The Intelligence Layer
EOSYN Core™
Applies proprietary GeoAI models to detect change, predict risk, and generate confidence-scored outputs.
Overview
What It Does
EOSYN Core is the cognitive engine of the platform. It applies proprietary GeoAI models to the harmonized data cubes produced by Fabric, generating instrumental outputs — not visualizations. The distinction is critical: a map shows what is; an instrument measures what is. Core's outputs are measurements: risk scores with confidence intervals, change detection with significance levels, anomaly alerts with severity classifications.
Every Core output carries a confidence score — a statistical measure of how certain the model is about its prediction. This is what makes EOSYN outputs defensible in institutional contexts: an insurer can justify a pricing decision, an infrastructure operator can prioritize maintenance, a public institution can allocate resources — all backed by quantified confidence rather than subjective interpretation. Core also includes a model versioning system: every output references the specific model version that produced it, enabling reproducibility and auditability.
Core supports custom model development for clients with specialized needs. A client may require a model trained on their historical claims data, or a model tuned for a specific geographic region or asset type. Custom models go through the same rigor as standard models: bias testing, performance evaluation, and model card publication before deployment.
Capabilities
Key Capabilities
Change Detection
Identify and classify physical changes over time
Risk Prediction
Forward-looking risk scores with confidence intervals
Anomaly Detection
Identify unusual patterns requiring attention
Forecast Layers
Predictive modeling for future scenarios
Confidence Scoring
Statistical certainty on every output
Model Versioning
Reproducible, auditable model references
Custom Model Development
Bespoke models for specialized use cases
Deliverables
Example Deliverables
| Deliverable | Description |
|---|---|
| Risk Scores | Quantified risk assessments with confidence intervals |
| Anomaly Alerts | Real-time notifications for detected anomalies |
| Forecast Layers | Predictive outputs for future scenario planning |
| Detection Models | Trained models for specific change types |
| Model Performance Reports | Accuracy, precision, recall, and bias metrics |
The Stack
How It Connects
Data flows upward through the stack: Orbit sources raw signals, Fabric harmonises them into measurement-grade data cubes, Core generates confidence-scored intelligence, and Atlas delivers decisions into client systems.