Raleigh’s InsightFinder uses Machine Learning to Improve IT Operations
Text below from Grepbeat by Suzanne Blake
It’s no secret that artificial intelligence can improve many industries. InsightFinder, based in Raleigh, is proving how AI can predict and prevent IT incidents.
Founder and NC State computer science professor Helen Gu has committed her life’s work to using unsupervised machine learning to detect anomalies in data in valuable ways. When her success had garnered attention from organizations like Google and Credit Suisse, she realized the technology that underlies InsightFinder could lead much further than just licensing it to software companies.
CEO Dan Turchin spent more than 20 years working toward similar ends—sharing a keen professional interest in using AI to help IT operations run more smoothly—but on the customer-facing side in go-to-market activities. When Gu and Turchin connected last year, Turchin noted that InsightFinder’s technology was more mature and boasted a stronger technical team, led by Gu as CTO, than many competitors.
“The importance of digital-first strategies has been, if anything, elevated by work-from-home trends,” Turchin said. “So it’s kind of right technology, right team, right market. It just kind of felt like clouds parting, birds singing—it was meant to be.”
Founded in 2015, InsightFinder’s algorithms can determine an anomaly score and predict the future impact on a company’s infrastructure, providing 5-7 hours of advance notice before a system might be unavailable.
“Basically, InsightFinder is the smartest kid in the class, who has that ability to anticipate what the teacher is about to say,” Turchin said.
InsightFinder serves companies through a SaaS subscription-based business model, charging on an annual basis based on the number of nodes monitored. Typically, customers save $30,000-$50,000 in reduced downtime per month, Turchin said.
InsightFinder has already raised $5.3M in funding, including an investment by Durham-based IDEA Fund Partners. IDEA Fund Managing Partner Lister Delgado was especially interested in the cutting-edge tech that Gu had built.
“InsightFinder has been able to crack the code and develop technology,” Delgado said, “not because they stumbled by luck but because of the hard work of the founder in developing this technology over many years. That is very unique.”
Turchin said InsightFinder has the opportunity to change the world of work and infrastructure management.
The “data exhaust stream”
Monitoring a dashboard with 5-10 million events per day is not something humans are very good at, Turchin said, noting the “data exhaust stream” that is created by machines.
“That problem of data exhaust from machines is not going to be solved by more people looking at more data,” Turchin said. “It’s really going to be solved by smart algorithms, automatically examining the data, so that the people can do things that require empathy, or judgment, or rational thinking.”
Customers of InsightFinder can get back an hour a day because the other tasks are getting consolidated efficiently by machines, he said.
InsightFinder’s technology also allows clients like edtech startups to scale during the pandemic and connect students reliably and consistently without any hiccups.
While the pandemic has had harsh personal effects, it spurred InsightFinder to recruit developers remotely from any location while pushing forward a digital transformation where companies are thinking more about accelerating the innovation in their digital services.
“Now they (employees) can get to their kids’ soccer game, or explore a passion, volunteer, do things that really make them better humans,” Turchin said, noting the importance of the fusion of human and machine intelligence. “We’re really proud to be kind of on the front lines catalyzing that shift in the workplace.”
Other Helpful Resources
Unified Intelligence Engine (UIE): A Technical Deep Dive Paper
InsightFinder utilizes the industry’s best unsupervised multivariate machine learning algorithms to analyze a large amount of production system data.
Root Cause Analysis
A major credit card company’s mobile payment service experienced severe performance degradation on a Friday afternoon.