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Loom Systems Launches AI Platform

Loom Systems announced an AI-powered operational analytics platform used for real-time detection and resolution for any type of application.

Targeted at DevOps and IT professionals, Loom instantly analyzes logs and semi-structured machine data for immediate visibility into a company’s digital environment. Accelerating the detection and resolution of IT problems in real-time, Loom helps reduce the cost and complexity of working with operational analytics. Via on-premises or SaaS installation, Loom Systems easily generates insights from raw data and with zero configuration or maintenance of the IT stack, including homegrown applications.

“Many organizations implement log analysis solutions using manual techniques, however very few organizations derive the data’s full value from those efforts,” said John L Myers, managing research director, Enterprise Management Associates – a Boulder, CO based industry analysis firm. “Today’s transformation towards digital economy based on mobile and online apps requires an automated process that can extract insights out of the logs from complex IT environments. Loom Systems empowers organizations to make this leap by automatically loading, preparing and presenting application log data. This allows for real-time analytics as well as providing an intelligence layer that ties log data to corrective action.”

Loom Systems takes digitized information in structured, semi-structured or uncommonly structured text format and structures it automatically. By mathematically modeling how humans analyze such structures, Loom Systems fuses analytical skills with computational speed to simulate and enhance the entire data analysis cycle. The solution considers each metric and tracks it to learn its unique baseline and behavioral pattern over time to detect anomalies and predict future trends.

Loom uses complex modules to determine whether a signal has shifted, as well as the type of shift that has occurred. The signal types are distinguished and anomaly detection algorithms are tailored to fit them. Signals are then automatically tracked in ways that complement their expected behavior.

Additional features and benefits of the Loom Systems platform include:

- Zero pre-processing required

- Detection of hidden and emerging issues in data and correlation of problems between all applications and services

- All data dynamically aggregated and correlated in real-time

- Automatic Root-cause Analysis

- Enrichment of alerts with insights and recommended resolutions from its Tribal Knowledge Bank, that contains a wide set of built-in recommended resolutions

“At Loom Systems, we’ve built a platform to understand, reason and learn about constantly evolving digital environments and operational complexity,” said Gabby Menachem, Founder and CEO, Loom Systems. “We build cognitive intelligence and expertise into a new set of tools that analyze logs, metrics and machine-generated data – just like DevOps application managers and IT professionals do every day – but with unprecedented speed and scale.”

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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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 ...

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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 ...

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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.

Loom Systems Launches AI Platform

Loom Systems announced an AI-powered operational analytics platform used for real-time detection and resolution for any type of application.

Targeted at DevOps and IT professionals, Loom instantly analyzes logs and semi-structured machine data for immediate visibility into a company’s digital environment. Accelerating the detection and resolution of IT problems in real-time, Loom helps reduce the cost and complexity of working with operational analytics. Via on-premises or SaaS installation, Loom Systems easily generates insights from raw data and with zero configuration or maintenance of the IT stack, including homegrown applications.

“Many organizations implement log analysis solutions using manual techniques, however very few organizations derive the data’s full value from those efforts,” said John L Myers, managing research director, Enterprise Management Associates – a Boulder, CO based industry analysis firm. “Today’s transformation towards digital economy based on mobile and online apps requires an automated process that can extract insights out of the logs from complex IT environments. Loom Systems empowers organizations to make this leap by automatically loading, preparing and presenting application log data. This allows for real-time analytics as well as providing an intelligence layer that ties log data to corrective action.”

Loom Systems takes digitized information in structured, semi-structured or uncommonly structured text format and structures it automatically. By mathematically modeling how humans analyze such structures, Loom Systems fuses analytical skills with computational speed to simulate and enhance the entire data analysis cycle. The solution considers each metric and tracks it to learn its unique baseline and behavioral pattern over time to detect anomalies and predict future trends.

Loom uses complex modules to determine whether a signal has shifted, as well as the type of shift that has occurred. The signal types are distinguished and anomaly detection algorithms are tailored to fit them. Signals are then automatically tracked in ways that complement their expected behavior.

Additional features and benefits of the Loom Systems platform include:

- Zero pre-processing required

- Detection of hidden and emerging issues in data and correlation of problems between all applications and services

- All data dynamically aggregated and correlated in real-time

- Automatic Root-cause Analysis

- Enrichment of alerts with insights and recommended resolutions from its Tribal Knowledge Bank, that contains a wide set of built-in recommended resolutions

“At Loom Systems, we’ve built a platform to understand, reason and learn about constantly evolving digital environments and operational complexity,” said Gabby Menachem, Founder and CEO, Loom Systems. “We build cognitive intelligence and expertise into a new set of tools that analyze logs, metrics and machine-generated data – just like DevOps application managers and IT professionals do every day – but with unprecedented speed and scale.”

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.