The function of observability platforms has undergone another transformation. Although the sector for technologies that maintain the reliability of technical systems has expanded, the primary focus has gradually moved from a "track everything" approach to one that emphasizes "controlling complexity and costs." Concurrently, the swift introduction and integration of AI agents into business operations has introduced an entirely new type of workload requiring observation.

InsightFinder AI, a startup founded on fifteen years of academic research, is deeply familiar with this challenge. Since 2016, the company has employed machine learning to monitor, identify, and preemptively resolve IT infrastructure problems. It is now addressing the contemporary issue of AI model reliability with a solution for AI agents capable of handling detection, diagnosis, remediation, and prevention.

According to Gu, the most significant challenge for the industry today extends beyond merely monitoring and diagnosing AI model failures. It involves diagnosing the performance of the entire technology stack now that AI is integrated into it. "It's not always a model problem or a data problem; it's a combination. Sometimes, it's simply your infrastructure," she noted.

Gu illustrated this with a real-world example involving a major U.S. credit card company, a customer that observed drift in one of its fraud detection models. Because InsightFinder monitored the company's complete infrastructure, it identified that the model drift originated from outdated cache in certain server nodes.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 explained.

The company's latest product, named Autonomous Reliability Insights, accomplishes this by integrating unsupervised machine learning, proprietary large and small language models, predictive AI, and causal inference. Gu states this foundational layer is data-agnostic, enabling the system to ingest and analyze complete data streams. It gathers signals that can then be correlated and cross-validated to pinpoint a root cause.

The observability field is now crowded with competitors vying for a share of the new market created by the influx of AI tools. After nearly a decade in operation, InsightFinder competes with firms like Grafana Labs, Fiddler, Datadog, Dynatrace, New Relic, and BigPanda, all of which are developing capabilities to tackle the novel problems presented by AI. However, Gu remains undaunted. She asserts that InsightFinder's expertise, experience, and customizability form a sufficient competitive barrier. "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," she said.

InsightFinder's current client portfolio includes UBS, NBCUniversal, Lenovo, Dell, Google Cloud, and Comcast. Gu credits this success to a decade spent understanding the needs of 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 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 reported the company's revenue stream is "strong," having grown "over threefold" in the past year. She revealed the company was not initially seeking to raise this Series B round; investors approached them after InsightFinder secured a seven-figure deal with a Fortune 50 company within three months. The new capital will be used for its first dedicated sales and marketing hires to expand its team of fewer than 30 people and to invest in its go-to-market strategy. To date, InsightFinder has raised a total of $35 million.