Agriculture and Food Technology Crops and Food Grading Crops / Trees Labeling Food Inspection Data Labeling Others Construction Site Management Smart City Use Cases (Human Monitoring, Crowd Controls, Carpark Capacity Scanning System) The global data annotation tools market size was valued at USD 629.5 million in 2021 and is anticipated to expand at a compound annual growth rate (CAGR) of 26.6% from 2022 to 2030. The growth is majorly driven by the increasing adoption of image data annotation tools in the automotive, retail, and healthcare sectors - the demand for annotation tools is soaring because of the need to reinforce the value of data in these industries. Additionally, the global data collection and labeling market size was valued at USD 1.67 billion in 2021 and is expected to expand at a compound annual growth rate (CAGR) of 25.1% from 2022 to 2030. The market is expected to witness a surge in the adoption of the technology owing to benefits such as extracting business insights from socially shared pictures and auto-organizing untagged photo collections. It also contributes to developing enhanced safety features in autonomous vehicles, such as condition monitoring, terrain detection, wear detection, and emergency vehicle detection. Machine Learning has been incorporated in various industries, including facial recognition on social networking websites, automated picture arrangement on visual websites, and robotics and drones. Helps teams label data much faster - an AI-enabled segmentation tool that uses only a few clicks to generate pixel-perfect masks for your data. Completely online, no additional plug-ins are required - labelling is done in a web browser, and can be performed collaboratively within the team or with an offshore workforce Medical data for example, require years of expertise to label correctly - this tool allows professionals such as doctors, and researchers to label data 10 times faster. Does not require learning any proprietary software (minimal learning curve) Allows teams to quickly train powerful AI models after data-labeling for an end-to-end workflow as images are taken as inputs, and trained deep-learning models are generated as outputs (FasterRCNN, MaskRCNN, DeepLabV3, YOLO etc) Supports the generation of labeled files for multiple popular frameworks Artificial Intelligence, Deep-Learning, Data Labeling, Machine Learning, Computer Vision, Instance Segmentation, Medical Imaging, Defect Detection Infocomm, Video/Image Analysis & Computer Vision, Artificial Intelligence

Despite the increasing prevalence of obesity, all FDA-approved medications, which act indirectly on the central nervous system to suppress appetite or on the gastrointestinal tract to inhibit fat absorption, suffer from poor effectiveness and side effects. Most of these medications have been withdrawn from the market. Although liposuction performed in clinics can effectively remove targeted subcutaneous fat, it suffers from invasiveness, high costs, associated risks, induction of compensatory increase of visceral fat. Although thermal lipolysis induced by high-power laser energy is a non-invasive way to reduce subcutaneous fat, its effectiveness is limited and it often causes skin burning. Both liposuction and laser lipolysis cannot improve whole-body metabolism. The technology owner has developed a transdermal mild photothermal therapy directly acting on the root of evil, i.e. subcutaneous fat, to induce its ameliorating remodelling (browning, lipolysis, angiogenesis, and apoptosis), based on the injectable hydrogel encapsulated with photothermal agent. Browning refers to the conversion of energy-storage white fats into energy-burning brown fats. Further, combining with pharmaceutical therapy by codelivery of pharmacological agent leads to a strong therapeutic synergy. This method not only ensures high effectiveness and low side effects due to localized and targeted application but also remotely creates significant improvements in whole-body metabolism (e.g., reduction of visceral fat, relief of diabetic symptoms). In addition, this technology is applicable for cosmetic purposes, e.g., body contouring and reduction of double chin. The technology owner is seeking potential biotechnology companies, clinicians, and other partners to clinically translate and commercialize this technology. Possible modes of collaboration include R&D, process, and product development. Photothermal agents and pharmacological agents are encapsulated in biocompatible and biodegradable hydrogel which retains the therapeutics to ensure sustained effects. After being injected into subcutaneous fat depots (e.g., belly fat) using an insulin needle or an automated injection device with minimal pain, NIR irradiation at each injection site will be applied for only five minutes using a portable laser source, one to three times a day for several days without the need of another injection. This described procedure (injection + laser treatment) can be applied once or several times. Obvious mass reduction of the treated fat and other beneficial effects on the whole-body should be resulted. In contrast to laser lipolysis, this procedure shall be much more effective, pain free without causing skin burning, and profoundly beneficial. It can be conducted in clinics by professionals or be self-administered at home for long-term care. The primary application area of our technology is for treating obesity and associated metabolic diseases (e.g., type 2 diabetes). This technology can also be used for non-therapeutic or cosmetic purposes, including contouring, sculpting, or slimming one or more regions of the subject’s body for a desirable appearance. To be specific, this technology can locally remove stubborn fat below the chin, or in thigh, abdomen, thorax, flank, upper limb, upper body, lower limb, back, etc.   