In an increasingly connected world, unexpected network outages lead to economic losses, security vulnerabilities, and disrupted essential services (healthcare, emergency response, financial transactions). UptimeAI provides a predictive AI-powered solution that foresees network outages before they happen, enabling proactive mitigation. Using real-time data from the IODA (Internet Outage Detection & Analysis) system, we analyze global network patterns via BGP, active probing, and network telescope data. Machine learning models (Variational Autoencoders) detect anomalies in network activity across thousands of cities to predict upcoming potential disruptions. Who benefits from UptimeAI? - Internet Service Providers (ISPs): Reduce customer churn & infrastructure failure costs - Telecoms & Enterprises: Prevent costly downtime & optimize business continuity - Governments & Emergency Services: Ensure reliable communication during disasters - Cloud & Cybersecurity Firms: Improve threat detection and resiliency planning Key Features & Benefits: - Real-Time AI-Based Outage Forecasting: Predict outages before they happen - Multi-Source Data Analysis: Combines BGP, active probing, and network telescope data (future iterations will include additional data sources like weather, user-reported outages, and infrastructure outage reports) - Interactive Dashboard & API: Seamless integration into ISP & enterprise systems - Geo-Based Risk Assessment: Incorporates data from the geographic vicinity - Scalable & Cost-Effective: Potential to develop custom plans for ISPs, telecoms, enterprises, and local governments With UptimeAI, we transform network monitoring from reactive to proactive, saving businesses & governments millions in lost revenue and operational failures while improving digital infrastructure reliability worldwide.
Category tags:"Great presentation, clear business value. What would be interesting to know is how you track the mitigation actities your customers would take based on your data. That could help you to create case studies to showcase how much a custoemr could save to use your platform."
Jenny Tillgren
Venture Scout and Advisor
"Very clear presentation of the problem, solution and your planned next steps. Would be great to see some metrics on the performance of the VAE to make predictions on unusual behaviour."
Thomas Blake
CTO
"Interesting AI approach to outage detection, demo is too abstract and little representative though"
Ivan Dotu
"Demo contains specs beyond network outage predictions, which make it hard to focus on the main value which is the ML itself running the prediction. A good methodological rooting to a relevant problem. "
Maria Antonia Bravo