InsightFinder raises $15 million to help companies catch mistakes made by AI agents

The role of monitoring tools has evolved again. While the market for solutions to ensure the reliability of technology systems has grown over the years, the center of gravity has steadily shifted from “tracking everything” to “controlling complexity and costs.” At the same time, the rapid influx of AI agents and their adoption within organizations has added an entirely new category of workload to consider.

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

It was the company Using machine learning To proactively monitor, identify and fix IT infrastructure issues Since 2016 It now addresses today’s AI model reliability problem with an AI agent solution that can do everything from detection and diagnosis to treatment and prevention.

The company, founded by CEO Helen Guo, 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 learned exclusively.

According to Joe, the biggest problem facing the industry today is not just monitoring and diagnosing faults in AI models; It diagnoses how the entire technology stack works now that AI is a part of it.

“In order to diagnose problems with these AI models, you need to observe 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.”

Joe explained what this looks like in real life with an anecdote: One of her clients, a major American 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was monitoring all of the company’s infrastructure, it was able to determine that the model skew was due to an outdated cache on some server nodes.

“The biggest misconception is that AI observability is limited to LLM evaluation during the development and testing phases. Conversely, a sound AI observability platform should provide comprehensive support for feedback spanning development, evaluation, and production phases,” she said.

InsightFinder’s latest product, called Autonomous Reliability Insights, can do all this using a combination of unsupervised machine learning, proprietary large and small language models, predictive AI, and causal inference. This foundational layer is data agnostic, per Gu, which allows the system to ingest and analyze entire data streams to collect signals that can then be correlated and validated to get to the root cause.

Now, the monitoring space is crowded with vying for a share of the new market opened up by the influx of AI tools. Nearly a decade into its journey, InsightFinder has been encountering the likes of Grafana Labs, Fiddler, Datadog, Dynatrace, New Relic, and BigPanda, who are all working to build capabilities to address new problems posed by AI tools.

But Gu is not bothered. On the contrary, it claims that InsightFinder’s expertise, experience, and customizability serve as a sufficient moat. “Actually, we rarely lose[customers]to anyone yet… It’s about the insights, right? The problem is that a lot of data scientists understand the 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, they don’t understand the underlying relationships.”

Today, InsightFinder’s clients include UBS, NBCUniversal, Lenovo, Dell, Google Cloud, and Comcast. Joe attributes this success to 10 years of work understanding what large enterprise customers need.

“It is time to work with our Fortune 50 customers to refine and understand the requirements of the enterprise environment to deploy these types of models,” she said. “We’ve worked with Dell to deploy our AI systems around the world at some of our largest customers. This isn’t something you can take from foundational AI and just tap device data to do.”

Joe said the company’s revenue stream is “strong”, having increased “more than three-fold” in the past year. In fact, she says InsightFinder wasn’t looking to raise that 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 less than 30 people and invest in its go-to-market move. To date, the company has raised a total of $35 million.