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10 Best Artificial Intelligence Companies to Watch 2020

On an Ambitious Mission to Bring the Incredible Benefits of Artificial Intelligence to Every Business: mezzanine.ai

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By Vishnuvardhan Kulkarni, Managing Editor, TSR

When evaluating the AI ecosystem, one can quickly categorize the majority of “AI” companies as developers. And while the major hyperscalers such as Google, IBM, Microsoft and Amazon are focused on building armies of Machine Learning (ML) scientists, it is frequently the smaller AI startups that focus on building more innovative and unique models to address the industry-specific problems.

An Ambitious Mission

The Silicon Review recently sat down with mezzanine.ai, an orchestrated AI platform service that was launched from the IP foundation developed over 10 years by a company called Geminare. Mezzanine.ai is throwing its hat in the ring using the highly disruptive approach of focusing not on building ML models but on building solutions from the ML models. Mezzanine.ai is on an ambitious mission to bring the incredible benefits of artificial intelligence to every business in an accessible manner, and along the way to elevate Service Providers to the level of leaders in the exploding AI space.

 

Geminare is the parent company of mezzanine.ai and over the past ten years the company has significantly contributed to the resiliency market. Similarly, mezzanine.ai aims to bring ML models as packaged solutions to the forefront of business leaders. Joshua Geist is the CEO of Geminare and is responsible for creating one of the most recognized and acclaimed Resiliency Orchestration Platforms in the market and along the way has enabled Service Providers who leveraged this platform to become industry leaders. A feat not for the faint at heart, over the course of the evolution of the Resiliency (or DRaaS) market, Geminare was successful in helping to place not one but three leading Service Providers into the Leadership Quadrant within one of the most highly respected industry analyst’s reports: Gartner’s DRaaS Magic Quadrant.

Everyone Is Looking to Be a Leader in AI

For the uninitiated, AI is perhaps one of the most complex areas to understand let alone deploy, yet its level of importance within business and society as a whole is so impactful that it’s being positioned as the next industrial revolution. AI works in various ways, but the main ideology behind it is enabling computers to solve problems that would usually require human understanding and intervention; essentially applying human-like skills to complex problem solving such as recognizing images and patterns.

Major cloud providers in the industry are keenly observing AI’s growth and are focused on becoming leaders in this space. Just last year Google CEO, Sundar Pichai, claimed that AI was the single most important innovation within the company, stating, “For us, this is an evolution of Google itself. We have this vision of a shift from mobile-first to an AI-first world over many years.” Microsoft, IBM and Amazon have all made similar claims, which is why it was so interesting to Geminare’s Geist when Service Providers began approaching Geminare in their attempts to launch AI programs to their own established markets.

Bridging the Gap Between Expectation and Reality

A report from MIT Sloan Management Review suggests that there is a massive gap between expectation and reality for most organizations when it comes to deploying AI within their business. To solve this, and to bring together the AI that’s out there and the businesses who want to use it, Geminare went back to its product team looking to leverage its award winning Resiliency Management Platform (RMP).

According to Geminare, the RMP is a multi-patented Orchestration & Automation Platform that creates advanced process automation and orchestration across virtually any hybrid deployed technology. Its strength has been leveraged in a new way to reach out to customer data securely, run the data through multiple ML models across multiple vendors, and present the results graphically or via API integration.

The result was mezzanine.ai, a platform that enables businesses of every size and from any industry to leverage the power of AI in a simple, wizard-driven interface delivered via a marketplace of ready-to-use industry focused solutions.

Staying Ahead of the Curve

Joshua Geist stated that, “mezzanine.ai connects ML models like puzzle pieces through its powerful underlying orchestration engine – the same engine that has had seven global patents issued – and allows businesses to access these models through prepackaged solutions in a marketplace, almost like an app store for ML solutions.”

It was this marketplace approach that captured the Service Providers’ attention, and the Service Provider community has taken notice, in a big way.

This statement clears the air around the platform’s application and business case. AI is considered to be the engine of the 4th industrial revolution and an integral part of the digital transformation because it is reshaping society, businesses, and governments. For any organization that wants to stay ahead in the race of an AI-led future, there is a driving need to accelerate the adoption of new technology in every aspect of the business.

