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Spotflock collaborates with private and government entities to build deep tech based offerings

Sridhar-Seshadri

By: Sridhar Seshadri, CEO & Co-Founder, Spotflock Technologies Private Limited

MoU with INCOIS to improve the accuracy of ocean predictions and advisories

 

Kindly brief us on the recent MoU with the Indian National Centre for Ocean Information Services (INCOIS).

Indian National Center for Ocean Information Services (INCOIS) is an autonomous body under the Ministry of Earth Sciences (MoES). It is mandated to provide the best possible ocean information and advisory services through sustained ocean observations and constant improvements through systematic and focused research. INCOIS has collaborated with Spotflock to investigate the use of deep-tech tools such as data pre-processing tools, data visualizations, business intelligence, AI-Machine Learning, Natural Language Processing, Computer Vision, Blockchain, and IoT in completing the mandated task. Some use cases that we can solve immediately for INCOIS includes amongst other things species classification and tracking in the Indian Ocean, AI-driven environmental pollution warning systems, and the use of geophysical, infrastructure, field, and well data; data wellbores, seismic surveys, and pipelines for machine learning algorithms to analyse sensor data, allowing 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%.

Please brief us about the products/solutions you provide to your customers and how they get value out of it.

IntelliHub & Intellichat are our key product offerings.

IntelliHub is a unique multi-cloud AI platform that runs on Google TensorFlow, H2O.AI, and our in-house [Intellihub] AI algorithms. This keeps the enterprise data secure on a private cloud and frees up data and models for total client privacy and control. Giving customers the ability to select the AI solution that will benefit their business the most, while lowering the cost of platform switching by simply changing configurations with no code changes required. Platform is created with learning convenience in mind as well as ownership and protection of Data & AI Models, which is crucial for organisations like PSUs that store sensitive and private data for many Indian citizens.

Intellichat is an AI-based conversational chatbot platform with advanced chatbot features powered by Intellihub, BERT, and GPT-based models. Intellichat features an interactive dashboard with multiple metrics displayed with chatbot tracking data that is simple to integrate into any website or social media platform. The chatbot is multilingual, and the infrastructure has been designed for enterprise scale, with the ability to grow automatically and handle traffic spikes.

Which industry verticals are you currently focusing on?

We have four business verticals: the first is our core AI, business analytics, business intelligence, and block chain-based solutions; the second is our sports vertical; the third is our medical vertical, where, for instance, one of our joint ventures would use our medical equipment to treat diabetic retinopathy; and the fourth is our pharmaceutical and nutraceutical segment. Additionally, there is E-Governance, a larger vertical that will address numerous smaller problem statements.

Which are your ongoing projects?

We are collaborating with multiple state and national government entities in India, as well as private entities, to investigate the possibility of applying intelligent, transparent, and ethical deep tech technologies. One of the important projects is the one mentioned above with INCOIS.  We are also heavily involved with the Government of Haryana on a variety of initiatives, ranging from the establishment of a Centre of Excellence for Haryana State to the processing of nearly 2 crore records using AI capabilities. Our sports vertical is in advanced discussions with a Dubai-based entity, and our medical vertical is working on a joint venture for medical equipment dealing with diabetic retinopathy.

How are disruptive technologies like IoT/AI/Machine Learning impacting today’s innovation?

Disruptive technology is defined as a technology that alters the normal operation of a market or industry. It displaces an established product or technology, thereby spawning a new industry or market. These technologies can be used in a variety of applications, including fraud detection, video games, and spam detection in emails. They can streamline business operations and improve people’s lives. Chatbots for web support, virtual assistants, and tracking the estimated arrival time of online food orders have made things more predictable and convenient. These disruptive technologies have transformed data into a source of limitless opportunities. For instance, our product “Spotflock Studio” has capabilities for face recognition, emotion & shape detection, feedback booth, GIS MAP for crop detection, and analogue to digital conversion using computer vision. These outputs can be further used by various industries to power their product or process innovations.

How AI can make healthcare more accessible?

AI start-ups all over the world are developing disruptive diagnostic solutions that will significantly reduce costs while eliminating the need for physical distance. This is accomplished through cloud-based connections to hospitals and clinics, chatbots, smart apps, and AI-enabled data analytics. Healthcare organisations can explore implementing new AI to improve the experience of calling, planning, and accessing health centers. Robotic process automation (RPA) combines workflow, business rules, and the presentation of information to perform structured digital tasks for administrative purposes. They have simplified the process of obtaining prior authorization, gaining access to updated patient records, and making bill payments.

AI enables access to the learnings and data from hundreds of thousands of cases. AI algorithms are assisting doctors in analysing a much broader range of data and predicting new drug combinations that are personalised for a patient’s specific need with greater granularity. The most common applications of NLP (natural language processing) in healthcare are the creation, comprehension, and classification of clinical documentation and published research. NLP systems can analyse unstructured clinical notes on patients, generate reports, transcribe patient interactions, and perform conversational AI.

AI Powered Prognosis engine with highly robust, secured and interoperable EMR, adhering to HIPAA, MCI, HL7 and other statutory medical regulations by different governments equipped with a peer-to-peer tele-consult and scheduler over a patient consent technique.

Diabetic Retinopathy:

An extensively trained AI- Computer Vision models for image classification for early detection/prediction of PR and NPR and classification of retinal images pertaining mostly to diabetic patient in provision to a good visualization on the severity of DR for the medical practitioners.

Diagnostics – Lung Imaging Models:

AI Powered – Image classification model which classifies Diagnostic images and predict/classifies lung issues pertaining to a person for Pulmonary Embolism, Deep Vein Thrombosis etc., with paramount accuracy.

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