As highlighted by Mordor Intelligence, the anti-obesity drugs market was valued at about US$ 1,690 million in 2020, and it is expected to reach US$ 4,250 million in 2026, registering a CAGR (compound annual growth rate) of 15% over the forecast period, 2021-2026. And according to IMARC Group, the global body contouring market reached a value of US$ 7.3 billion in 2021, and is expected to reach US$ 11.1 billion by 2027, exhibiting at a CAGR of 6.9% during 2022-2027. This technology provides an unprecedented solution with high effectiveness and low risks. Non-invasive targeted treatment on subcutaneous fat Drastic reduction of subcutaneous fat and visceral fat Relief of obesity-associated metabolic diseases (e.g., diabetes) Low risks and low side-effects High effectiveness Self-administrable Obesity, Diabetes, Metabolic Diseases, Transdermal Therapy, Personal Care, Slimming Healthcare, Medical Devices, Pharmaceuticals & Therapeutics

Singapore is currently only producing 13% of its vegetable consumption. With little farming land available, Singapore relies heavily on imports from other countries. Due to increasing focus on food security, the alternative to solve land scarcity problem is to build greenhouses on concrete rooftop. Although concrete rooftop greenhouse are able to keep pests out, there is a signifcant heating problem which severely inhibits the growth of the vegetables. Therefore, there is a need for a rooftop greenhouse that is able to actively cool itself to avoid such problem. This technology offer is a modular rooftop greenhouse farming system (hydroponics) capable of producing vegetables on concrete roofs to meet the local demand while reducing over-reliance on imports. The design of the greenhouse farming system enables cooling and does not heat up, thus allowing the growth of pest-free vegetables. The system is approximately the size of a typical carpark lot (2.5 x 5 m). The production rate is 30 kg per month (2.5 x 5 m size) and requires minimal human intervention. The technology offer comprises both the farming system and its operation know-how. The modular rooftop greenhouse farming system can be set-up within 3 days or scaled-up when required with guaranteed vegetable growth. The break-even cost of one greenhouse is about 3 years. The technology owner is seeking to out-license their technology. This technology offer is a temperature regulated and modular farming system (hydroponics) for rooftop farming. The features and specifications are as follows: Modular and scalable Flexible sizes (as small as 2.5 x 5 m) 30 kg/month (2.5 x 5 m size) Active cooling design (20% reduction in temperature) Passive system operation (minimal manpower) Anti-pest  Quick setup (3 days) Applicable for wide range of crops The main application for this technology is for those that are interested in rooftop farming. The potential applications are: Conversion of barren and unused concrete space into temporary/permanent arable land (eg. Carpark rooftops, schools, apartments, factories and floating platforms etc.) Can be applied to high-value crops Minimal human intervention is needed as the modular greenhouse farming system encompasses Internet of Thing (IoT) and automation Ability to scale-up immediately greenhouse, vegetable, urban farming, rooftop farming, bak choy, deep water culture hydroponics system, hydroponics, pesticide-free, farming IoT, farming automation Life Sciences, Agriculture & Aquaculture, Sustainability, Food Security

In recent years, there has been an increasing demand for carbon dioxide (CO2) adsorbents due to climate change. These materials can be used for CO2 capture in both flue gas and directly from the air which can mitigate and reduce greenhouse gas (GHG) emissions. The current conventional CO2 adsorbents includes alkaline salts, aqueous amine solution and metal organic frameworks (MOF). However, these materials are expensive (MOF) and suffers from problems such as heat generation (alkaline salt) to energy intensive post-adsorption recovery (aqueous amine solution) which severely limits its wide scale adoption. This technology offer is an amino-based resin adsorbent for low concentration and ambient temperature CO2 adsorption and desorption. This adsorbent is capable of adsorbing low concentrations of CO2 in air at room temperature and generates little heat when adsorbing CO2. It is also possible to capture CO2 from flue gas in the same manner as well. In addition, the regeneration (release of CO2) of the adsorbent can be performed at low temperature with significantly less energy consumption than existing materials. This technology offer is an amino-based resin adsorbent for low concentration and room temperature capture of CO2. The technical features and specifications are as follows: Porous amino-based resin Easy to handle granules High affinity CO2 chemisorption Low