INSUBCONTINENT EXCLUSIVE:
InsightFinder, a startup from North Carolina based on 15 years of academic research, wants to bring machine learning to system monitoring
to automatically identify and fix common issues
Today, the company announced a $2 million seed round.
​IDEA Fund Partners, a VC out of Durham, N.C.,​ led the round, with
participation from ​Eight Roads Ventures​ and Acadia Woods Partners
The company was founded by North Carolina State University professor Helen Gu, who spent 15 years researching this problem before launching
the startup in 2015.
Gu also announced that she had brought on former Distil Networks co-founder and CEO Rami Essaid to be chief operating
Essaid, who sold his company earlier this year, says his new company focuses on taking a proactive approach to application and
infrastructure monitoring.
&We found that these problems happen to be repeatable, and the signals are there
We use artificial intelligence to predict and get out ahead of these issues,& he said
He adds that it about using technology to be proactive, and he says that today the software can prevent about half of the issues before they
even become problems.
If you&re thinking that this sounds a lot like what Splunk, New Relic and Datadog are doing, you wouldn&t be wrong,
but Essaid says that these products take a siloed look at one part of the company technology stack, whereas InsightFinder can act as a
layer on top of these solutions to help companies reduce alert noise, track a problem when there are multiple alerts flashing and completely
automate issue resolution when possible.
&It the only company that can actually take a lot of signals and use them to predict when something
It doesn&t just help you reduce the alerts and help you find the problem faster, it actually takes all of that data and can crunch it using
artificial intelligence to predict and prevent [problems], which nobody else right now is able to do,& Essaid said.
For now, the software is
installed on-prem at its current set of customers, but the startup plans to create a SaaS version of the product in 2020 to make it
accessible to more customers.
The company launched in 2015, and has been building out the product using a couple of National Science
Foundation grants before this investment
Essaid says the product is in use today in 10 large companies (which he can&t name yet), but it doesn&t have any true go-to-market motion
The startup intends to use this investment to begin to develop that in 2020.
