The landscape of observability tools continues its dynamic evolution. Over the years, the market for solutions dedicated to ensuring the reliability of technology systems has expanded significantly, with its core focus shifting from merely "tracking everything" to the more strategic objective of "controlling complexity and costs." Concurrently, the rapid proliferation and integration of AI agents within enterprises have introduced an entirely new category of workloads that demand rigorous observation.
InsightFinder AI, a startup built upon 15 years of academic research, is uniquely positioned to address these intricate challenges.
Since 2016, the company has leveraged machine learning to effectively monitor, identify, and proactively resolve IT infrastructure issues. Now, InsightFinder is tackling the contemporary issue of AI model reliability with an advanced AI agent solution that encompasses a full spectrum of capabilities, from detection and diagnosis to remediation and prevention.
TechCrunch has exclusively learned that the company, founded by CEO Helen Gu—a computer science professor at North Carolina State University with prior experience at IBM and Google—recently secured $15 million in a Series B funding round, led by Yu Galaxy.
According to Gu, the paramount challenge confronting the industry today extends beyond simply monitoring and diagnosing where AI models falter; it involves comprehensively understanding the operational dynamics of the entire tech stack now that AI components are deeply embedded within 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 explained to TechCrunch. “It’s not always a model problem or a data problem; it’s a combination. Sometimes, it’s simply your infrastructure.”
Gu illustrated this point with a real-world scenario: one of their clients, a major U.S. credit card company, observed drift in a fraud detection model. Because InsightFinder continuously monitored all of the company’s infrastructure, it was able to pinpoint that the model drift was caused by an outdated cache within specific server nodes.
“The biggest misconception is that AI observability is limited to LLM evaluation during the development and testing phases. On the contrary, a sound AI observability platform should provide end-to-end feedback loop support covering the development, evaluation, and production stages,” she emphasized.
InsightFinder’s latest product, named Autonomous Reliability Insights, achieves this by combining unsupervised machine learning, proprietary large and small language models, predictive AI, and causal inference. This foundational layer is data agnostic, as per Gu, enabling the system to ingest and analyze entire data streams to gather signals, which are then correlated and cross-validated to determine the root cause.
The observability sector is currently densely populated with competitors vying for market share, spurred by the proliferation of AI tools. For nearly a decade, InsightFinder has been competing against established players such as Grafana Labs, Fiddler, Datadog, Dynatrace, New Relic, and BigPanda, all of whom are developing capabilities to address the new complexities introduced by AI.
Yet, Gu remains unfazed. She asserts that InsightFinder’s specialized expertise, extensive experience, and high degree of customizability serve as a robust competitive advantage. “We actually rarely lose [customers] to anybody so far […] This is about the insights, right? The problem is that a lot of data scientists understand AI, but they don’t understand the system. And a lot of SRE [site reliability engineering] developers understand the system, but not the AI […] They don’t look at it, and they don’t understand the intrinsic relationships.”
Today, InsightFinder boasts an impressive client roster, including UBS, NBCUniversal, Lenovo, Dell, Google Cloud, and Comcast. Gu attributes this success to the company's decade-long commitment to understanding the specific requirements of its large enterprise customers.
“It has come down to working with our Fortune 50 customers to polish and understand the enterprise environment requirements to deploy these kinds of models,” she stated. “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 that.”
Gu reported that the company’s revenue stream is "strong," having surged "over threefold" in the past year. She further revealed that InsightFinder had not actively sought this Series B funding; rather, investors approached the company after it secured a seven-figure deal with a Fortune 50 company within a mere three months.
The newly acquired capital will be strategically allocated to InsightFinder’s initial sales and marketing hires, expanding its team of fewer than 30 individuals, and investing in its broader go-to-market strategy. To date, the company has raised a total of $35 million.
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