Bayence is an adaptive machine‑learning platform that automatically ingests any structured (tabular) data and continuously learns normal behavior without hand‑written rules. It uses a self‑learning ensemble of specialized models and an evidence‑based scoring layer to detect novel anomalies and provide forward‑looking predictions, delivering low‑false‑positive threat detection and proactive risk insights for network security, financial monitoring, IoT/OT, and infrastructure use cases.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional security systems rely on static rules and thresholds that quickly become outdated as network traffic patterns evolve, leading to missed novel threats and overwhelming analysts with false positives.
Solution
Bayence is an adaptive machine‑learning platform that continuously learns from any structured data—such as network flows, transaction logs, or IoT telemetry—without requiring hand‑written rules. It combines multiple specialized model architectures (reconstruction, generative, sequence‑aware) into a self‑learning ensemble and fuses their outputs with evidence‑based scoring to identify truly anomalous events and explain why they are flagged. The platform also provides forward‑looking predictions of likely future events, turning reactive detection into proactive defense. Deployment requires zero configuration; the system automatically ingests tabular data, adapts to changing patterns, and reduces alert fatigue by surfacing only the most significant signals. Bayence is offered as a cloud‑based service that can be applied across network security, financial transaction monitoring, IoT/OT sensor analysis, and infrastructure monitoring.
Target Audience
Primary customers are mid‑market enterprises, managed service providers (MSPs), MSSPs, and ISPs that need automated, low‑false‑positive threat detection and predictive analytics for their network and telemetry data.
Features
- Self‑learning ensembles of four specialized model types that capture different aspects of normal behavior
- Intelligent fusion layer that correlates model outputs and assigns evidence‑based anomaly scores with explanations
- Dual-mode operation: novelty detection for unseen threats and predictive modeling for future risk forecasting
- Zero‑configuration onboarding for any structured (tabular) data source, eliminating the need for manual rule creation
- Domain‑agnostic architecture supporting network security, financial services, IoT/OT, and infrastructure monitoring
- Continuous model updating that prevents staleness and adapts to evolving traffic patterns