Technology
Multi-Layered AI Architecture
A sophisticated intelligence system featuring a proprietary Knowledge Graph, a Fact-Checking Engine, and a self-improving Selection Algorithm.
The Miko AI Agent is a complex intelligence system composed of several functional layers that interact organically to arrive at a final decision.
Layer 1: Data Ingestion & Semantic Filtering: Connects to data sources like the Twitter API to collect and filter raw data in real-time, identifying relevant crypto-related discourse and filtering out noise.
Layer 2: Proprietary Knowledge Graph & Fact-Checking Engine: Integrates collected information into a structured knowledge graph, mapping the complex relationships between tokens, KOLs, and market events. A fact-checking engine cross-verifies claims to ensure credibility.
Layer 3: Persona-Driven Generation Core (PGC): This is the generation engine that creates Miko's 'voice'. It runs a highly-tuned LLM on the verified knowledge from Layer 2, expressing Miko's diverse persona through various "reaction modes" to respond dynamically to any situation.
Layer 4: Selection Algorithm: The engine of the weekly asset selection. It combines rule-based filtering with a three-phase ML pipeline (Bayesian Optimization → Thompson Sampling → CatBoost Learning-to-Rank) that evolves as more cycles complete. An 'Engagement Feedback Loop' ensures the model continuously learns and improves from past performance.
Technology
Multi-Layered AI Architecture
A sophisticated intelligence system featuring a proprietary Knowledge Graph, a Fact-Checking Engine, and a self-improving Selection Algorithm.
The Miko AI Agent is a complex intelligence system composed of several functional layers that interact organically to arrive at a final decision.
Layer 1: Data Ingestion & Semantic Filtering: Connects to data sources like the Twitter API to collect and filter raw data in real-time, identifying relevant crypto-related discourse and filtering out noise.
Layer 2: Proprietary Knowledge Graph & Fact-Checking Engine: Integrates collected information into a structured knowledge graph, mapping the complex relationships between tokens, KOLs, and market events. A fact-checking engine cross-verifies claims to ensure credibility.
Layer 3: Persona-Driven Generation Core (PGC): This is the generation engine that creates Miko's 'voice'. It runs a highly-tuned LLM on the verified knowledge from Layer 2, expressing Miko's diverse persona through various "reaction modes" to respond dynamically to any situation.
Layer 4: Selection Algorithm: The engine of the weekly asset selection. It combines rule-based filtering with a three-phase ML pipeline (Bayesian Optimization → Thompson Sampling → CatBoost Learning-to-Rank) that evolves as more cycles complete. An 'Engagement Feedback Loop' ensures the model continuously learns and improves from past performance.
Technology
Multi-Layered AI Architecture
A sophisticated intelligence system featuring a proprietary Knowledge Graph, a Fact-Checking Engine, and a self-improving Selection Algorithm.
The Miko AI Agent is a complex intelligence system composed of several functional layers that interact organically to arrive at a final decision.
Layer 1: Data Ingestion & Semantic Filtering: Connects to data sources like the Twitter API to collect and filter raw data in real-time, identifying relevant crypto-related discourse and filtering out noise.
Layer 2: Proprietary Knowledge Graph & Fact-Checking Engine: Integrates collected information into a structured knowledge graph, mapping the complex relationships between tokens, KOLs, and market events. A fact-checking engine cross-verifies claims to ensure credibility.
Layer 3: Persona-Driven Generation Core (PGC): This is the generation engine that creates Miko's 'voice'. It runs a highly-tuned LLM on the verified knowledge from Layer 2, expressing Miko's diverse persona through various "reaction modes" to respond dynamically to any situation.
Layer 4: Selection Algorithm: The engine of the weekly asset selection. It combines rule-based filtering with a three-phase ML pipeline (Bayesian Optimization → Thompson Sampling → CatBoost Learning-to-Rank) that evolves as more cycles complete. An 'Engagement Feedback Loop' ensures the model continuously learns and improves from past performance.