Co-Founder
ESGnie
Co-Founder
Neptune
Co-Founder
WaterFutureTech
Co-Founder
Groundup.ai
Director, APAC
Fieldbox.ai
Co-Founder
Datakrew
Co-Founder
Teredo Analytics
Co-Founder
Memsing
ESGnie is an advanced A.I data analytics and processing Saas platform focused on bringing better and more timely insights towards UN’s sustainability goals. We do so with our innovative AI Technology by helping companies draw better insights from their unstructured and disparate data, and compare them against Sustainability Standards with little human supervision. We provide actionable insights to optimise water usage & planning. This ability to better track and predict and optimise water usage and maintenance needs across ecosystem allows us to help companies adopt greener practices.
Our innovative business model allows us to streamline the flow of information between companies and funds. Which in turn helps funds dispersing green investments and financing better understand price environmental risks of their portfolio companies.
Team Neptune is the brainchild Team of 3 SUSS Biomedical + Accountancy undergrad and 2 UIT (Vietnam National University Ho Chi Minh City) Data Science undergrad. To reduce the time and labour cost of automated diagnostic process and rule-based compliance management for underground water distribution network, the team is designing the next generation “Swarm-Coordinated” Soft-Robot Bionic “AI Leak Cam” and a suite of cloud-based machine learning expert-system tools for 6 type of missions (Patrol, Detect, Confirmation, Tether, Repair, Supervise) . Targeted at OPEX & Performance based contractors in the water and energy utility space as well as smart pipe makers (foundry), the team hopes to be a regional and global deep tech player in the “Utility Op-tech market-space”.
WFT provides a novel and accurate predictive maintenance approach for water leakage by classifying damage type and predict expected time of failure in advance – using deep learning.
Leveraging on time-frequency sensory data that captures both time and frequency domain features, our deep learning algorithm will automatically learn the optimal feature representation and improve the performance on the target task.
Groundup.ai is an Artificial Intelligence (AI) and sensors company whose robust capabilities help industrial enterprises in sectors such as heavy machinery, manufacturing and maritime create better strategies through a data-driven approach. Our core capability lies in condition-based monitoring and predictive maintenance, whereby we leverage on our proprietary sound sensors and AI platform to tackle unplanned downtime by accurately identifying and predicting machine failure beforehand, and thereby saving millions of dollars in unneeded losses. In this Digital Water Hackathon, we will be showcasing our solution, AssetSense, which comprises 2 main features – proprietary sound analyser and intelligent software platform. Through this, we are able to help PUB optimise maintenance at a lower cost.
As an AI Operator, FieldBox.ai offers a complete range of technologies and services to enable cost-efficient development, deployment, run and scale of AI in industrial operations.
Our solution for monitoring the condition of rotating equipment, detecting anomalies and predicting failures is a pre-packaged solution that combines sensors, an AI model and a business application to collect real-time data, encapsulate expert knowledge and provide operators in the field support for making better decisions and reducing the costs of maintenance.
We manage end-to-end data projects with a unique combination of expertise in data science, software/IT and industrial Engineering and provide the service and technology infrastructure to run AI for 24/7 operations worldwide.
Datakrew provides an end-to-end IoT/AI solution for monitoring and predicting the performance of motors, pumps, and other water-related equipment. Our AIoT platform software (MADS) predicts the faults by analysing the vibration signature of the machinery and correlating it with current surge and heatmap signatures. We are able to identify and isolate more than 20 faults accurately and provide pre-emptive maintenance recommendations. The predictive maintenance engine is couple with 3D digital twin which can show real-time insights on 3D digital twin models or even through on-site Augmented Reality. Our secure IoT gateway hardware (ITUS) can be used for both monitoring and control/automation of plant machinery. It uses our patented post-quantum encryption technology to protect even against quantum cyberattacks on plant IT/OT infrastructure.
Datakrew provides an affordable yet reliable solution for monitoring and control of small-scale remote industrial wastewater treatment plants. There are two functional parts to our solution: Secure wireless IoT connectivity hardware (ITUS) and No-code IoT/AI platform software (MADS). The ITUS device can be used for both monitoring and control/automation of plant machinery either directly connecting to sensors/actuators or retrofitted via existing PLC/SCADA. It is enabled with our patented post-quantum encryption technology to protect even against quantum cyberattacks on plant IT/OT infrastructure. It supports multiple communication channels such as LoRa mesh network, WiFi, 3G/4G, LAN, or even Satellite and is suitable for remote plant locations. The MADS platform can be used for data visualization, analytics, and reporting in a rapid way without any coding.
Using its proprietary hardware and in-housed developed machine learning based algorithm. Teredo Analytics aims to provide timely & comprehensive insights on pipelines integrity before the development of detrimental leaks.
Taking full advantage of in-house developed / A*STAR licensed next generation MEMS-hydrophone sensors and associated AI-algorithms, MEMSING developed miniaturized hydrophone leak detection solution with low power consumption small footprint which enables new application area (such as distribution networks pipe monitoring). The prototype system demonstrated up to 2.5m faulty location resolution in the test bed pipe lines.
Besides, MEMSING also proposed non-intrusive MEMS sensors more widely deployed along pipeline system to collect supplementary info as cross reference both at the Edge or in the Cloud for even lower false alarm and higher location accuracy .
By combining the best out from both intrusive and non-intrusive MEMS sensors, we aim to prove the functionality and cost effectiveness and leak detection breakthrough on wider range and more complex pipe network asset monitoring.