InsightFinder raises $15M to help companies figure out where AI agents go wrong
Startup's platform identifies root causes like outdated cache causing model drift for major banks.
InsightFinder, a startup built on 15 years of academic research, has secured $15 million in Series B funding led by Yu Galaxy to tackle the growing problem of AI agent reliability in enterprise systems. Founded by CEO Helen Gu, a computer science professor with stints at IBM and Google, the company is shifting from traditional IT infrastructure monitoring to solving the complex, interconnected failures that occur when AI models are integrated into production tech stacks. Its core argument is that diagnosing AI issues requires observing the data, model, and infrastructure as a unified system, not in isolation.
The company's flagship product, Autonomous Reliability Insights, uses a combination of unsupervised machine learning, proprietary language models, and causal inference to provide an end-to-end observability platform. It is designed to be data-agnostic, ingesting entire data streams to correlate signals and identify root causes. In a real-world example, the platform helped a major U.S. credit card company diagnose fraud-detection model drift by tracing the problem not to the AI model itself, but to outdated cache in specific server nodes—a systemic infrastructure issue invisible to siloed monitoring tools.
InsightFinder now competes in a crowded observability market against giants like Datadog, Dynatrace, and New Relic, all of which are adding AI capabilities. However, Gu claims a moat based on deep expertise in correlating AI behavior with system performance, a need highlighted by the disconnect between data scientists and site reliability engineers. The company's Fortune 50 client roster, including UBS, NBCUniversal, and Google Cloud, validates its approach to providing actionable insights across the development, evaluation, and production lifecycle of AI agents.
- Raised $15M Series B led by Yu Galaxy to expand AI agent observability platform.
- Solves cross-stack failures: identified model drift caused by outdated server cache for a major credit card company.
- Platform uses unsupervised ML & causal inference for end-to-end monitoring of data, models, and infrastructure.
Why It Matters
As enterprises deploy AI agents, tools that diagnose failures across the entire tech stack are critical for reliability and cost control.