NeuBird founders Goutham Rao and Vinod Jayaraman came from PortWorx, a cloud-native storage solution that they eventually sold to PureStorage in 2019 for $370 million. It was their third successful outing.
When they looked for their next startup challenge last year, they saw an opportunity to combine their cloud-native knowledge, particularly in IT operations, with the growing field of generative AI.
Today, Neubird announced a $22 million investment from Madrona to commercialize the idea. This is a considerable sum for an early-stage startup, but the company is likely counting on the founders’ prior experience to build another successful business.
Rao, the CEO, says that while the cloud-native community has done a good job solving many difficult problems, it has created increasing levels of complexity along the way.
“We have done incredible work as a community over the past 10 years creating cloud-native architectures with service-oriented designs. It added a lot of layers, which is nice. This is a good way to design software, but it also came at a cost in increased telemetry. There are just too many layers in the stack,” Rao told TechCrunch.
They concluded that this level of data prevented human engineers from finding, diagnosing and fixing large-scale problems within large organizations. At the same time, large language models were beginning to mature, so the founders decided to put them to work on this problem.
“We leverage 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 seconds and be able to diagnose the state of health of the environment. , detect if there is a problem and find a solution,” he said.
The company is essentially building a trusted digital assistant for the engineering team. “So it’s a digital collaborator that works alongside the SRE and ITOps engineers and monitors all the alerts and logs for issues,” he said. The goal is to reduce the time it takes to respond and resolve an incident from hours to minutes, and they believe that by putting generative AI to work, they can help businesses achieve this goal.
The founders understand the limitations of large language models and seek to reduce hallucinatory or incorrect responses by using a limited set of data to train the models and implementing other systems to provide more accurate responses.
“Because we’re using this in a very controlled way for a very specific use case for environments that we’re familiar with, we can check the results that come out of the AI, again through a vector database and see if that makes sense and if we are not comfortable with it we will not recommend it to the user.
Customers can log in directly to their various cloud systems by entering their credentials, and without moving data, NeuBird can use this access to check against other available information to find a solution, reducing the difficulty overall associated with obtaining company-specific data. for the model to work.
NeuBird uses various models, including Llama 2, to analyze logs and metrics. They use Mistral for other types of analysis. The company actually turns every natural language interaction into an SQL query, essentially turning unstructured data into structured data. They believe this will result in greater accuracy.
The early-stage startup is currently working with design and alpha partners to refine the idea as they work to bring the product to market later this year. Rao says they took out a large amount of money because they wanted the room to work on the problem without having to worry about seeking more money too soon.
techcrunch
