The function of observability tools has evolved once again. While the market for solutions ensuring tech systems’ reliability has expanded over time, the focus has steadily shifted from extensive monitoring to managing complexity and costs. Concurrently, the rapid integration and adoption of AI agents within enterprises have introduced an entirely new category of workloads that require observation.
InsightFinder AI, a startup founded on 15 years of academic research, is well-versed in tackling this problem.
Since 2016, the company has leveraged machine learning to monitor, identify, and proactively resolve IT infrastructure issues, and is now addressing current AI model reliability challenges with an AI agent solution capable of everything from detection and diagnosis to remediation and prevention, as noted since 2016.
The company, founded by CEO Helen Gu, a computer science professor at North Carolina State University who previously worked at IBM and Google, recently secured $15 million in a Series B funding round led by Yu Galaxy, TechCrunch has exclusively learned.
According to Gu, the most significant challenge facing the industry today isn’t just monitoring and diagnosing where AI models malfunction; it’s diagnosing how the entire technological stack operates now that AI is an integral component.
“To diagnose these AI model problems, it’s crucial to concurrently monitor and analyze the data, the model, and the infrastructure,” Gu explained to TechCrunch. “It’s not always solely a model problem or a data problem; it’s frequently a combination. Sometimes, it’s simply your infrastructure.”
Gu illustrated this with an anecdote: one of its customers, a major U.S. credit card company, observed that a fraud-detection model was exhibiting drift. By monitoring all of the company’s infrastructure, InsightFinder was able to determine that the model drift was caused by outdated cache in some server nodes.
“The primary misconception is that AI observability is confined to evaluating large language models during the development and testing phases. Conversely, an effective AI observability platform should offer comprehensive feedback loop support covering the development, evaluation, and production stages,” she stated.
InsightFinder’s newest offering, named Autonomous Reliability Insights, achieves all of this by utilizing a combination of unsupervised machine learning, proprietary large and small language models, predictive AI, and causal inference. This foundational layer is data-agnostic, as per Gu, allowing the system to ingest and analyze entire data streams to collect signals that can then be correlated and cross-validated to ascertain the root cause.
Presently, the observability sector is crowded with numerous contenders vying for a share of the new market opened up by the proliferation of AI tools. Nearly a decade into its journey, InsightFinder has been competing with companies such as Grafana Labs, Fiddler, Datadog, Dynatrace, New Relic, and BigPanda, all of whom are developing capabilities to address the fresh problems introduced by AI tools.
But Gu remains unperturbed. On the contrary, she asserts that InsightFinder’s expertise, experience, and customizability serve as a sufficient competitive advantage. “We’ve actually rarely lost [customers] to anybody so far… This is about the insights, isn’t it? The challenge is that many data scientists comprehend AI but lack system understanding. Similarly, many SRE [site reliability engineering] developers understand the system but not the AI… They fail to observe and understand the intrinsic relationships.”
Today, InsightFinder’s roster of customers includes UBS, NBCUniversal, Lenovo, Dell, Google Cloud, and Comcast. Gu attributes this success to 10 years of working to understand what large enterprise customers genuinely require.
“Our focus has been on collaborating with our Fortune 50 clients to refine and comprehend the enterprise environment requirements for deploying these kinds of models,” she stated. “We’ve partnered with Dell to implement our AI systems globally for some of our largest customers. This is not a task where you can simply take a foundational AI and merely apply it to machine data.”
Gu reported that the company’s revenue stream is “strong,” having grown “over threefold” in the past year. In fact, she noted that InsightFinder was not actively seeking to raise this Series B funding at all, and investors approached the company after it secured a seven-figure deal with a Fortune 50 company within just three months.
InsightFinder will utilize the fresh capital to make its first sales and marketing hires, expand its team of fewer than 30 people, and invest in its go-to-market strategy. The company has now accumulated a total of $35 million in funding.