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Flip AI Launches Observability Intelligence Platform

Flip AI launched with its observability intelligence platform, Flip, powered by a large language model (LLM) that predicts incidents and generates root cause analyses in seconds.

“When enterprise software doesn't perform as intended, it directly impacts customer experience and revenue. Current observability tools present an overwhelming amount of data on application performance. Developers and operators spend hours, sometimes days, poring through data and debugging incidents,” said Corey Harrison, co-founder and CEO of Flip AI. “Our LLM does this heavy lifting in seconds and immediately reduces mean time to detect and remediate critical incidents. Enterprises are calling Flip the ‘holy grail’ of observability.”

“We see in our research that observability, particularly incident resolution, is still in its early stages and remains a significant pain point for enterprises of all sizes. In fact, we see that 36% of respondents indicate they are planning to implement in the next 12-24 months,” said Paul Nashawaty, principal analyst at Enterprise Strategy Group. “Flip AI brings a refreshing and novel approach that is poised to transform observability and generative AI, as a whole.”

Flip automates incident resolution processes, reducing the effort to minutes for enterprise development teams. Flip’s core tenet is the notion of serving as an intelligence layer across all observability and infrastructure data sources and rationalizing through any modality of data, no matter where and how it is stored. Flip sits on top of traditional observability solutions like Datadog, Splunk and New Relic; open source solutions like Prometheus, OpenSearch and Elastic; and object stores like Amazon S3, Azure Blob Storage and GCP Cloud Storage. Flip’s LLM can work on structured and unstructured data; operates on-premises, multi-cloud and hybrid; requires little to no training; ensures that an enterprise’s data stays private; and has a minimal compute footprint.

“Software vendors of all types use generative AI to guide users and enrich products,” said Kevin Petrie, vice president of research at Eckerson Group. “Flip AI takes things a step further by using a language model to derive insights from multiple observability tools and explain their implications to users. This approach can simplify the work of ITOps engineers and speed their time to issue resolution.”

Flip AI also announced $6.5 million in seed funding led by Factory. Morgan Stanley Next Level Fund and GTM Capital also participated. The company plans to use the money to continue to advance its product roadmap and LLM and to expand its team and operations.

"Flip AI is a world-class team with deep AI and enterprise experience. They are industry veterans when it comes to building next level customer experiences for enterprises. Their large language model, the first in the world for DevOps, is a breakthrough in generative AI and sets a new standard in observability for years to come," said Andy Jacques, CEO and managing partner at Factory.

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Flip AI Launches Observability Intelligence Platform

Flip AI launched with its observability intelligence platform, Flip, powered by a large language model (LLM) that predicts incidents and generates root cause analyses in seconds.

“When enterprise software doesn't perform as intended, it directly impacts customer experience and revenue. Current observability tools present an overwhelming amount of data on application performance. Developers and operators spend hours, sometimes days, poring through data and debugging incidents,” said Corey Harrison, co-founder and CEO of Flip AI. “Our LLM does this heavy lifting in seconds and immediately reduces mean time to detect and remediate critical incidents. Enterprises are calling Flip the ‘holy grail’ of observability.”

“We see in our research that observability, particularly incident resolution, is still in its early stages and remains a significant pain point for enterprises of all sizes. In fact, we see that 36% of respondents indicate they are planning to implement in the next 12-24 months,” said Paul Nashawaty, principal analyst at Enterprise Strategy Group. “Flip AI brings a refreshing and novel approach that is poised to transform observability and generative AI, as a whole.”

Flip automates incident resolution processes, reducing the effort to minutes for enterprise development teams. Flip’s core tenet is the notion of serving as an intelligence layer across all observability and infrastructure data sources and rationalizing through any modality of data, no matter where and how it is stored. Flip sits on top of traditional observability solutions like Datadog, Splunk and New Relic; open source solutions like Prometheus, OpenSearch and Elastic; and object stores like Amazon S3, Azure Blob Storage and GCP Cloud Storage. Flip’s LLM can work on structured and unstructured data; operates on-premises, multi-cloud and hybrid; requires little to no training; ensures that an enterprise’s data stays private; and has a minimal compute footprint.

“Software vendors of all types use generative AI to guide users and enrich products,” said Kevin Petrie, vice president of research at Eckerson Group. “Flip AI takes things a step further by using a language model to derive insights from multiple observability tools and explain their implications to users. This approach can simplify the work of ITOps engineers and speed their time to issue resolution.”

Flip AI also announced $6.5 million in seed funding led by Factory. Morgan Stanley Next Level Fund and GTM Capital also participated. The company plans to use the money to continue to advance its product roadmap and LLM and to expand its team and operations.

"Flip AI is a world-class team with deep AI and enterprise experience. They are industry veterans when it comes to building next level customer experiences for enterprises. Their large language model, the first in the world for DevOps, is a breakthrough in generative AI and sets a new standard in observability for years to come," said Andy Jacques, CEO and managing partner at Factory.

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...