AI observability startup InsightFinder has raised $15 million in a Series B funding round led by Yu Galaxy, as it looks to tackle one of the biggest emerging challenges in enterprise AI—understanding where and why AI agents fail.
Founded by CEO Helen Gu, a computer science professor at North Carolina State University with prior experience at IBM and Google, the company focuses on improving reliability across complex AI-driven systems.
InsightFinder’s platform goes beyond traditional monitoring tools by analyzing the entire technology stack—data, models, and infrastructure together—to detect, diagnose, and prevent failures in AI systems. Gu emphasized this challenge, stating, “It’s not always a model problem or a data problem; it’s a combination. Sometimes, it’s simply your infrastructure.”
The company’s solution uses machine learning, predictive analytics, and causal inference to provide end-to-end observability, covering development, evaluation, and production stages. This allows enterprises to identify issues such as model drift, infrastructure bottlenecks, or data inconsistencies in real time.
A real-world example highlighted how the platform helped a financial services company detect that a fraud detection model’s performance drop was caused not by the model itself but by outdated server cache—demonstrating the importance of holistic system monitoring.
Operating in a competitive space alongside players like Datadog, Dynatrace, and New Relic, InsightFinder differentiates itself through its unified approach to AI and system observability.
The fresh funding will be used to expand the company’s team, particularly in sales and marketing, and accelerate go-to-market efforts as enterprises increasingly adopt AI agents across operations.
