# Overview

SIRE operates as a multi-layered intelligence network that transforms real-world sports data into on-chain execution. It combines computer vision, quantitative modelling, and AI-agent automation to detect inefficiencies and act on them programmatically.

#### The Technology

SIRE runs on three interlocking layers that transform live sport into deployable intelligence. 

While elements of this architecture are already powering αVault’s  strategies, several components — particularly the advanced vision and multi-source optimisation systems — are actively in development.

#### **1. Unique Data Layer**

**Score Vision (Bittensor Subnet 44)** is a primary contributor. It converts live video into tracking-grade coordinates and events, then packages **Vision-Language-Action (VLA)** and **Player Value Function (PVF)** insights as contributor “packets.”

These packets sit alongside proprietary datasets, live match feeds, market odds, and analyst notes to form a unified, structured stream.

#### **2. Quantitative Core**

The core combines and reweights multi-source signals in real time. For each target, it:

* Ingests contributor packets, including Score’s VLA and PVF outputs.
* Fuses them with the SIRE LLM, using multiple passes to estimate fair value and potential edge.
* Logs outcomes, scores contributor sources by live calibration and PnL tracking, and reweights on the fly.
* Sizes positions using fractional Kelly with explicit edge thresholds.\
  The LLM-first design searches large context windows to uncover independent signals for live, adaptive performance.

#### **3. Autonomous Agent**

Signals surface in the token-gated **aLink** terminal and execute through automated pools and **aVault**.

Every decision is recorded from odds to settlement, which keeps performance verifiable and enables continuous improvement.

#### Continuous data loop

The engine ingests and recalculates every second, incorporating:

* Real-time match events and visual streams
* Player and team performance metrics
* Historical patterns and contextual priors
* Market prices, liquidity gaps, and risk parameters

The result is a live stream of institutional-grade intelligence that bettors, partners, and automated vaults can use at scale.