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Anodot Exits Stealth and Introduces Real-Time Anomaly Detection

Anodot exited stealth, introducing its real-time anomaly detection solution with patented machine learning algorithms for big data.

Pinpointing performance issues and business opportunities in real time, Anodot enables its customers to increase operational efficiency and maximize revenue generation.

The company also announced it closed a $3 million Series A funding round led by Disrupt-ive Partners, bringing total funding in the company to $4.5 million. The company will use the funding to accelerate its product roadmap and expand its sales activity, focusing on the ad tech, e-commerce, IoT and manufacturing industries in the U.S. and EMEA.

Founded in June 2014, Anodot is an analytics and anomaly detection solution that is data agnostic and automates the discovery of outliers in all business and operational data. Anodot’s platform isolates issues and correlates them across multiple parameters to surface and alert on incidents in real time.

“I experienced the data analysis lag problem first hand as CTO for Gett,” said Anodot CEO David Drai. “As a mobile taxi app, SMS text orders were dropped by the carrier, but it could take up to three days to spot critical issues and fix them, costing tens of thousands of dollars per incident. That’s where I got the idea for Anodot — to employ the latest advances in machine learning to detect performance problems automatically and in real time, eliminating the latency.”

Anodot is led by a proven team of three co-founders with strong credentials as entrepreneurs and technologists with deep experience in data science and global-scale SaaS infrastructures.

- CEO David Drai was co-founder and CTO of Cotendo for four years when it was acquired by Akamai for $300 million.

- Chief Data Scientist Dr. Ira Cohen held the same position at HP Software where he led research and development in machine learning and data mining techniques.

- R&D VP Shay Lang has led engineering teams for more than 10 years at leading technology companies.

On the board of directors, the team also includes Anthony Bettencourt, president and CEO at Imperva and a board member at Proofpoint, and Ben Lorica, O'Reilly Media’s chief data scientist and a top influencer on Twitter, as a board advisor.

Features and advantages of Anodot Anomaly Detection include:

- Operates in real time

- Works with any type of metric or KPI and scales to any big data volume

- Uses proprietary patented machine learning algorithms

- Correlates different metrics to help identify root causes of problems and eliminate alert storms

- Simulation capability optimizes alert planning and reduces false positive alerts

- Eliminates the need for time-intensive manual analysis

- Enables non-specialists to gain the insights they want and delivers fast time-to-value

- Provides clear visualizations that help any user to understand what the data is showing them

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Anodot Exits Stealth and Introduces Real-Time Anomaly Detection

Anodot exited stealth, introducing its real-time anomaly detection solution with patented machine learning algorithms for big data.

Pinpointing performance issues and business opportunities in real time, Anodot enables its customers to increase operational efficiency and maximize revenue generation.

The company also announced it closed a $3 million Series A funding round led by Disrupt-ive Partners, bringing total funding in the company to $4.5 million. The company will use the funding to accelerate its product roadmap and expand its sales activity, focusing on the ad tech, e-commerce, IoT and manufacturing industries in the U.S. and EMEA.

Founded in June 2014, Anodot is an analytics and anomaly detection solution that is data agnostic and automates the discovery of outliers in all business and operational data. Anodot’s platform isolates issues and correlates them across multiple parameters to surface and alert on incidents in real time.

“I experienced the data analysis lag problem first hand as CTO for Gett,” said Anodot CEO David Drai. “As a mobile taxi app, SMS text orders were dropped by the carrier, but it could take up to three days to spot critical issues and fix them, costing tens of thousands of dollars per incident. That’s where I got the idea for Anodot — to employ the latest advances in machine learning to detect performance problems automatically and in real time, eliminating the latency.”

Anodot is led by a proven team of three co-founders with strong credentials as entrepreneurs and technologists with deep experience in data science and global-scale SaaS infrastructures.

- CEO David Drai was co-founder and CTO of Cotendo for four years when it was acquired by Akamai for $300 million.

- Chief Data Scientist Dr. Ira Cohen held the same position at HP Software where he led research and development in machine learning and data mining techniques.

- R&D VP Shay Lang has led engineering teams for more than 10 years at leading technology companies.

On the board of directors, the team also includes Anthony Bettencourt, president and CEO at Imperva and a board member at Proofpoint, and Ben Lorica, O'Reilly Media’s chief data scientist and a top influencer on Twitter, as a board advisor.

Features and advantages of Anodot Anomaly Detection include:

- Operates in real time

- Works with any type of metric or KPI and scales to any big data volume

- Uses proprietary patented machine learning algorithms

- Correlates different metrics to help identify root causes of problems and eliminate alert storms

- Simulation capability optimizes alert planning and reduces false positive alerts

- Eliminates the need for time-intensive manual analysis

- Enables non-specialists to gain the insights they want and delivers fast time-to-value

- Provides clear visualizations that help any user to understand what the data is showing them

The Latest

In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability... 

While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...

Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

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