Spotflock Technologies Private Limited, a deep tech company which specializes in AI, machine learning, and natural language processing, has signed an MOU with Indian National Center for Ocean Information Services (INCOIS) for promoting ocean information and advisory services by applying deep tech to increase the accuracy in predicting outcomes in various industries.
Globally ocean industries and Government departments use deep-tech tools like data pre-processing tools, data visualizations, business intelligence, AI-Machine Learning, Natural Language Processing, Computer Vision, Blockchain, and IoT. In India the Ministry of Earth Sciences (MoES) is implementing the Ocean Services, Modelling, Applications, Resources, and Technology (O-SMART) Scheme.
Established as an autonomous body in 1998, INCOIS is mandated to provide the best possible ocean information and advisory services through sustained ocean observations and constant improvements through systematic and focused research.
Spotflock collaborates with global academia and MNCs on research in Healthcare, Education, Environment, and Deep Technologies. “Intellihub” flagship product of the company is popular all over the world with 3.2 lakh API calls, 20,000 DIY Projects Made in 132 Countries & 15,400 Udemy Course Subscribers.
Spotflock has partnered with INCOIS, a pioneer in deep-tech technologies. The collaboration aims to investigate the possibility of pooling their capabilities and resources to aid in the development of a collaborative framework and deep technology applications in the domains of ocean science and services.
Mr. Sridhar Seshadri, CEO & Co-Founder, Spotflock said, “We are happy to sign an MOU with an esteemed organization INCOIS to closely work on classifying and tracking species in Indian Oceans by using CNN-based models for identifying potential Fishing Zone information gathering, impact of climate change on species population, track migration routes of species, detection of Ocean Pollution by adopting AI-driven early warning systems for authorities to react expediently and confine environmental pollution, habitat modeling and species distribution.
Prediction of species presence & their distributions in similar habitats, seafloor depth estimation, classify whether the bottom identified by the automatic echogram process is clear using CNN-based models.
Prediction of wave condition, reduce computational power required by physics-based forecasting modeling and faster prediction of wave condition and we can leverage geophysical, infrastructure, field, and well data, data wellbores, seismic surveys, and pipelines for machine learning algorithms to analyze sensor data, enabling the industry to identify suboptimal operations and impending failures before they occur.
We are confident that within a quarter of engagement, accuracy and quality of results will increase by 5% to 25%. Some of the key features that Intellihub offers for the entire AI lifecycle include the Self-Service AI Platform, Model as a Service, Visualization as a Service, and ML Ops, further enabling the interoperability of Data Science systems and algorithms.”