InsightFinder raises $15M to help companies figure out where AI agents go wrong

The role of observability tools has evolved once again. While the market for solutions to ensure tech systems’ reliability has grown over the years, the center of gravity has steadily shifted from “track everything” to “control complexity and costs.” Meanwhile, the rapid influx and adoption of AI agents within enterprises have only added a brand-new category of workload that needs to be observed.

InsightFinder AI, a startup based on 15 years of academic research, is no stranger to this problem.

The company has been using machine learning to monitor, identify, and proactively fix IT infrastructure issues since 2016 and is now attacking today’s AI model reliability issue with an AI agent solution 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 exclusively learned.

According to Gu, the biggest problem facing the industry today is not just monitoring and diagnosing where AI models go wrong; it’s diagnosing how the entire tech stack operates now that AI is a part of 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 told TechCrunch. “It’s not always a model problem or a data problem; it’s a combination. Sometimes, it’s simply your infrastructure.”

Gu explained how that looks in real life with an anecdote: One of its customers, a major U.S. credit card company, saw that one of its fraud-detection models was drifting. Because InsightFinder was monitoring all of the company’s infrastructure, it was able to identify that the model drift was caused by outdated cache in some server nodes.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 said.

InsightFinder’s newest product, dubbed Autonomous Reliability Insights, can do all this by using a combination of unsupervised machine learning, proprietary large and small language models, predictive AI, and causal inference. This base layer is data agnostic, per Gu, which lets the system ingest and analyze entire data streams to gather signals that can then be correlated and cross-validated to arrive at a root cause.

Now, the observability space is crowded with contenders for a share of the new market that’s been opened up by the influx of AI tools. Nearly a decade into its journey, InsightFinder has been going up against the likes of Grafana Labs, Fiddler, Datadog, Dynatrace, New Relic, and BigPanda, who are all building capabilities to deal with the new problems presented by AI tools.

But Gu isn’t fazed. On the contrary, she claims that InsightFinder’s expertise, experience, and customizability act as a sufficient moat. “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’s roster of customers includes UBS, NBCUniversal, Lenovo, Dell, Google Cloud, and Comcast. Gu attributes the success to 10 years of working to understand what large enterprise customers need.

“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 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.”

Gu said the company’s revenue stream is “strong,” having grown “over threefold” in the past year. In fact, she says InsightFinder wasn’t looking to raise this Series B at all, and investors approached the company after it won a seven-figure deal with a Fortune 50 company within three months.

InsightFinder will use the fresh capital to make its first sales and marketing hires to expand its team of fewer than 30 people and invest in its go-to-market motion. The company has so far raised a total of $35 million.