NeuBird founders Goutham Rao and Vinod Jayaraman came from Portworx, a cloud-native storage solution they eventually sold to PureStorage in 2019 for $370 million. It was their third successful exit.
When they went looking for their next startup challenge last year, they saw an opportunity to combine their cloud-native knowledge, especially around IT operations, with the burgeoning area of generative AI.
Today NeuBird announced a $22 million investment from Mayfield to get the idea to market. It’s a hefty amount for an early-stage startup, but the firm is likely banking on the founders’ experience to build another successful company.
Rao, the CEO, says that while the cloud-native community has done a good job at solving a lot of difficult problems, it has created increasing levels of complexity along the way.
“We’ve done an incredible job as a community over the past 10-plus years building cloud-native architectures with service-oriented designs. This added a lot of layers, which is good. That’s a proper way to design software, but this also came at a cost of increased telemetry. There’s just too many layers in the stack,” Rao told TechCrunch.
They concluded that this level of data was making it impossible for human engineers to find, diagnose and solve problems at scale inside large organizations. At the same time, large language models were beginning to mature, so the founders decided to put them to work on the problem.
“We’re leveraging large language models in a very unique way to be able to analyze thousands and thousands of metrics, alerts, logs, traces and application configuration information in a matter of seconds and be able to diagnose what the health of the environment is, detect if there’s a problem and come up with a solution,” he said.
The company is essentially building a trusted digital assistant to the engineering team. “So it’s a digital co-worker that works alongside SREs and ITOps engineers, and monitors all of the alerts and logs looking for issues,” he said. The goal is to reduce the amount of time it takes to respond to and solve an incident from hours to minutes, and they believe that by putting generative AI to work on the problem, they can help companies achieve that goal.
The founders understand the limitations of large language models, and are looking to reduce hallucinated or incorrect responses by using a limited set of data to train the models, and by setting up other systems that help deliver more accurate responses.
“Because we’re using this in a very controlled manner for a very specific use case for environments we know, we can cross check the results that are coming out of the AI, again through a vector database and see if it’s even making sense and if we’re not comfortable with it, we won’t recommend it to the user.”
Customers can connect directly to their various cloud systems by entering their credentials, and without moving data, NeuBird can use the access to cross-check against other available information to come up with a solution, reducing the overall difficulty associated with getting the company-specific data for the model to work with.
NeuBird uses various models, including Llama 2 for analyzing logs and metrics. They are using Mistral for other types of analysis. The company actually turns every natural language interaction into a SQL query, essentially turning unstructured data into structured data. They believe this will result in greater accuracy.
The early-stage startup is working with design and alpha partners right now refining the idea as they work to bring the product to market later this year. Rao says they took a big chunk of money out of the gate because they wanted the room to work on the problem without having to worry about looking for more money too soon.
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