AI/ML Infrastructure

At the core of Sentra lies a multi-model, multi-modal AI stack designed to understand, interpret, and act on the crypto markets in real time. We go beyond surface-level analysis — leveraging language models, custom ML pipelines, and anomaly detection systems to surface insight that charts and scanners miss.

Model Backbone

Sentra Protocol uses an ensemble of best-in-class foundation models — each selected and tuned for specific tasks within the ecosystem:

• GPT-4 (OpenAI): Contextual interpretation, sentiment explanation, AI-powered summaries, and agent-level reasoning

• Claude (Anthropic): Risk assessment logic, compliance-aware filtering, agent inference safety

• DeepSeek: Precision transaction mapping and cross-wallet heuristics for wallet clustering

• LLaMA (Meta): Lightweight, efficient on-device inference for deployed bots and agents

This layered model approach ensures response diversity, redundancy, and domain adaptation across fundamental, behavioral, and sentiment dimensions.

Custom ML Pipelines

Our proprietary ML engines are trained on millions of historic token, wallet, and sentiment datapoints to extract patterns, build predictive logic, and score risk with nuance.

Key Pipelines:

• Anomaly Detection Engine Identifies wallet behavior outliers, flagging potential insider dumps, stealth accumulation, or inorganic activity

• Narrative Velocity Tracker Measures acceleration of token mentions across influencers and sentiment channels to detect early-stage narrative formation → Powering /sentiment and /kol modules

• Fluctuation Index (FI) Our custom volatility metric trained on token microstructure and sell pressure behavior → Used in Fundamental Analysis for early risk warning

• Multi-Factor Alpha Signal Combines social, wallet, and contract signals into a confidence-ranked call (alpha trigger) → Core engine for Auto-Trading and Agent Builder

Continuous Learning & Fine-Tuning

• Weekly retraining of sentiment and wallet models on fresh on-chain and X (Twitter) data

• Reinforcement learning for agent agents based on user feedback loops and alert accuracy

• Ensemble voting and fallback logic to resolve conflicting model signals

Modular AI for Custom Agents

Every user-created agent taps into Sentra’s inference layer via lightweight, containerized calls:

• Model selection: GPT/Claude/LLM hybrid

• Data source routing: VaultStream™, Telegram, On-chain

• Action generation: Alert, notify, or execute

Sentra’s AI doesn’t just summarize the market — it thinks like a trader, reasons like an analyst, and evolves like an agent.