InsightFinder AI closed a $15 million Series B round led by Yu Galaxy, bringing total funding to $35 million. The company plans to make its first dedicated sales and marketing hires to expand a team of fewer than 30 people, TechCrunch reported exclusively on April 16.
The Product
InsightFinder’s newest offering, Autonomous Reliability Insights, uses unsupervised machine learning, proprietary large and small language models, predictive AI, and causal inference to monitor AI systems end-to-end. The platform ingests entire data streams across models, data pipelines, and infrastructure, then cross-validates signals to arrive at root causes.
The core thesis: AI agent failures don’t fit neatly into existing monitoring categories. A drifting fraud detection model might be caused by outdated cache on server nodes, not a model problem. Traditional APM and log aggregation tools can’t see that because they monitor infrastructure and application layers separately.
“In order to diagnose these AI model problems, you need to actually monitor and analyze the data, the model, and the infrastructure together,” CEO Helen Gu told TechCrunch. “It’s not always a model problem or a data problem; it’s a combination. Sometimes, it’s simply your infrastructure.”
The Customer Base
InsightFinder’s roster includes UBS, NBCUniversal, Lenovo, Dell, Google Cloud, and Comcast. Gu said revenue has grown “over threefold” in the past year. The company wasn’t actively seeking a Series B; investors approached after InsightFinder won a seven-figure deal with a Fortune 50 company within three months, according to TechCrunch.
The company was founded by Gu, a computer science professor at North Carolina State University who previously worked at IBM and Google. InsightFinder has been using machine learning for IT infrastructure reliability since 2016, built on 15 years of academic research.
The Competitive Landscape
The observability market is crowded. Grafana Labs, Fiddler, Datadog, Dynatrace, New Relic, and BigPanda are all building AI-specific monitoring capabilities. InsightFinder’s bet is that nearly a decade of enterprise deployment experience gives it an edge competitors can’t replicate quickly.
“A lot of data scientists understand AI, but they don’t understand the system. And a lot of SRE developers understand the system, but not the AI,” Gu said. “We have been working with Dell to deploy our AI systems across the world at some of the largest customers we have. This is not something that you can take a foundational AI and just slap on the machine data to do.”
The funding validates a growing category: as enterprises shift from deploying individual AI agents to orchestrating teams of autonomous agents, the failure modes multiply. Agents interacting with each other, with legacy infrastructure, and with real-time data create failure patterns that no single monitoring layer can capture alone.