Overview

Explore SIRE's revolutionary approach to sports betting intelligence

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.