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
Artificial intelligence (AI) is core to observability practices, with some 41% of respondents reporting AI adoption as a core driver of observability, according to the State of Observability for Financial Services and Insurance report from New Relic ...
Application performance monitoring (APM) is a game of catching up — building dashboards, setting thresholds, tuning alerts, and manually correlating metrics to root causes. In the early days, this straightforward model worked as applications were simpler, stacks more predictable, and telemetry was manageable. Today, the landscape has shifted, and more assertive tools are needed ...
Cloud adoption has accelerated, but backup strategies haven't always kept pace. Many organizations continue to rely on backup strategies that were either lifted directly from on-prem environments or use cloud-native tools in limited, DR-focused ways ... Eon uncovered a handful of critical gaps regarding how organizations approach cloud backup. To capture these prevailing winds, we gathered insights from 150+ IT and cloud leaders at the recent Google Cloud Next conference, which we've compiled into the 2025 State of Cloud Data Backup ...
Private clouds are no longer playing catch-up, and public clouds are no longer the default as organizations recalibrate their cloud strategies, according to the Private Cloud Outlook 2025 report from Broadcom. More than half (53%) of survey respondents say private cloud is their top priority for deploying new workloads over the next three years, while 69% are considering workload repatriation from public to private cloud, with one-third having already done so ...
As organizations chase productivity gains from generative AI, teams are overwhelmingly focused on improving delivery speed (45%) over enhancing software quality (13%), according to the Quality Transformation Report from Tricentis ...
Back in March of this year ... MongoDB's stock price took a serious tumble ... In my opinion, it reflects a deeper structural issue in enterprise software economics altogether — vendor lock-in ...
In MEAN TIME TO INSIGHT Episode 15, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses Do-It-Yourself Network Automation ...
Zero-day vulnerabilities — security flaws that are exploited before developers even know they exist — pose one of the greatest risks to modern organizations. Recently, such vulnerabilities have been discovered in well-known VPN systems like Ivanti and Fortinet, highlighting just how outdated these legacy technologies have become in defending against fast-evolving cyber threats ... To protect digital assets and remote workers in today's environment, companies need more than patchwork solutions. They need architecture that is secure by design ...
Traditional observability requires users to leap across different platforms or tools for metrics, logs, or traces and related issues manually, which is very time-consuming, so as to reasonably ascertain the root cause. Observability 2.0 fixes this by unifying all telemetry data, logs, metrics, and traces into a single, context-rich pipeline that flows into one smart platform. But this is far from just having a bunch of additional data; this data is actionable, predictive, and tied to revenue realization ...
64% of enterprise networking teams use internally developed software or scripts for network automation, but 61% of those teams spend six or more hours per week debugging and maintaining them, according to From Scripts to Platforms: Why Homegrown Tools Dominate Network Automation and How Vendors Can Help, my latest EMA report ...