The role of observability tools has evolved again. While the market for solutions to ensure the reliability of technology systems has grown over the years, the center of gravity has steadily shifted from “track everything” to “control complexity and cost.” Meanwhile, the rapid influx and deployment of AI agents within enterprises has only added a whole new category of workloads that need to be observed.
InsightFinder AI, a startup based on 15 years of academic research, is not immune to this problem.
Since 2016, the company has been using machine learning to monitor, identify, and proactively remediate issues in its IT infrastructure. We are currently tackling the reliability issues of today’s AI models with AI agent solutions 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 learned exclusively.
According to Gu, the biggest problem facing the industry today is not just monitoring and diagnosing what’s wrong with AI models. AI is being built into the entire technology stack to diagnose how it all works.
“Diagnosing problems with these AI models requires actually monitoring and analyzing the data, models, and infrastructure together,” Gu told TechCrunch. “It’s not necessarily a model problem or a data problem; it’s a combination of those things. Sometimes it’s just an infrastructure problem.”
Mr. Gu explained with an anecdote how it would actually work. One of our customers, a large credit card company in the United States, noticed that one of its fraud detection models was drifting. Because InsightFinder monitored all of the company’s infrastructure, it was able to identify that model drift was caused by stale cache on some server nodes.
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“The biggest misconception is that AI observability is limited to LLM evaluation during the development and testing stages. On the contrary, a healthy AI observability platform should provide end-to-end feedback loop support that covers the development, evaluation, and production stages,” she said.
InsightFinder’s latest product, called Autonomous Reliability Insights, can do all this by using a combination of unsupervised machine learning, proprietary large and small model language models, predictive AI, and causal inference. According to Gu, this base layer is data agnostic and allows the system to ingest and analyze the entire data stream to collect signals and correlate and cross-validate them to get to the root cause.
The observability space is currently crowded with competitors vying for a share of the new market opened up by the influx of AI tools. InsightFinder has been on its journey for nearly a decade and has gone head-to-head with the likes of Grafana Labs, Fiddler, Datadog, Dynatrace, New Relic, and BigPanda. All of these companies are building capabilities to address new problems posed by AI tools.
But Mr. Gu is unfazed. On the contrary, she claims that InsightFinder’s expertise, experience, and customizability serve as a sufficient moat. “So far, we have very rarely actually lost[customers]to anyone (…) This is a story about insights, right? The problem is that a lot of data scientists understand AI, but they don’t understand systems. And a lot of SRE (site reliability engineering) developers understand systems, but they don’t understand AI (…) They don’t look at systems, they don’t understand the essential relationships.”
InsightFinder’s customer roster now includes UBS, NBCUniversal, Lenovo, Dell, Google Cloud, and Comcast, and Gu attributes this success to a decade of work to understand what large enterprise customers need.
“Ultimately, it became important for us to work with our Fortune 50 customers to hone and understand their enterprise environment requirements for deploying this type of model,” she said. “We’ve worked with Dell to deploy AI systems to some of our largest customers around the world. This is something you can do with basic AI and do more than just input machine data.”
Gu said the company’s revenue streams are “strong” and have grown “more than three times” in the past year. In fact, she said the company never considered this Series B funding, and investors approached the company after the company secured a seven-figure deal with a Fortune 50 company within three months.
InsightFinder will use the new funding to make its first sales and marketing hires, expand its team of less than 30 people, and invest in go-to-market efforts. The company has raised a total of $35 million to date.
