System Design
Last updated
Last updated
The AI Research Orchestrator (ARO) is a modular AI framework system designed to handle diverse analytical tasks efficiently. It integrates multiple reasoning layers, specialized tools, and data storage mechanisms to process inputs and deliver insights in a structured and automated manner. This section provides a technical overview of the key components and processes within ARO.
ARO’s design emphasizes modularity, scalability, and interoperability. Its core components include:
Input
Accepts requests from chat, UI dashboard, APIs.
Validates and formats queries for downstream modules.
Multimodel Reasoning Layer
A cluster of LLMs—GPT-4o, o1, DeepSeek-R1, Claude 3.5 Sonnet, Gemini-2.0, Grok-2, Qwen2.5, etc.
Adaptive Model Selection dynamically chooses the most suitable model(s) for each query, optimizing for task requirements such as text processing, numerical analysis, or data interpretation.
AI Research Orchestrator
Core logic layer that routes tasks between LLMs, specialized tools, and reasoning modules to ensure tasks are processed effectively and efficiently.
Advanced reasoning capabilities that analyze and break down complex queries into manageable components, assigning the best-suited resources for each part of the task.
Aggregation of outputs from models and tools into final, consolidated results, providing users with actionable insights in a unified format.
Orchestration Intelligence layer
Over 80 specialized tools (financial analytics, blockchain explorers, sentiment analysis, etc.).
Capable of parallel or sequential execution to optimize performance.
Memory System
Short-Term Memory: Caches intermediate data during a session.
Long-Term Memory: Archives historical data for trend analysis and ongoing model improvements.
Dataset Creation Module
Ingests data from tasks and external sources, then cleans and stores it in structured datasets.
Output
Delivers insights via chat, UI dashboard, automated responses.
