The Bayence Platform
Systems & Architecture
The infrastructure layer that ingests real-world data streams, continuously trains and refines detection models, and delivers contextual outputs — without manual intervention. One platform, any structured data.
How the ML Factory Works
Most detection systems are built around static models — trained once, deployed, and left to drift. Bayence treats model training as a continuous operational process, not a one-time event.
Ingest
Real-world data streams ingested, scaled, and routed per model
Train
Models continuously updated as data distributions evolve
Infer
Ensemble outputs correlated in real-time against live data
Deliver
Scored, contextual outputs surfaced as feeds, events, or APIs
See It in Action
A full walkthrough of the Bayence platform — how the ML Factory operates, and how Certus and Sentrix are built on top of it.
What Bayence Powers
Purpose-built products for specific data domains, each applying the same ML Factory approach.
Certus
Malware Domain Prediction
Monitors Certificate Transparency logs in real time, enriching every new domain with DNS infrastructure signals and behavioral features to produce a predictive malware score — before threats reach your users.
- → Real-time scoring from CertStream data
- → Threshold-based feed for SOC and reputation pipelines
- → Historical data for enrichment and retrospective analysis
Sentrix
Network Anomaly Detection
Applies the Bayence ML Factory to network flow telemetry, continuously learning what normal looks like for your environment and surfacing the deviations that matter — without rules, without tuning, without noise.
- → Multi-model ensemble detection across flow feature space
- → Adapts to drift and seasonality without manual reconfiguration
- → Built for high-volume network environments at scale
Platform Differentiators
The mechanisms that make Bayence work — not just what it does, but how.
Ensemble Architecture
Why multiple models beat one
- → Each model architecture specializes in different data characteristics
- → No single point of failure — blind spots in one model are covered by others
- → Scales by adding specialized models, not rewriting rules
Continuous Training Loop
Why perpetual learning beats periodic retraining
- → Models update as data distributions evolve, not on a schedule
- → Captures both gradual drift and sudden distributional shifts
- → No manual retraining cycles or data science intervention required
Statistical Fusion
Why evidence-based scoring beats threshold rules
- → Ensemble outputs unified through a single statistical foundation
- → Separates novelty from noise using distributional context
- → Delivers both novelty detection and predictive signals from the same engine
Most ensembles are static — fixed weights, fixed assumptions. Bayence includes a meta-learning layer that observes how each model contributes and continuously rebalances the ensemble. The system doesn't just learn your data — it learns how it learns. Classical algorithms that normally demand expert tuning have their parameters derived directly from system output, not set by hand. This extends to seasonality — our models don't learn time directly. The structure of the system and the measurement approach handle seasonal patterns natively. The longer it runs, the sharper it gets. No manual thresholds, no stale configurations.
Build on the ML Factory
We're working with design partners to apply Bayence to new data domains. If you have structured data and a detection problem, let's talk.