InsightFinder raises $15M to help companies figure out where AI assistants go wrong

The role of visual aids has also changed. While the market for technology reliability assurance solutions has grown over the years, the focus has slowly shifted from “tracking everything” to “managing problems and costs.” Meanwhile, the rapid growth and adoption of AI agents in businesses has only added a new category of work that needs to be looked at.

InsightFinder AIan initiative based on 15 years of academic research, is no stranger to this problem.

The company has been using machine learning proactively monitor, diagnose, and fix IT problems since 2016 and now it is attacking the reliability of today’s AI model with an AI assistant solution that can do everything from detection and recognition to repair and prevention.

The company, founded by CEO Helen Gu, a professor of computer science at North Carolina State University who previously worked at IBM and Google, recently raised $15 million in a Series B round led by Yu Galaxy, TechCrunch has learned independently.

According to Gu, the biggest challenge facing the industry today is not just monitoring and identifying where AI models go wrong; it is examining how all technologies work now that AI is part of it.

“In order to identify AI model problems, you need to manage and analyze the data, the model, and the architecture together,” Gu told TechCrunch. “It’s not always a model problem or a data problem; it’s a combination. Sometimes, it’s just your baseline.”

Gu explained what this looks like in real life with a story: One of his clients, a major US credit card company, noticed that one of his fraud displays was shaking. Because InsightFinder monitors all of the company’s data, it was able to identify that the model failure was caused by an old cache in some server location.

“A major misconception is that AI visualization is limited to LLM evaluation during the development and testing phases. Instead, a visual AI platform should provide end-to-end support for solutions related to the development, evaluation, and production phases,” he said.

InsightFinder’s newest product, called Autonomous Reliability Insights, can do all of this using unsupervised machine learning, large and small machine models, predictive AI, and predictive analytics. This basic unit is data agnostic, according to Gu, which allows the system to access and analyze all data streams to gather signals that can be correlated and verified to reach its goals.

Now, the scene is full of competitors in the new market opened up by the proliferation of AI tools. Almost ten years into its journey, InsightFinder has been going up against the likes of Grafana Labs, Fiddler, Datadog, Dynatrace, New Relic, and BigPanda, who have the ability to build to solve new problems presented by AI tools.

But Gu was not disappointed. Instead, he says InsightFinder’s expertise, capabilities, and customizations serve as a sufficient moat. “We rarely lose (customers) to anyone else so far… That’s a highlight, right?

Today, InsightFinder’s clients include UBS, NBCUniversal, Lenovo, Dell, Google Cloud, and Comcast. Gu attributes the success of 10 years of working to understand the needs of large enterprise customers.

“It has come down to working with our Fortune 50 customers to refine and understand the business requirements to deploy these models,” he said. “We’ve been working with Dell to deploy our AI systems around the world to the biggest customers we have. This is not something you can take basic AI and just slap on a machine to do.”

Gu said the company’s revenue is “strong,” having grown “threefold” in the past year. In fact, he says InsightFinder wasn’t looking to raise this Series B at all, and investors approached the company after it won seven rounds of funding with a Fortune 50 company within three months.

InsightFinder will use the new capital to develop its first sales and marketing strategy to grow its team of less than 30 people and invest in its sales. The company has so far raised $35 million.