Tetron.fun - Platform Overview

A high‑level view of Tetron's architecture and core components.

Platform Overview

This chapter provides a detailed look at the architecture, modules, and data flows that power TetronFun. You will learn how our AI engines, blockchain connectors, and analytics layers interact to deliver real‑time crypto payment intelligence.

1. High‑Level Architecture

┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│  User Apps  │ →  │  API Layer  │ →  │  AI Engine  │
└─────────────┘    └─────────────┘    └─────────────┘
                       │   ↑                │
                       │   │                │
                       ▼   │                ▼
                 ┌─────────────┐      ┌─────────────┐
                 │  Data Lake  │ ◀──  │ Blockchain  │
                 └─────────────┘      │ Connectors  │
                                      └─────────────┘

• User Apps Web, mobile, and backend clients that invoke TetronFun services.

• API Layer Exposes REST and WebSocket endpoints; handles authentication, rate‑limiting, and request routing.

• AI Engine Hosts machine learning models for fraud detection, reputation scoring, and payment routing.

• Data Lake Central repository for raw and processed data, including on‑chain events, historical transactions, and model outputs.

• Blockchain Connectors Adapters for Ethereum, Bitcoin, Solana, and other supported networks; stream blocks, parse events, and normalize data.

2. Core Modules

2.1 Fraud Detection

• Supervised learning models trained on transaction patterns and heuristics.

• Real‑time scoring to flag anomalous behavior before confirmation.

2.2 Wallet Reputation Scoring

• Aggregates on‑chain history, throughput, and counterparty profiles.

• Continuous scoring with decay factors and anomaly adjustments.

2.3 Smart Payment Routing

• Multivariate optimization to select the fastest, cheapest, and safest path.

• Factors in on‑chain fees, mempool congestion, and wallet trust levels.

2.4 Analytics Dashboard

• Visualizes KPIs, alert logs, and historical trends.

• Customizable widgets and exportable reports for compliance teams.

3. Data Flow

1. Ingestion

   • Blockchain Connectors subscribe to network events and push data to the Data Lake.

   • Webhooks and batch uploads from user applications feed additional context.

2. Processing

   • ETL jobs normalize and enrich raw events.

   • Feature extraction pipelines prepare inputs for AI models.

3. Inference

   • API Layer routes transaction requests to the AI Engine.

   • Models return scores and recommendations within milliseconds.

4. Persistence & Feedback

   • All inference logs and user actions are stored for retraining and audit.

   • Feedback loop refines model accuracy over time.

4. Security & Compliance

• Authentication & Authorization OAuth 2.0 and API keys with granular scopes.

• Data Encryption TLS in transit, AES‑256 at rest for all sensitive datasets.

• Audit Logging Immutable logs of API calls, model decisions, and administrative actions.

• Regulatory Compliance GDPR, AML/KYC support via configurable policy engines.

5. Scalability & Resilience

• Microservices Architecture Independently deployable services for elastic scaling.

• Containerization & Orchestration Docker and Kubernetes for automated deployments nd self‑healing.

• High Availability Multi‑region clusters, active‑active failover, and distributed data stores.

Next Steps

Proceed to Getting Started for hands‑on instructions on creating your first account, configuring API credentials, and testing a sample transaction through TetronFun.