Our co-founders, Matthew Stump and Avinash Mandava, have spent much of their careers helping enterprises and startups adopt and run, mission-critical data infrastructure technologies. They’ve witnessed firsthand how quickly engineering teams can become overwhelmed and stressed as they try to manage the ever-increasing number of components that comprise any large system.
When things start to go wrong, the depth of knowledge required to operate and troubleshoot each component system (for instance, Cassandra, Kafka or Elasticsearch), makes it virtually impossible for a human team to scale and solve problems fast, without experiencing burnout.
That’s when Matthew and Avinash started to envision a different way to manage infrastructure.
At Vorstella we see a future of autonomous systems, where teams pull the tech they want off the shelf, plug it into an A.I. agent, and let the deployment run itself. We believe infrastructure vendors can—and should—instrument their systems to enable more autonomous control.
So that’s what we have built. Using machine learning and A.I. to automate systems management, one skill at a time, we’re changing the status quo and rethinking the economics of infrastructure operations.
Vorstella’s AI agent can forecast future problems, identify an outage root cause, and walk engineers through complex remediation procedures. Vorstella works with your existing teams to keep your systems available and performant, so you can say goodbye to stress and instead focus on system design, innovation & strategy.
As a leading expert in distributed systems, Matt has architected and supported some of the largest deployments in existence. He has contributed significant functionality to a number of popular open source projects including Kubernetes, Apache Cassandra, and Apache Spark.
Avinash has a deep passion for cutting-edge infrastructure technologies. He has spent his whole career working with fast-growth startups and Fortune 100 companies and helping their engineering orgs adopt and run mission-critical, large-scale systems in production.
David is an expert in developing machine learning algorithms that can be used in domains under significant uncertainty—with randomness, imprecise data, hidden information, and partial observability. For Vorstella, he has developed deep learning methods that can significantly increase prediction and classification accuracy of time series signals.