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Harvest the potential of AI with Hypergiant’s Next-generation products and solutions

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Previously, AI and machine learning were considered as a part of science fiction, but now they have reached a point where they can help make decisions, evaluate options, and also execute on those decisions. AI solutions are now powered by ample supplies of data and affordable computing power, giving abundant opportunities to organizations who invest in these tools.

Without proper vision and strategies in place, though, AI initiatives can fail to deliver on their promises., To overcome these adversities,  Hypergiant Industries has come up with cutting edge solutions to help companies unleash the full power of AI. Hypergiant approaches AI as a cornucopia of tools and matches the right tool to the business or government use case. They customize solutions according to customers’ specific needs and datasets. As a result of this practical approach to AI, many of their repeatable solutions are normalized high-level models that can be retrained in different data sets for different use cases. For example, Hypergiant has built a robust and flexible neural network-based computer vision platform that they have trained towards hand gesture identification in live video feeds and as a robust AI-powered O.C.R. that is able to identify the content in tables and infographics contextually. Their Eos Bioreactor leverages multiple AI-techniques on the data coming from the multiple sensors in the unit to increase confidence in measuring algae growth alongside anomaly detection to monitor oxygen levels. Hypergiant leverages its models and market insights to develop more robust AI-driven products and platforms.

In conversation with Ben Lamm, CEO and Founder of Hypergiant

Q. Everything that Hypergiant talks about is the real-time applications of AI. Do you think it is mandatory for companies to embrace AI to increase operational efficiency?

I believe if companies do not embrace AI in the long-term, then they will ultimately fail. As more and more companies automate, and as machine learning and auxiliary tech improve into more useful and applicable AIs, I do think more and more companies will find the advantages of incorporating AI into their companies. This was started with more back-end operational services, but it is now evolving into more front-end experiences.

Q. How did you gain the expertise of providing real-time AI tech services to any and every industry sector?

We have an incredible team of technologists who have worked across a diverse array of industries and on many different types of machine learning and automation projects. These individuals bring a lot of experience and opportunity to the clients we work with and improve Hypergiant at large. While we work with a lot of companies and government agencies, most of the use cases and data sets are relatively similar. We aim to focus on the intersection of critical infrastructure, space, and defense, all of which have common threads in the data and problems they are facing.

Q. There are other reputed companies in the market providing AI tech and machine intelligence solutions. What makes you a better service provider?

We are one of the few AI companies that also have a strong background in UI/UX, which means that we build solutions people actually want to use. The intersection of great UI/UX with powerful machine learning models is something that we aren’t seeing many others combine. It’s a part of our DNA that puts us far above other AI companies in terms of our ability to ensure the money companies and government agencies spend on products actually yield returns. We also leverage our abilities to execute for clients to build out our own software platforms to use alongside our solutions division.

Q. AI undoubtedly has the potential to pull insights from data sets, but still Data Quality is one of the top most challenges to successful implementation of AI systems in enterprises. How do you help your customers overcome this challenge?

There is a lot of education necessary, not only in having a data strategy catered to the business needs but also about the amount of data needed to unlock the full potential of AI on that data. The more data that is available over a longer period of time becomes more valuable for building models against that data that can be used to predict future outcomes. If there is enough data and a willingness to share the data with our team, this is usually conquerable. Many enterprises will give us just a spreadsheet’s worth of data and expect us to do something with it. In those instances, we have had to create our own synthetic data to model out what would potentially be possible once they grant us access to more data. On top of the usual data quality concerns (cleaning, feature engineering, and synthetic data), it is essential to have a proper feedback loop. The customer needs to work with data scientists and make updates based on new recommendations to ensure good quality data. Plus, the company should evolve to be data quality-centric by the adoption of data cataloging plus metadata tagging tools to make the data robust for ongoing AI projects. The skill sets needed for data cataloging and metadata tagging are more on the data stewardship and business domain expertise roles which they can prepare in-house with our guidance.

Q. Do you have any new services ready to be launched?

We are working on some pretty impressive smart infrastructure, and robotic agriculture systems that we think could be pretty impactful. As we get further, we may make some of those more public.

Meet the leader behind the success of Hypergiant

Ben Lamm is a serial technology entrepreneur who builds intelligent and transformative businesses. He is currently the founder and CEO of Hypergiant, a next-gen AI and defense company. Previous to founding Hypergiant, Lamm was the CEO and founder of Conversable, the leading conversational intelligence platform that helps brands reach their customers through automated experiences on all major messaging and voice platforms. Conversable was acquired by LivePerson (NASDAQ: LPSN) in 2018. Lamm was also the founder and CEO of Chaotic Moon, a global mobile creative technology powerhouse acquired by Accenture (NYSE: ACN). During his time at Chaotic Moon and as a Managing Director at Accenture, Lamm spearheaded the creation of some of the Fortune 500’s most groundbreaking digital products and experiences in the emerging tech world of IoT, VR, Connected Car, Mobile, Tablet, and Wearables. Lamm also co-founded Team Chaos, a mobile gaming company focused on making fun, original games that people can easily play across a variety of platforms. In 2016, Team Chaos was acquired by Zynga (NASDAQ: ZNGA).

“We approach problems from a user-centric perspective, marrying business needs with user needs through the lens of intelligent technologies.”