Too often, IT operations teams focus on fixing outages rather than proactively solving problems. The challenge is analyzing data to understand what’s happening with various systems and figure out how to most effectively resolve pressing problems. There’s no silver bullet, but a relatively new startup, InsightFinder, claims to offer a solution in a platform that algorithmically predicts IT infrastructure issues and attempts to automatically resolve them.

Founded by Helen Gu, a computer science professor at North Carolina State University who formerly worked at IBM and Google, InsightFinder is designed to continuously learn from machine data to identify and fix problems before they impact web or app performance. It’s not a new idea — companies like BigPanda and Moogsoft offer tools along these lines — but InsightFinder has enthusiastic investors, some of which contributed $10 million to the startup’s coffers ($17 million, inclusive of the $10 million) in a Series A round that closed today.

“The pandemic has made businesses more cautious about their spending. However, businesses are searching for ways to become more efficient, distributed and nimble,” Gu told TechCrunch in an email interview. “This is where InsightFinder can help businesses as they adapt and scale.”

Powered by an AI system, InsightFinder — which integrates with existing monitoring tools like Datadog, PagerDuty and New Relic — learns from logs, traces and triage data from engineers to bubble up root causes and predict and even remediate incidents. Where possible, the platform attempts to restart, auto-scale or migrate resources like containers upon identifying a problem.

To avoid unnecessary data transfers and preserve privacy, InsightFinder uses a federated learning approach, where data is analyzed locally and only high-level insights are sent across public networks. Users can access a dashboard to view patterns and trends as well as estimations of downtime savings, cost of labor savings and the number of incidents resolved.

Gu claims that, using InsightFinder, a Fortune 50 tech company was able to predict IT incidents between two to 12 hours ahead of time.

“InsightFinder can help businesses understand their efficiencies and inefficiencies, and help them lean into their strengths,” Gu said. “Although our number of customers is not large today due to the time and resources we spend with these strategic customers, this will change significantly as we scale from this funding round.”

With the same unbridled optimism, Gu told me that InsightFinder — which isn’t making its revenue public at present — will grow its workforce from 20 people today to 25 by the end of the year. The goal is to eventually expand InsightFinder’s platform to predict more than just IT outages, for example unusually high cloud compute bills.

Silicon Valley Future Capital led InsightFinder’s Series A, with participation from Yu Star Fund, Acadia Woods Partners, Eight Roads Ventures, Eastlink Capital, Fellows Fund, IDEA Fund Partners and Triangle Tweener Fund.


Other Resources

A major credit card company’s mobile payment service experienced severe performance degradation on a Friday afternoon.
InsightFinder utilizes the industry’s best unsupervised multivariate machine learning algorithms to analyze a large amount of production system data.