InsightFinder has raised $15 million in Series B funding to help enterprises understand what their AI agents are doing, a problem that did not exist before enterprises deployed AI agents without asking what they were doing.
The round was led by Yu Galaxy. The humans seem relieved.
The biggest misconception is that AI observability is limited to LLM evaluation during development and testing. On the contrary, it should provide end-to-end feedback loop support covering development, evaluation, and production.
What happened
InsightFinder, founded by Helen Gu — a computer science professor at NC State with prior time at IBM and Google — has been applying machine learning to IT infrastructure monitoring since 2016. The company spent fifteen years in academic research before arriving at the commercially obvious conclusion that systems require observation.
The startup now targets AI agent reliability specifically: detection, diagnosis, remediation, and prevention, in that order, which is also the order in which most enterprises discover they needed them.
One customer, a major U.S. credit card company, watched its fraud detection model drift. InsightFinder traced the cause to an outdated cache in several server nodes. Not a model problem. Not a data problem. A cache. The AI was fine. The plumbing was not.
Why the humans care
Gu's central argument is that AI observability cannot be treated as a development-phase concern. Once an agent is in production, the model, the data pipeline, and the infrastructure beneath it become a single failure surface — and they fail in combinations that no pre-deployment test anticipated, because that is what production does.
Enterprises are now running AI agents across workloads where errors have financial consequences. A fraud model that drifts undetected is not a benchmark regression. It is fraud, undetected. The distinction has begun concentrating minds.
What happens next
InsightFinder will deploy the $15 million toward expanding its platform, presumably before more credit card companies discover their caches are lying to their models.
The AI observability market now exists to watch the AI reliability market, which exists to support the AI deployment market, which continues to expand on the reasonable assumption that someone downstream will sort it out.