Our team at Resolve AI comes with a wealth of experience in this space. I was an early contributor to Kubernetes at Google where I worked on Kubernetes and associated technologies for ~6 years. More recently, I was the tech lead for the Kubernetes-based compute platform at Robinhood where my teams were in a number of SEVs per year, not necessarily caused by the platform itself but still supported (pretty much the story of life for Infrastructure Engineers everywhere). Our co-founders, Spiros Xanthos and Mayank Agarwal co-created OpenTelemetry at their previous startup Omnition (acquired by Splunk). More recently, Spiros was the GM and Senior Vice President of Splunk Observability and Mayank was the lead architect for all of Splunk's observability product lines. We have all lived the problems we are trying to solve.
Resolve is AI for production engineers. Production systems are dynamic and complex. Addressing common production engineering concerns like incident troubleshooting, cloud operations, security, compliance and cost involves painfully piecing together information from many teams (service on-call rotations, Platform, SRE, etc) and multiple (routinely 10+) different tools (observability, CI/CD, infrastructure, paging, chat, etc). These tools were not designed to work together, pushing the complexity on humans.
Resolve AI is tackling this challenge by building an AI Production Engineer with the goal of automating the majority of tasks across incident management, cloud operations, security engineering, compliance, and cost management. As the first step in our ambitious journey, we are automating incident troubleshooting as it is the most direct way to prevent outages and improve reliability while relieving engineers from the most stressful part of their job. Our goal is to automate the resolution of 80%+ of alerts and incidents without human involvement.
Resolve AI automatically maps and keeps up-to-date a complete knowledge graph of any production environment, without needing any upfront training or user input. It builds knowledge of which tools and signals are relevant for any situation. It comes pre-built with models for various tool categories such as metrics, logs, traces, alerts, seamlessly connecting with category- and vendor-specific products like Prometheus, Splunk, GCP, AWS, Azure and others. These models automatically and continuously adapt to each customer's environment.
With the state-of-the-art reasoning engine that’s composed of multiple agents, Resolve AI is able to investigate novel incidents, accurately determine causality, learn and adapt as it encounters new situations and perform various complex actions.
Generative AI is inherently probabilistic and not always 100% accurate. Without full context, AI models may hallucinate, potentially misleading users. For an AI that takes actions, building user trust is paramount; it must present clear evidence for any decision or action. We address these challenges by building an interface that supports claims with evidence, present findings with context and allow humans to collaborate with the system so that they can guide the system when needed.
Our video demo is on the website. Please take a look. We really appreciate your feedback. We are also happy to hop on a call to show a demo live if you are interested (madhu@resolve.ai).
