AI Infrastructure

At the heart of Aelio lies a modular AI infrastructure, built to process massive volumes of decentralized financial data, detect subtle patterns, and execute dynamic portfolio changes with surgical precision.

Instead of relying on a single AI agent or a fixed strategy, Aelio functions as a composite engine, made up of interconnected models—each specialized for a different stage of the investment lifecycle.

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↝ Algorithmic Stack

Aelio uses a multi-layered AI architecture that blends classic statistical techniques with state-of-the-art machine learning.

Here’s how the stack is structured:

• Time Series Forecasting: Models like LSTM and Transformers forecast price movements across various timeframes. These are continuously retrained on historical data, volatility patterns, and on-chain signals.

• Reinforcement Learning (RL): A dedicated RL agent manages capital allocation. Trained in simulated environments reflecting real market conditions, it learns to maximize long-term returns while limiting drawdowns.

• Natural Language Processing (NLP): Fine-tuned transformer models (e.g., BERT, RoBERTa) digest sentiment from Twitter, Reddit, and Discord, detecting narrative shifts and assigning sentiment-weighted confidence scores.

• Bayesian Risk Modeling: Helps forecast tail-risk events and generate confidence intervals around performance. This informs how much exposure Aelio takes during high-uncertainty regimes.

• Market Regime Classification: Through unsupervised learning (e.g., k-means, DBSCAN), Aelio identifies whether markets are bullish, bearish, or sideways, adjusting strategy settings accordingly.

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↝ Data Sources

To ensure signal accuracy, Aelio taps into a wide range of on-chain and off-chain data feeds:

• Historical Price Data: Pulled from decentralized oracles and DEX aggregators • On-Chain Metrics: TVL, wallet activity, and token velocity from LetsBonk-native contracts • Sentiment Signals: Sourced from social listening tools monitoring Twitter, Discord, Reddit, and Medium • Market Intelligence: Aggregated from Glassnode, CoinGecko, and custom alpha feeds • Liquidity Tracking: Real-time inputs from AMMs and liquidity pools

All this data is normalized, timestamped, and stored in a feature-rich time-series database, making it ready for AI processing.

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↝ Decision Pipeline

Aelio’s decision-making engine follows a clear, step-by-step flow:

1. Signal Aggregation Raw inputs are cleaned, enhanced, and enriched with derived metrics like momentum, skew, or funding anomalies.

2. Scoring Engine Each asset is scored on: – Expected returns – Volatility forecast – Liquidity depth – Sentiment strength These scores are weighted based on the current market regime.

3. Allocation Engine The RL allocator determines how to distribute capital across assets, while respecting constraints (e.g., exposure limits, correlation caps, risk controls).

4. Execution Layer Final allocations are converted into on-chain transaction bundles via smart contracts, ensuring full transparency and auditability.

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↝ Training, Updates & Model Governance

Aelio’s AI models are constantly evolving. They’re trained on a rolling data window, and automatically retriggered when:

• Model drift is detected • Volatility spikes occur • Data formats change

Key features of the training process:

• RL agents retrained in multi-agent simulations • NLP models updated weekly to stay in sync with shifting language in the community • Hyperparameter tuning via Bayesian optimization • Ensemble validation, where each model is tested against control scenarios before deployment

All models are monitored by a meta-governance layer, which flags abnormal predictions, execution issues, or unexpected interactions—without ever introducing centralized decision-making.