Architecture

Understand the technical infrastructure behind SIRE

SIRE’s architecture runs as a sequence of interlocking processes, each one refining, weighting, and executing intelligence in real time.

From intake to optimisation, every layer builds on the last, creating a continuous adaptive loop.

System Flow

1. <INIT> Information Intake

Multi-source contributor packets enter the system, including Score Vision (Subnet 44) data, proprietary datasets, live match feeds, and market odds. Each packet contains unique predictive signals that seed the forecasting engine.

2. Model Processing

The SIRE LLM processes all packets, interpreting statistical and visual relationships, generating structured representations of match context, and aligning them with model priors.

3. Multiple Prediction Runs

The engine performs ensemble inference: multiple independent predictions across different packet combinations, to estimate fair value, edge, and volatility-adjusted confidence intervals.

4. Outcome Tracking

Every prediction is recorded against real-world outcomes.

Market prices, settlement data, and live match results are logged to benchmark and recalibrate performance continuously.

5. Performance Scoring

Each contributor packet is evaluated using statistical metrics such as calibration, Sharpe ratio, and information value.

Contributors are ranked based on predictive quality and long-term reliability.

6. Dynamic Weighting

High-performing contributors are up-weighted; underperforming or correlated ones are reduced or replaced.

Sizing logic applies fractional Kelly scaling with adaptive exposure limits to maintain consistent risk profiles.

7. Iterative Optimisation <SUCCESS>

The engine retrains on new data, updates weights, and redeploys refined models automatically.

Sharper, verified signals flow to aLink for visibility and aVault for autonomous execution.

Why It Works

• Unified pipeline connecting data ingestion, modelling, and execution.

• Continuous performance feedback ensures adaptive learning.

• Modular structure allows new models, datasets, or signals to integrate without redesign.

• Verifiable on-chain transparency at every step.

Read more in The Multi-Source Prediction Engine section.