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
As enterprise networks get more complex, encompassing on-prem, cloud and hybrid systems and applications, network automation is no longer optional. It's critical for uptime, security and scale. Yet persistent misconceptions about increasingly capable network automation platforms among the very NetOps professionals who would benefit the most from using them are holding back adoption. Here are 5 of the most common of those misconceptions, and why NetOps teams might want to re-think them ...
While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalize AI at scale, according to The Future of IT Operations in the AI Era, a report from Riverbed ...
Many organizations rely on cloud-first architectures to aggregate, analyze, and act on their operational data ... However, not all environments are conducive to cloud-first architectures ... There are limitations to cloud-first architectures that render them ineffective in mission-critical situations where responsiveness, cost control, and data sovereignty are non-negotiable; these limitations include ...
For years, cybersecurity was built around a simple assumption: protect the physical network and trust everything inside it. That model made sense when employees worked in offices, applications lived in data centers, and devices rarely left the building. Today's reality is fluid: people work from everywhere, applications run across multiple clouds, and AI-driven agents are beginning to act on behalf of users. But while the old perimeter dissolved, a new one quietly emerged ...
For years, infrastructure teams have treated compute as a relatively stable input. Capacity was provisioned, costs were forecasted, and performance expectations were set based on the assumption that identical resources behaved identically. That mental model is starting to break down. AI infrastructure is no longer behaving like static cloud capacity. It is increasingly behaving like a market ...
Resilience can no longer be defined by how quickly an organization recovers from an incident or disruption. The effectiveness of any resilience strategy is dependent on its ability to anticipate change, operate under continuous stress, and adapt confidently amid uncertainty ...
Mobile users are less tolerant of app instability than ever before. According to a new report from Luciq, No Margin for Error: What Mobile Users Expect and What Mobile Leaders Must Deliver in 2026, even minor performance issues now result in immediate abandonment, lost purchases, and long-term brand impact ...
Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...
Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...
AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...