GoKawiilThe role of observability tools has evolved once again. While the market for solutions to ensure tech systems’ reliability has grown over the years, the center of gravity has steadily shifted from “track everything” to “control complexity and costs.” Meanwhile, the rapid influx and adoption of AI agents within enterprises have only added a brand new category of workload that needs to be observed.
InsightFinder AI, a startup based on 15 years of academic research, is no stranger to this problem.
The company has been using machine learning to monitor, identify, and proactively fix IT infrastructure issues since 2016, and is now attacking today’s AI model reliability issue with an AI agent solution that can do everything from detection and diagnosis to remediation and prevention.
The company, founded by CEO Helen Gu, a computer science professor 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 exclusively learned.
According to Gu, the biggest problem facing the industry today is not just monitoring and diagnosing where AI models go wrong; it’s diagnosing how the entire tech stack operates now that AI is a part of it.
“In order to diagnose these AI model problems, you need to actually monitor and analyze the data, the model, and the infrastructure together,” Gu told TechCrunch. “It’s not always a model problem or a data problem; it’s a combination. Sometimes, it’s simply your infrastructure.”
Gu explained how that looks in real life with an anecdote: One of its customers, a major U.S. credit card company, saw that one of its fraud detection models was drifting. Because InsightFinder was monitoring all of the company’s infrastructure, it was able to identify that the model drift was caused by outdated cache in some server nodes.
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