Anodot has been nominated for the 2026 World AI Awards in the Anomaly Detection category, recognising the company’s work in using machine learning to identify unusual behaviour across large volumes of business and operational data.
It is an AI problem that rarely gets the attention of chatbots or image generators, but the consequences are very real. A sudden fall in payment approvals, an unexpected spike in cloud spending, a broken product feature or deteriorating network performance can quietly cost a business money long before someone notices a dashboard moving in the wrong direction.
Anodot is built around catching those changes automatically.
The World AI Awards recognises organisations, individuals, products and technologies contributing to the development and practical application of artificial intelligence across industries.
Anomaly detection without chasing static thresholds
Traditional monitoring often depends on predefined thresholds. An engineer might create an alert when transactions fall below a particular level or server latency rises above another.
Real businesses are rarely that predictable.
Traffic changes during weekends. Payments rise around shopping events. Network behaviour varies by location. A metric that looks unusual at 3 a.m. may be completely normal at midday.
Anodot’s monitoring technology instead uses machine learning to establish dynamic baselines for individual metrics and learn how their normal behaviour changes over time. The company’s Deep 360 Monitoring technology can select from modelling approaches including modified Holt-Winters and ARIMA as it learns patterns and seasonality in incoming time-series data.
When behaviour deviates from those learned patterns, the system identifies and scores the anomaly rather than waiting for a manually configured threshold to be crossed.
That matters when the number of metrics becomes impossible for people to watch individually.
Independent material published by Intel describes Anodot’s anomaly detection and forecasting applications as ensemble machine-learning systems analysing hundreds of millions of time-series metrics every minute.
Finding an anomaly is only half the problem
Spotting something strange does not automatically tell an operations team what went wrong.
Anodot tackles that second problem through correlation. Its technology looks for relationships between abnormal metrics, events and contributing factors, grouping related anomalies into incidents rather than sending a separate alert for every unusual data point.
The idea is simple: fewer alerts, more context.
Anodot says its incident-detection technology can reduce time to detection by up to 80% and reports a 95% “good catch” rate for its scored alerts. These are company-reported performance claims rather than guarantees that every deployment will achieve the same results.
Alerts can then feed into tools already used by operational teams, including Slack, PagerDuty and webhooks.
From payment problems to network outages
The technology is not limited to conventional IT monitoring.
Anodot positions its anomaly detection across payment transactions, digital experiences, applications, cloud costs, advertising campaigns and telecommunications networks. Its fintech offering, for example, can monitor payment approvals, merchant activity, deposits, withdrawals, login attempts and partner APIs for behaviour that falls outside expected patterns.
There are named deployments behind those use cases.
Razorpay uses Anodot for automated monitoring and real-time anomaly detection, according to Anodot’s customer material. The company also lists customer cases involving organisations such as LivePerson and telecommunications operators using the platform to identify operational incidents.
One telecommunications customer reported that Anodot allowed its team to identify some incidents one or two hours before they affected customer experience. That result comes from the customer account published by Anodot and should not be treated as a universal performance benchmark.
The practical value is less about finding spectacular anomalies than noticing small deviations early enough for somebody to act.
Why anomaly detection matters as businesses collect more data
Modern companies have no shortage of metrics. The harder problem is deciding which changes deserve attention.
Anodot’s approach uses machine learning to continuously learn normal behaviour, detect deviations, correlate related signals and rank incidents according to significance. Its technology also incorporates user feedback on alerts to help improve subsequent detection.
That makes anomaly detection an unusually practical application of AI. The objective is not to generate content or imitate human conversation. It is to notice that something in a complicated system no longer behaves the way it normally does — preferably before customers notice too.
Graham Cooke, President of the World AI Awards, said:
“Anodot’s nomination highlights an important application of artificial intelligence that operates largely behind the scenes. Businesses generate enormous volumes of operational data, and identifying the few changes that genuinely require attention is becoming increasingly difficult to do manually.
“Anodot’s work in machine-learning-based anomaly detection demonstrates how AI can help organisations identify unusual behaviour, connect related signals and give operational teams earlier context around emerging incidents. We congratulate Anodot on its 2026 World AI Awards nomination in the Anomaly Detection category and look forward to following the continued development of its technology.”
Anodot joins organisations, researchers, entrepreneurs and technology developers being recognised through the 2026 World AI Awards.
The programme recognises organisations, individuals and technologies contributing to the development and application of artificial intelligence across industries. Anomaly detection represents a particularly established part of that landscape, applying machine learning to a problem businesses encounter every day: understanding when changing data signals a genuine problem rather than ordinary variation.
For Anodot, that means turning enormous streams of metrics into a much smaller set of incidents that people can investigate and act on.
Learn more about Anodot and its AI-powered anomaly detection technology at anodot.com.
Discover the World AI Awards 2026, explore the nominees and learn more about the awards at worldawards.ai.
