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An Interview with Joshua Geist, CEO: ‘We’re Democratizing ML for Businesses by Enabling Them to Access Whatever Models are Best Suited for their Needs from Whichever Cloud Platform they Choose to Use’


“In the Entertainment industry alone, we have a client who has saved thousands of hours in the manual review and collection of data from movie segments for the purpose of classifying content and sentiment.”

Artificial Intelligence (AI) is an umbrella term for the analysis and interpretation of data using self-learning applications, called Machine Learning (ML) models, with the intent of making predictions about the content and meaning of that data. The more data we make available to train the ML models, the better they become at correctly identifying what they were tasked with. For example, you might want to know “does this picture contain a cat?”, or “are there irregular patterns in these financial transactions?”. These models will have been told what a “cat” is and what a “data anomaly” is during the initial training so that they can correctly identify these things when shown new data. That said, where data is the fuel for AI, ML models are the engines that use that fuel.

In light of the foregoing, we’re thrilled to highlight what is doing. was developed as a stand-alone solution through the utilization of Geminare’s award-winning and multi-patented orchestration and automation platform. As a leader in delivering data curation, resiliency, and IT orchestration solutions, Geminare has helped advance global service providers with market-leading orchestrated Cloud solutions. With, Geminare is leveraging its capabilities to power the next generation of ML solutions for business.

Joshua Geist, CEO, spoke to The Silicon Review. Below is an excerpt.

If I were a business looking to get into AI in the easiest way possible, what would mean for me? approaches ML models as AI building blocks, allowing a user’s data to be run through multiple models as needed in order to achieve the business insights they’re looking for. Through the wizard-driven platform, ML models can be integrated with each other, meaning the output from one can serve as the input to another. For example, a text transcript output from a Video Analysis ML model can be used as the input to a Text Analysis ML model all within the same platform. This is how is able to create unique solutions for each user, which can be shared throughout the business or leveraged directly – quickly, securely, and in a cloud-agnostic environment. Furthermore, allows you to compare results from each of the ML models instantly so that you can choose the best model for your needs. Your data and applications can be stored in Google, Amazon, Microsoft, IBM, countless other clouds, or even locally on-premises.

Essentially, it democratizes AI so that all businesses are able to access the extremely powerful capabilities of ML without the need for data scientists or in-house knowledge.

As a business decision leader, why do I need to be thinking about AI now?

It seems futuristic and something that’s not a critical decision to be made at this time.

AI is already everywhere, whether you’re asking your phone to look something up, translating text online, or browsing through “similar items recommended for you” on your favorite shopping site. In fact, your customers rely on AI to find your business, and perhaps more importantly, to research your business. Given that 84 percent of B2B executives and 92 percent of B2B buyers are using social media to make buying decisions, and that a huge amount of that social media feedback is being driven behind the scenes by AI, you can see how this is already directly impacting business in a very real way.

What type of impact have you seen at businesses using

One of the largest impacts we’ve seen so far is around time savings in automating a process that was previously done manually.

In the Entertainment industry alone, we have a client who has saved thousands of hours in the manual review and collection of data from movie segments for the purpose of classifying content and sentiment. Using multiple ML models through, this business started with a video and was able to a) derive content, context, keywords, and sentiment directly from the video, b) obtain transcripts of the audio portion, and c) translate and distribute their clips internationally – all in a fraction of the time, going from days to seconds per clip.

In the marketing industry, another client is using the platform to a) obtain transcripts from audio recordings of interviews following a marketing event, b) derive sentiment from the interviewees regarding their recent interactions at that event, and c) determine data relationships, both positive and negative, between key event product placements which is critical to developing strategies for the next marketing event. This multi-stage analysis is not only saving them time, but opening up new insights into their data, and was only made possible through a platform such as

Besides the Media & Entertainment and Marketing industries, what other industries are likely to be impacted by and how would they benefit from using your technology?

An important point to note is that AI and the application of ML models is relevant across all industries, but there are some pre-trained models that are particularly suited to some industries. For example, a data loss prevention ML model can find sensitive data such as credit card numbers left erroneously in retail transactions prior to archiving. A document comparison ML model is able to highlight any changes in legal documents between versions and can also call out specific features such as parties, timelines and obligations, making tracking changes and finding key information much easier. And a pattern recognition model can alert financial businesses or manufacturing shop floors to important discrepancies.

Joshua Geist: A Leader to Watch

Joshua Geist is the CEO of Geminare, the company behind He holds a degree in Physics and has helped launch leadership positions for major service providers in the Gartner Magic Quadrant.

Learn more at

“Our marketplace ease-of-use is designed to scale so that you can come to with a question, much like asking an expert in the field for their advice, and the platform will direct you to the best possible options to explore. It’s the easiest and most friendly way to start your AI journey.”