InsightFinder AI Raises $15M Series B to Deliver End-to-End AI Observability for Enterprise Tech Stacks
Observability tools have entered a new era of evolution. Though the market for tech system reliability solutions has expanded steadily for years, its core focus has gradually shifted from the early "track everything" mindset to today’s priority of taming complexity and cutting unnecessary costs. At the same time, the rapid, widespread adoption of AI agents across enterprises has created an entirely new category of observability workloads that did not exist before.
InsightFinder AI, a startup rooted in 15 years of academic research, is uniquely positioned to tackle this new challenge. The company has leveraged machine learning to monitor, detect, and proactively resolve IT infrastructure issues since 2016, and it now addresses modern AI model reliability gaps with a full-stack AI agent solution that covers the entire workflow from problem detection and diagnosis to remediation and prevention.
TechCrunch has learned exclusively that the company—founded by CEO Helen Gu, a computer science professor at North Carolina State University with prior stints at IBM and Google—recently closed a $15 million Series B funding round led by Yu Galaxy.
According to Gu, the biggest challenge the industry faces today extends far beyond just monitoring and diagnosing where AI models fail. With AI now integrated into every layer of enterprise tech, the real problem is understanding how the entire stack operates as a unified system.
"To diagnose AI model problems, you have to actually monitor and analyze data, the model itself, and underlying infrastructure all together," Gu told TechCrunch. "Issues aren’t always purely a model problem or purely a data problem; they’re a combination of all layers. Sometimes, the root cause is simply your infrastructure."
Gu illustrated this dynamic with a real customer example: a major U.S. credit card provider noticed unexpected model drift in one of its fraud detection tools. Because InsightFinder monitored the company’s full infrastructure stack, it quickly traced the drift to outdated cache on a small set of server nodes, rather than a flaw in the model or training data.
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"The biggest misconception in the space right now is that AI observability is limited to LLM evaluation during the development and testing phases. On the contrary, a robust AI observability platform should provide end-to-end feedback loop support covering development, evaluation, and production stages," Gu said.
InsightFinder’s newest offering, named Autonomous Reliability Insights, delivers this full coverage by combining unsupervised machine learning, proprietary large and small language models, predictive AI, and causal inference. Per Gu, the platform’s core base layer is data-agnostic, which allows it to ingest and analyze entire end-to-end data streams, aggregate relevant signals, then correlate and cross-validate those signals to pinpoint an accurate root cause.
The modern observability space is crowded with competitors vying for a share of the new market opened by the AI boom. Nearly a decade into its journey, InsightFinder competes with established players including Grafana Labs, Fiddler, Datadog, Dynatrace, New Relic, and BigPanda, all of which are building out new capabilities to address the unique challenges AI tools bring.
But Gu is unfazed by the crowded field. She argues that InsightFinder’s deep expertise, years of hands-on experience, and customizability create a durable competitive moat: "We actually rarely lose customers to any competitor so far … This work all comes down to actionable insights, right? The problem is that a lot of data scientists understand AI, but they don’t understand full systems. And a lot of SRE (site reliability engineering) developers understand the system, but not AI … They don’t examine the full stack, so they can’t understand the intrinsic relationships between layers."
Today, InsightFinder’s customer roster includes major global brands including UBS, NBCUniversal, Lenovo, Dell, Google Cloud, and Comcast. Gu attributes this traction to 10 years of refining the product to meet the needs of large enterprise clients.
"It all comes down to working with our Fortune 50 customers to refine and understand the requirements of enterprise environments for deploying these kinds of AI models," she said. "We have worked with Dell to deploy our AI systems across the world at some of our largest customers. This is not something you can build by just taking a generic foundational AI and slapping it on top of machine data."
Gu shared that InsightFinder’s revenue is strong, with growth of more than threefold over the past year. In fact, the company was not actively seeking this Series B round at all: investors reached out to InsightFinder after it closed a seven-figure deal with a Fortune 50 company in less than three months.
InsightFinder will use the new capital to hire its first dedicated sales and marketing team members, expand its current headcount of fewer than 30 employees, and scale its go-to-market motion. To date, the startup has raised a total of $35 million.
InsightFinder AI Raises $15M Series B to Deliver End-to-End AI Observability for Enterprise Tech Stacks
