InsightFinder Raises $15M to Secure Enterprise AI Agent Reliability and Performance

InsightFinder AI has successfully closed a $15 million Series B funding round to tackle the emerging challenge of AI agent reliability. Led by Yu Galaxy, the investment brings the company’s total capital raised to $35 million. This infusion of cash follows a year of massive growth where the startup tripled its revenue and secured major contracts with Fortune 50 enterprises.

The company’s flagship offering, Autonomous Reliability Insights, uses a composite AI approach to monitor and fix IT infrastructure issues before they cause outages. Unlike traditional tools, it analyzes the interplay between data, models, and hardware simultaneously. This allows teams to determine if an AI failure is a software bug or an infrastructure bottleneck.

Founded by Dr. Helen Gu, a computer science professor and former engineer at Google and IBM, InsightFinder is built on fifteen years of academic research. The platform functions as an "immune system" for digital operations, focusing on proactive incident prevention. It is currently used by global giants such as UBS, Lenovo, Dell, and Comcast to maintain system uptime. The observability space is increasingly crowded with established players like Datadog and Dynatrace racing to add AI capabilities. However, InsightFinder distinguishes itself by bridging the gap between site reliability engineering and data science. Its specialized engine correlates signals across the entire stack to find root causes that others might miss.

A recent case study involving a major credit card provider highlighted the platform’s unique value. When a fraud detection model began to drift, InsightFinder identified the cause as an outdated server cache rather than a flaw in the AI itself. This level of cross-validation is critical as companies move from testing AI to deploying it in production.

With the new funding, the company plans to expand its team of thirty employees by making its first formal hires in sales and marketing. The goal is to accelerate go-to-market efforts and scale engineering teams globally. This shift marks a transition from a research-heavy operation to a commercially aggressive market contender.