concentration and temperature CO2 adsorption (as low as 400 ppm and at room temperature) Desorption is possible at lower temperatures than existing materials (30 oC or higher) Environmentally friendly (non-toxic, non-volatile) Flexible implementation design (filter parts, filing columns etc.) The use of this technology is for industries who are interested in CO2 capture and/or utilisation. The potential applications are: - Scenarios for CO2 Capture Air conditioners (passive CO2 capture and indoor CO2 concentration control) Manufacturing and other CO2 emitting industries (removal of CO2 from pre-combustion or flue gas) - Scenarios for CO2 Utilisation Beauty applications (promotion of blood circulation by use of CO2) Agriculture application (promotion of growth by use of CO2)  Low concentration and room temperature CO2 capture Desorption is possible at lower temperatures than existing materials (30 oC or higher) Environmentally friendly adsorbent (non-toxic, non-volatile) Flexible use case (direct air capture, flue gas capture) This technology owner is keen to out-license this patented technology, or to do R&D collaboration utilising the adsorbent material with partners who are interested in CO2 capture and/or utilisation. Direct air capture, Flue gas capture, CO2, Adsorbent, Amine, Resin Materials, Plastics & Elastomers, Environment, Clean Air & Water, Filter Membrane & Absorption Material, Green Building, Heating, Ventilation & Air-conditioning

Manufacturing plants constantly seek opportunities to save energy, reduce cost, and be more environmentally sustainable. However, achieving these goals often requires heavy expenditure in the form of hiring teams of experienced engineers, who then perform cost-reduction tasks manually - this method is time-consuming, costly, and prone to inaccuracies due to the risk of human error.  This technology offer provides a no-code Artificial Intelligence (AI) powered platform that monitors energy consumption, carbon dioxide(CO2) emission, and operational expenditures (OPEX) in real-time. The AI engine builds a virtual cognitive model (digital twin) of a physical asset, e.g. a manufacturing plant or a piece of machinary. Simulations are carried out on the model to predict operational inefficiency i.e. high energy usage, equipment breakdown, etc. Upon detection of inefficiencies, the engine is able to suggest the best operating parameters to resolve the inefficiency. Monitoring: Tracks real-time operational data through sensor data from every equipment Monitors the lifecycle and performance (energy usage, carbon emission, operational expenditure) Predicts and alerts to potential equipment failures Optimisation: Autonomously optimises the operational variables to prevent operational failures, reduce downtime, energy usage and carbon emission based on a user-defined thresholding value Simulation: Software comprises a simulation capabillity to test if changes in specific operating parameters can cause knock-on issues or increase efficiency The software platform can be deployed securely on-premise, private cloud, or public cloud. The technology can be paired with sensor solutions and 3D modelling software as end-to-end solutions to build digital capabilities in optimising and visualising operations/processes. This technology offer provides an AI-powered cognitive digital twin that is applicable for all types of machinary used in manufacturing operations, and refineries in the following industries: Chemical Oil and gas Pharmaceutical Energy/Power This AI-enabled solution is intended to assist in the autonomous reduction of downtime, operational expenditures, energy consumption, and CO2 emissions.   In comparison with conventional digital twin software which virtually represents physical assets with 3D models, and are commonly used as simulation, prediction, and life cycle monitoring tools. This technology can be differentiated in the following ways: Operates autonomously Does not need to be operated by specialised engineers with technical experience; workforce reduction Is not simply a complementary tool for analysis, operational oversight and decision-making Built-in AI engine acts, makes decisions autonomously to optimise throughput The technology owner is looking to collaborate with machinary owners in the chemical and process industries, as well as original equipment manufacturers (OEM) and digitisation/digital transformation companies. Cognitive Digital Twin, Optimisation, Emission Reduction, Digitilisation, Modelling, Simulation Infocomm, Artificial Intelligence, Computer Simulation & Modeling