Legacy Marketing and Mezzanine.ai

With the ability to reshape competitive advantages just a click away, customers have taken notice as well. Companies such as Legacy Marketing, an events-based marketing strategy firm based in Chicago, IL, are jumping into the power of machine learning through mezzanine.ai by leveraging the platform to exponentially contribute value-added benefits to their in-market solutions.

New Data Insights

Legacy Marketing’s primary challenge was deriving new insights from feedback data gathered during and after marketing events. They were already using an analytical tool to pull statistical information but the insightful relationship between events and keywords identified across events was missing. By applying several text analysis ML models from different vendors to the same data, multiple facets of data relationships were discovered. These relationships can now be used by their customers to direct more strategic and more targeted marketing campaigns. As such, mezzanine.ai enabled Legacy Marketing to offer their client base a higher level of value based on deeper insight into the data collected.

In a recent campaign example, Legacy Marketing provided both positive and negative feedback gathered from social events across various locations in the U.S. hosted by a major CPG brand. With mezzanine.ai, Legacy Marketing was able to run the captured data through multiple ML models seamlessly. Models including Watson’s Natural Language, AWS’s Comprehend, GCP’s Cloud Natural Language and Azure’s Cognitive Services Text Analytics were all run through an orchestrated solution template within mezzanine.ai’s marketplace, helping to determine the stories that can be told about the data, and allowing for the possibility of rapid feedback and campaign adjustment.

While insights can certainly be captured by running the data through a single model, not all models perform the same way or even return the same data despite claiming to achieve the same goal. For example, while focusing solely on Text Analytics ML models that provide content, sentiment and keywords, Legacy Marketing used mezzanine.ai to experiment and compare ML model outputs, ultimately building a deeper understanding of their data and building an ML Model workflow that best fit their needs.

An Exploding Ecosystem

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Joshua Geist provided further commentary on why Legacy Marketing saw such success with their mezzanine.ai use, adding, “there is a large disconnect between the AI world of data scientists and the businesses that stand to benefit from their use. Incorporating in-house expertise is a huge obstacle for businesses who want to remain focused on what they do best, which is not necessarily software development and certainly not building out a team of data scientists. Mezzanine.ai makes it possible to consider the alternative option of outsourcing AI talent and toolsets, essentially bridging this gap and bringing AI to businesses.”

In just the last few years, “machine learning” has come to seem like the new path forward. Algorithms freed from human programmers are training themselves on massive data sets and producing results that have shocked even the optimists in the field. Much like the microchip and the Internet ushered in an industrial revolution of their own, most within the technology industry agree that AI too has the power to create an information-based revolution. The opportunities ahead of us within AI and machine learning especially are so broad and numerous that it’s easy to imagine how platforms like mezzanine.ai are ripe to capture their spot within the exploding ecosystem.

Joshua Geist, CEO of Geminare

Geist, a career entrepreneur, presenter, multi-patent holder, technology industry commentator and expert is a recognized innovator within the IT Orchestration Market. Geist is a recipient of numerous industry accolades including multiple CRN Channel Chief awards, Gartner Cool Vendor designations, Most Innovative Solution winner from CIR international community, and lead contributor in the positioning of multiple Gartner Magic Quadrant Leaders. Geist previously founded and was CEO of MindBytes (acquired by Munimentum Group), a professional services organization focused on delivering technology best practices to small and medium-sized organizations. There he acted as the outsourced Chief Technology Officer and advisor for over 200 small and mid-sized organizations over the course of 12 years. Prior to founding MindBytes, Geist had the privilege of working for Apple Computer for many years as their Technical Training Manager where he developed and delivered network, system, sales and operational training programs across Canada. Geist holds a degree in Physics, sits on multiple government and technology industry boards, and is based out of Geminare’s Silicon Valley offices.

"Mezzanine.ai is throwing its hat in the ring using the highly disruptive approach of focusing not on building ML models but on building solutions from the ML models.”