Startup uses machine learning to monitor, identify, and fix IT infrastructure issues in complex tech stacks
A recent funding round has brought attention to a pressing issue in the tech industry: ensuring the reliability of AI models. InsightFinder, a startup founded by computer science professor Helen Gu, has raised $15 million in a Series B round led by Yu Galaxy.
The company’s expertise lies in using machine learning to monitor and fix IT infrastructure issues since 2016. With the influx of AI agents in enterprises, the complexity of tech stacks has grown exponentially, making it increasingly difficult for companies to identify and address problems.
InsightFinder’s latest product, Autonomous Reliability Insights, aims to bridge this gap by providing real-time detection and diagnosis of AI model issues. The platform covers the entire AI lifecycle, from development to production stages, offering end-to-end feedback loop support.
According to Gu, one of the biggest misconceptions in AI observability is that it’s limited to evaluating Large Language Models (LLMs) during development and testing phases. In reality, a robust AI observability platform should monitor and analyze data, models, and infrastructure together to identify problems.
What matters
- Autonomous Reliability Insights product detects and diagnoses AI model problems in real-time
- Monitoring platform covers development, evaluation, and production stages of AI lifecycle
- InsightFinder aims to simplify AI observability for enterprises with rapidly growing AI workloads
Why it matters
InsightFinder aims to simplify AI observability for enterprises with rapidly growing AI workloads
This GenAI News article was prepared in original wording using reporting and materials published by TechCrunch AI. Source reference: https://techcrunch.com/2026/04/16/insightfinder-raises-15m-to-help-companies-figure-out-where-ai-agents-go-wrong/.
Drafted by the GenAI News review pipeline.