This technology offer is technique that can turn renewable cellulose paper feedstock into lightweight carbon foams that are architected into highly complex geometries that cannot be produced through traditional manufacturing techniques, such as closed-cell lattices. These carbon foam lattices exhibited excellent mechanical properties, particularly in energy absorption, as well as good battery characteristics, low thermal conductivity and relatively good electrical conductivity. Unlike most traditional carbon foams that are brittle, paper-based carbon foams can withstand ~ 30% strain before significant deformation sets in. These multifunctional properties, the quick and easy customization of part geometry and the use of green feedstock are expected to be useful for aerospace, automobile, sports, medical and thermal insulation markets, as they search for the next generation of eco-friendly, high-performance materials. This technology is available for R&D collaboration, IP licensing, or test bedding, with partners such as battery manufacturers, supplier to battery manufacturer, space industry, etc. Technology is a 3D printed, highly porous carbon material produced from paper Density = 0.2 – 0.6 g/cc (79 – 92% porosity) Modulus = 4.5 – 700 MPa Max. Compressive Strength = 0.2 – 13.4 MPa Mechanical Energy Absorption = 0.02 – 4.8 MJ/m3; 0.08 – 10 kJ/kg Pseudo-elastic compressive strains up to 30% - 40% All the specifications above can be controlled through geometrical design As anode for Li-ion battery, specific capacity = 65 – 140 mAh/g for >300 cycles Thermal Conductivity = 0.1 – 0.5 Wm-1k-1 Electrical resistivity = 0.002 – 0.04 Wm The potential applications are as follow: Thermal Insulation Fire-proofing Refractory Material Composite Parts Rocket Nozzles Acoustic Tile EMI Shielding Water and Gas Filters Li-Battery Anodes, Aerospace Materials Material is multifunctional and covers several markets, including but not limited to aerospace, automobile, carbon foam batteries, insulation etc. The structural carbon market is ~ USD$4 billion in the US alone. The primary differentiator in this technology is that it uses cellulose paper, a freely available and renewable green resource rather than fossil fuel as a precursor. Next, the technology enables the carbon foams to be additively manufactured into prescribed geometries. Customization of geometry is therefore easy, quick and cheap and machining is not needed. Feedstock is a green, cheap, renewable material – cellulose paper Easy customization of carbon foam geometry with quick turnaround time Thermal, electrical, mechanical and battery performance are in the range of the best performing carbon foams on the market Carbon Foam, Additive Manufacturing, 3D Printing, Green Materials, Cellulose, Battery, Thermal Insulators Manufacturing, Additive Manufacturing

Existing methods for load monitoring typically focus primarily on residential building data, while few look at the effectiveness of such systems for industrial or commercial buildings. Apart from the use of this technology for real-time supply-demand response, such methods can be extended for use in anomaly detection, small-scale load change detection, or an estimation of energy usage, without the associated high costs of sub-metering equipment. The proliferation of neural networks for such demanding tasks solves the computationally expensive problem of traditional methods like Hidden Markov Models (HMM) and fuzzy clustering algorithms. This technology offer is a neural network solution for residential and industrial energy management. It utilises a time-series forecasting tool to predict load, renewable energy generation, and electricity prices, without the need for costly sub-metering equipment. It is based on reinforcement learning algorithms which are trained by rewarding and penalising neural network algorithms for good or bad decisions respectively, the solution is a non-intrusive technique that helps residential and commercial end-users save on energy costs in the open energy market by scheduling their load demand for heating, ventilation, air conditioning (HVAC) systems, washing machines, and charging of their Electric Vehicles (EVs). This technology is an integrated platform that consists of the following components: Non-intrusive load monitoring (NILM) and data analytics tools for smart homes Time series forecasting tool for renewable energy and dynamic electricity pricing Reinforcement learning-based neural network for energy management systems Electricity plan recommendation tool for residential and commercial users Support data imputation - tolerant to missing data by estimating values that are missing Integrates with several types of Deep Neural Network (DNN) models: Long Short Term Memory (LSTM) Bidirectional Short Term Memory (Bi-LSTM) Time Distributed Dense Layer The technology can be deployed for use in smart buildings, smart homes, and for commercial/industrial applications such as smart factories, server farms, etc to enable the following applications: Anomaly detection e.g. fault detection Small load change detection Energy data analytics for energy monitoring Energy disaggregation (addresses the problem of separating the electricity usage into individual disaggregated components) Determine equipment on-off status Non-intrusive load monitoring (NILM) represents a cost-efficient technology for observing power usage in buildings. It tackles several challenges in transitioning into a more effective, sustainable, and digital energy efficiency environment. Compared with existing smart home management systems that use model-based methods and only consider simple objectives, this technology helps to reduce energy costs by shifting electricity load demand to a low electricity price period while ensuring that electricity consumption needs are still met. The technology owner is interested to collaborate with smart building operators, in-home integrated system suppliers, and smart appliance manufacturers to test-bed or collaborate to build new products/services. Infocomm, Big Data, Data Analytics, Data Mining & Data Visualisation, Artificial Intelligence, Energy, Sensor, Network, Power Conversion, Power Quality & Energy Management, Sustainability, Sustainable Living, Low Carbon Economy

