Welcome to Flip AI, The GenAI Native Observability Company. While many of us developers have been conditioned to look at logs, metrics and traces simply as telemetry data, at Flip AI, we think about observability data differently: as a unique, semantic language. Observability is the language that your systems and applications use to communicate, but all too often, engineers aren’t able to fully understand what these systems are saying. Despite the advances in big data and analytics that allow ever increasing volumes of data to be queried and visualized with ease, distinguishing the signal from the noise in observability data is still fraught with challenges. By setting your alert thresholds too aggressively, you risk inundating your engineering team with false positives. Conversely, if you opt for overly conservative alert settings, your engineers might overlook crucial issues. As software systems become more complex and distributed, the dashboards and search-and-retrieval interfaces used to monitor their health become more complex and more cluttered. Even the most experienced engineers periodically ask themselves, “What exactly am I looking at?” when viewing these. As businesses of all sizes increasingly exist in the digital world, the cost of a critical system going down or having other performance issues has never been higher. As a result, stress and burnout are all too common on the engineering teams that build and support these critical systems.

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Now imagine a world in which your engineers have access to a body of intelligence that can recall - in seconds! - vast quantities of knowledge about all areas of every tech stack in use today. Even as recently as a few years ago, such a proposition would have been a thing of science fiction. Our customers said this “holy grail of observability” was impossible. But, that’s no longer the case. The power of generative AI models, such as large language models (or LLMs), is that they can capture almost all the knowledge that exists in digital form for a given domain. For Information Technology, this means that an LLM can learn from the expansive universe of technical information that exists on the internet today. Once this knowledge has been learned by the model, it can be recalled and used instantaneously. The utility of this to an engineer troubleshooting a problem is incredible. Instead of scouring for relevant information on the internet and in both internal and external knowledge bases, an engineer can leverage the power of an LLM to provide this type of analysis and insights. 

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Of course, in order for an LLM to obtain this knowledge, it must first be trained on a massive corpus of domain-specific data. To say this is challenging would be an understatement! The good news is that we at Flip AI are experts in AI and DevOps. We’ve trained an LLM designed for Observability, and it’s an expert at interpreting the four modalities of observability data - Metrics, Events, Logs, and Traces. We’ve built an intuitive application that allows engineers to debug issues with our LLM using the observability data they already have. Our observability intelligence platform, Flip, integrates seamlessly with all the major observability platforms as well as custom homegrown tooling. Flip can work with any system that can store and query data. Our LLM can run anywhere - in our cloud account, your cloud account, or even on premise - meaning your data never needs to leave your boundaries. 

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Over the next few weeks and months, we’ll be making a whole bunch of announcements. Our leadership team are all seasoned veterans in technology with deep expertise in big data, cloud computing, machine learning, and artificial intelligence. We’re innovating at breakneck speed, and will be sharing more details about our journey on this blog. Stay tuned.