Skip to main content

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

In APMdigest's 2026 Observability Predictions Series, industry experts offer predictions on how Observability and related technologies will evolve and impact business in 2025. Part 5 covers APM and infrastructure monitoring ...

AI continues to be the top story across the industry, but a big test is coming up as retailers make the final preparations before the holiday season starts. Will new AI powered features help load up Santa's sleigh this year? Or are early adopters in for unpleasant surprises in the form of unexpected high costs, poor performance, or even service outages? ...

In APMdigest's 2026 Observability Predictions Series, industry experts offer predictions on how Observability and related technologies will evolve and impact business in 2025. Part 4 covers user experience, digital performance, website performance and ITSM ...

In APMdigest's 2026 Observability Predictions Series, industry experts offer predictions on how Observability and related technologies will evolve and impact business in 2025. Part 3 covers more predictions about Observability ...

In APMdigest's 2026 Observability Predictions Series, industry experts offer predictions on how Observability and related technologies will evolve and impact business in 2025. Part 2 covers predictions about Observability and AIOps ...

The Holiday Season means it is time for APMdigest's annual list of predictions, covering Observability and other IT performance topics. Industry experts — from analysts and consultants to the top vendors — offer thoughtful, insightful, and often controversial predictions on how Observability, AIOps, APM and related technologies will evolve and impact business in 2026 ...

IT organizations are preparing for 2026 with increased expectations around modernization, cloud maturity, and data readiness. At the same time, many teams continue to operate with limited staffing and are trying to maintain complex environments with small internal groups. These conditions are creating a distinct set of priorities for the year ahead. The DataStrike 2026 Data Infrastructure Survey Report, based on responses from nearly 280 IT leaders across industries, points to five trends that are shaping data infrastructure planning for 2026 ...

Developers building AI applications are not just looking for fault patterns after deployment; they must detect issues quickly during development and have the ability to prevent issues after going live. Unfortunately, traditional observability tools can no longer meet the needs of AI-driven enterprise application development. AI-powered detection and auto-remediation tools designed to keep pace with rapid development are now emerging to proactively manage performance and prevent downtime ...

Every few years, the cybersecurity industry adopts a new buzzword. "Zero Trust" has endured longer than most — and for good reason. Its promise is simple: trust nothing by default, verify everything continuously. Yet many organizations still hesitate to implement Zero Trust Network Access (ZTNA). The problem isn't that ZTNA doesn't work. It's that it's often misunderstood ...

For many retail brands, peak season is the annual stress test of their digital infrastructure. It's also when often technical dashboards glow green, yet customer feedback, digital experience frustration, and conversion trends tell a different story entirely. Over the past several years, we've seen the same pattern across retail, financial services, travel, and media: internal application performance metrics fail to capture the true experience of users connecting over local broadband, mobile carriers, and congested networks using multiple devices across geographies ...

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

In APMdigest's 2026 Observability Predictions Series, industry experts offer predictions on how Observability and related technologies will evolve and impact business in 2025. Part 5 covers APM and infrastructure monitoring ...

AI continues to be the top story across the industry, but a big test is coming up as retailers make the final preparations before the holiday season starts. Will new AI powered features help load up Santa's sleigh this year? Or are early adopters in for unpleasant surprises in the form of unexpected high costs, poor performance, or even service outages? ...

In APMdigest's 2026 Observability Predictions Series, industry experts offer predictions on how Observability and related technologies will evolve and impact business in 2025. Part 4 covers user experience, digital performance, website performance and ITSM ...

In APMdigest's 2026 Observability Predictions Series, industry experts offer predictions on how Observability and related technologies will evolve and impact business in 2025. Part 3 covers more predictions about Observability ...

In APMdigest's 2026 Observability Predictions Series, industry experts offer predictions on how Observability and related technologies will evolve and impact business in 2025. Part 2 covers predictions about Observability and AIOps ...

The Holiday Season means it is time for APMdigest's annual list of predictions, covering Observability and other IT performance topics. Industry experts — from analysts and consultants to the top vendors — offer thoughtful, insightful, and often controversial predictions on how Observability, AIOps, APM and related technologies will evolve and impact business in 2026 ...

IT organizations are preparing for 2026 with increased expectations around modernization, cloud maturity, and data readiness. At the same time, many teams continue to operate with limited staffing and are trying to maintain complex environments with small internal groups. These conditions are creating a distinct set of priorities for the year ahead. The DataStrike 2026 Data Infrastructure Survey Report, based on responses from nearly 280 IT leaders across industries, points to five trends that are shaping data infrastructure planning for 2026 ...

Developers building AI applications are not just looking for fault patterns after deployment; they must detect issues quickly during development and have the ability to prevent issues after going live. Unfortunately, traditional observability tools can no longer meet the needs of AI-driven enterprise application development. AI-powered detection and auto-remediation tools designed to keep pace with rapid development are now emerging to proactively manage performance and prevent downtime ...

Every few years, the cybersecurity industry adopts a new buzzword. "Zero Trust" has endured longer than most — and for good reason. Its promise is simple: trust nothing by default, verify everything continuously. Yet many organizations still hesitate to implement Zero Trust Network Access (ZTNA). The problem isn't that ZTNA doesn't work. It's that it's often misunderstood ...

For many retail brands, peak season is the annual stress test of their digital infrastructure. It's also when often technical dashboards glow green, yet customer feedback, digital experience frustration, and conversion trends tell a different story entirely. Over the past several years, we've seen the same pattern across retail, financial services, travel, and media: internal application performance metrics fail to capture the true experience of users connecting over local broadband, mobile carriers, and congested networks using multiple devices across geographies ...