Machine Learning (ML) and Deep Learning (DL) have been the primary growth driver of Artificial Intelligence (AI) and has seen widespread adoption in areas such as Computer Vision, Speech Processing, Natural Language Processing, and Graph Search, among many others. It is also well-known that AI both needs and produces large amounts of data. However, traditional data repositories have not scaled effectively to handle the large amounts of vector representations that are common in AI applications - in such cases, searching for similarities across high-dimensional vectors is inefficient. To address such limitations, vector databases have been developed to address the limitations of traditional hash-based searches and search scalability, enabling similarity searches across large datasets. This technology offer is a unified Online Analytical Processing (OLAP) data platform that supports approximate vector search, enabling efficient searching over billion-scale structured data and vector data. The data engine simplifies the process of building enterprise-level AI applications such as search and recommendation systems, video analytics, text-based searches, and chatbots while accelerating the development of production-ready systems. Developers no longer need to deal with complicated scripts to query vector data as low latency, high-performance structured data, and vector data searches are made possible via vector data indexing methods and the use of extended Structured Query Language (SQL) syntax. This technology offer is purpose-built OLAP database, CPU-only implementation with a built-in vector query engine that uses extended SQL statements for data querying. Supported data include structured data (tabular text, numbers, dates, times) and unstructured data (image, video, audio) that have been converted to vector data representation. This technology enables high-performance joint queries, and a simplified manner of querying labels, text, and numbers within a single SQL statement. It supports highly performant SQL + vector searches, operating on billion-scale data, with an operating latency of 200 milliseconds at a throughput of 200 queries per second (QPS).  The key features of this technology are as follows: Fast query performance Column-oriented storage; data is stored in the same column and compression techniques are applied to reduce disk usage and save I/O resources Linear scalability Data is stored evenly across nodes, ensuring scalability Simultaneous data input Data can be inserted simultaneously via random data distribution Concurrent queries  Simultaneous insertion and querying The following similarity metrics are currently supported: Euclidean Cosine Similarity Dot Product The following indexing libraries are currently supported: Facebook AI Similarity Search (FAISS) hnswlib (with proprietary optimisations) The following interfaces are available for developer integration: C++/Java/Python language Software Development Kit (SDK) SQL interface Web User Interface (UI) This technology can be applied for similarity searches (identifying similar high-dimensionality vectors), or classification (locating images that contain a certain element, e.g. car, flower). The following potential applications of this technology have also been tested: Biometrics (fingerprint matching) DNA/genetic sequences and other biomedical fields (similarity search/classification) Multimedia - image, video, and audio (similarity search) Text-based - recommendation systems, chatbots (similarity search) Molecular (similarity search) Trademarks (similarity search) Commodities  GIS vectors (vector-based semantic analysis) Compared with existing techniques, this technology represents a single, unified pipeline for querying vector representation data without the need to store structured data and vectors separately in traditional databases (SQL) and vector repositories. This solves the limitation of having to merge results from standard database engines (specifically optimised for hash-based searches) with that of vector query databases. This data engine includes a vector search function and it can efficiently store, index, and manage vectors that are generated by deep learning networks and machine learning models. Additionally, the extended SQL query syntax of this technology enables a highly efficient, simplified search across a variety of different AI applications. The technology owner is keen to collaborate with companies that are conducting in-house AI application development in industries such as, but not limited to, e-commerce, video analytics, smart city, and healthcare. The following is an example of how the vector search engine can be used to query for similar logos (images): A large dataset of logos is prepared A feature extraction model is trained from the dataset e.g. DarkNet-53, VCG, NASNet-Large, Inception-ResNet-V2, etc Logos are converted into vectors using the trained model Vectors of logos within are stored in the database Any new logo is put through the trained model to generate a vector for similarity search against the pool of vectors Infocomm, Big Data, Data Analytics, Data Mining & Data Visualisation, Artificial Intelligence, Data Processing