Preventing Outages and Anomalies Before They Impact Your Users
We believe every anomaly is a story waiting to be told. AnomalyWatch uses state-of-the-art AI models to detect deviations the moment they happen — giving engineering teams the time they need to respond before users are affected.
Founded by Ex-SRE Engineers
In 2024, our founders experienced a 14-hour production outage that went undetected for the first 3 hours. The anomaly was visible in the data — a subtle drift in memory allocation patterns — but no existing tool caught it. That incident cost $2.3M and affected 200,000 users.
They built AnomalyWatch to solve the problem they lived through: detecting the anomalies that traditional threshold-based monitoring misses. Using foundation models trained on millions of time series, AnomalyWatch understands what "normal" looks like and alerts when reality diverges.
The Team
Sarah Chen
CEO & Co-Founder
Ex-SRE Lead @ Google, 12 years in observability
Marcus Rivera
CTO & Co-Founder
Ex-Staff Eng @ Netflix, built real-time anomaly systems
Priya Sharma
Head of AI/ML
PhD Stanford, time series research, ex-Meta AI
James Okafor
Head of Engineering
Ex-Principal Eng @ Datadog, monitoring infrastructure
Elena Volkov
Head of Product
Ex-PM @ PagerDuty, incident response workflows
David Kim
Lead SRE
Ex-SRE @ AWS, distributed systems reliability
Technology
TimesFM
Google DeepMind foundation model for time series forecasting and anomaly scoring
Chronos
Amazon probabilistic time series model for uncertainty-aware predictions
Streaming Engine
Custom Rust-based ingestion pipeline processing 500K+ data points per second
Real-time Scoring
Sub-100ms anomaly detection using quantized models at the edge
Our Journey
Founded by Sarah Chen and Marcus Rivera after experiencing a 14-hour outage that cost $2.3M
Seed round closed, first prototype detecting anomalies on internal infrastructure
Public beta launch, 50 companies onboarded in first month
Series A funding, integrated TimesFM and Chronos models
500+ companies, 10B data points processed, <100ms detection latency achieved