> For the complete documentation index, see [llms.txt](https://agent-ted.gitbook.io/agent-ted/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://agent-ted.gitbook.io/agent-ted/the-model/tess-ai-model-architecture.md).

# TES's AI Model Architecture

### Core Components

#### Predictive Modeling

TED’s AI uses supervised machine learning to model sports markets at depth.

The system analyzes thousands of variables per game, including:

* team and matchup dynamics
* player-level performance signals
* historical outcomes
* betting market behavior

These models generate probabilistic forecasts used directly for trading decisions.

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#### Execution Engine

TED executes trades automatically through its betting engine.

* Positions are placed programmatically
* Capital allocation follows vault-specific risk parameters
* No manual intervention or discretionary input

Execution is governed by model confidence, volatility, and bankroll constraints.

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#### Post-Execution Feedback

Every executed trade feeds back into the system.

* Outcomes are evaluated post-settlement
* Model parameters and execution thresholds are adjusted
* Performance improves through continuous statistical learning

This creates a closed learning loop between prediction and execution.

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### Real-Time Sports Data Integration

TED ingests both real-time and historical data from institutional-grade sports data providers, including:

* player statistics and performance indicators
* injury and lineup updates
* weather and venue conditions
* live market odds across multiple sportsbooks

This data is processed by TED’s proprietary neural network to convert raw inputs into actionable pricing signals.

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### Risk & Bankroll Optimization

TED applies dynamic risk management at the system level.

The engine:

* Sizes positions based on volatility, correlation, and expected value
* Adjusts exposure based on liquidity and bankroll availability
* Prioritizes long-term capital growth over short-term variance

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