Big Data Analytics Startup Quickpath Scaling as Machine Learning-Powered AI Demand Grows

By Iris Gonzalez
Quickpath founders from the left: Trent McDaniel and Alex Fly. Courtesy image.

When you use Amazon Alexa, Siri, or Google, you can thank machine learning (ML) powering the artificial intelligence (AI) for the targeted ads, personalized content, song recommendations, and more that you get in personalized services. That exploding demand for big data-driven enterprise applications is what is fueling Quickpath‘s rapid growth. 

The San Antonio big data analytics software startup uses its cloud-based software application program interface, or API, to help businesses lacking the specialized AI expertise to connect the different datasets that fuel machine learning AI. The proprietary Quickpath platform debuted in late 2018 for midmarket and enterprise companies implementing ML hardware and software packages. 

“We wanted to make it 10 times easier for companies to access machine learning-powered AI for one-tenth of the cost,” said chief executive officer Alex Fly.

Before launching their platform, Quickpath co-founders Fly and Trent McDaniel (who is chief technology officer) had been building real-time AI algorithms over the past 20 years working on custom implementations with a variety of Fortune 500 customers across different markets.

The duo met in the late 1990s while working for the San Mateo, California-based customer relationship software company called Epiphany. That business went public and was later acquired by SSA Global Technologies Inc. for $329 million. The founders now run their business to business (B2B) software as a service (SaaS) startup out of Geekdom, a downtown co-working space.

Machine learning-powered AI demand growing

Big data and business analytics worldwide revenues are forecast to grow to more than $187 billion in 2019, an increase of more than 50 percent over the past five years.

While the use cases for leveraging AI are practically endless – from manufacturing to healthcare to finance and more — the AI field is plagued with high failure rates for early machine learning initiatives. Only 13 percent of AI predictive models make it to production and a staggering 60 percent of those models fail when implemented.

To get these models to work, it takes considerable resource expertise, often working with powerful, but immature open source technology. That makes for AI applications that are very difficult to maintain as they need constant reworking known as “technical debt.”

“The technical and analytic debt is too high, because companies are implementing machine learning-based decision automation with duct tape and bailing wire, which isn’t sustainable,” Fly said. “Unlike typical application code, the data inputs to AI are dynamic and change over time, but if there’s no monitoring done on the health of the system, there can be problems.”

Quickpath provides the low-code API platform that helps enterprise companies with the “last mile” of AI implementation. Its platform reduces the often-underestimated complexity, effort, and costs of implementing machine learning and intelligent decision-making by streamlining the development, deployment, and monitoring processes. Data, models, and decisions unite in an integrated Quickpath platform allows data engineers, ML engineers, information technology professionals, and data scientists to collaborate across the entire machine-learning life cycle inside a company.

Quickpath has helped build scalable data and analytics products for companies including Unilever, United Health Group, USAA, and Autotrader.com. Michael Willette, executive director and technical fellow at USAA explained the value of using Quickpath’s services to reduce the technical debt in maintaining predictive models by using the Henry Ford example of how he streamlined automobile production.

“It takes considerable resources to maintain predictive models after the first year of deployment,” Willette said. “When something changes in your system, bringing data to bear on the problem becomes too costly. Quickpath seamlessly connects the analytics to our system of models at significantly reduced costs for us.”

Quickpath outlook points to aggressive growth 

The company bootstrapped the launch of its proprietary Quickpath platform and has one patent issued with two more patents pending. Three consecutive years of doubled growth has led to a mix of Fortune 500 and midmarket clients interested in licensing Quickpath’s platform. The company has also established strategic partnerships with the leading data science platform software providers such as H2O, Tibco, IBM, Google Cloud, Amazon Web Services, and Microsoft Azure. 

In addition to its existing services revenue, Quickpath will be close to $1 million in annual recurring revenue for its software platform by the end of 2019, according to Fly and McDaniel. The startup is also garnering notice — Quickpath was one of 10 finalists in the 2018 OReilly AI startup showcase conference held in San Francisco and was recently featured in a Forrester article on the future of machine learning.

The startup has about 30 employees who spend most of their time working at client sites managing a company’s data and machine learning-enabled applications. The startup is growing rapidly, hiring anywhere from 10 to 15 software engineers as well as sales and customer success team members. The co-founders are looking for investors for pre-Series A funding round to ramp up its sales, customer success, and engineering teams.

“The demand for monetizing data science isn’t going away,” Fly said. “It’s hard for companies to build better quality predictive models and integrate them into business applications like Salesforce, mobile apps, and marketing engines. Our intelligent decisioning APIs connects everything to automate their business decision-making.”

Quickpath’s value proposition is one companies appreciate, McDaniel added.

“We help companies manage analytic decisioning at scale to monetize data science and drive bottom-line growth,” he said.


Read more about Quickpath and emerging AI workforce development needs

The AI industry is constantly evolving which makes it challenging for companies and universities to keep pace with the latest innovation. Both Fly and McDaniel are members of a newly formed University of Texas at San Antonio (UTSA) Cyber and Analytics advisory board to help identify AI and big data analytics curriculum needs that will inform the school’s curriculum.

“We’re working to bring some of data analytics and cyber academic research to the marketplace,” McDaniel said. “What we’re trying to do is take AI out of the lab and into the hands of business owners.”

The company is also an industry partner with Codeup, a technology workforce development training provider. Quickpath has to date hired three students from Codeup’s first data science program cohort to work as data machine learning engineers, Codeup’s director of strategic partnerships Stephen Salas said.

“Employer partner engagement is invaluable at the beginning and end of the curriculum cycle,” Salas said. “We often lean on the relationships we have cultivated with our employer partners to help shape our curriculum development.”

 

Featured image is of the Quickpath founders, from the left: Trent McDaniel and Alex Fly. Courtesy image.

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