Data Flow & Multimodel Reasoning
Last updated
Last updated
User Request
Example: “What is the long-term analysis for Bitcoin $BTC”
Sent via chat , UI dashboard, API call.
AI Research Orchestrator Receives the Task
Classifies the query (market sentiment, price prediction, or code interpretation).
Multimodel Reasoning Layer
Model Selection: Chooses o1/deepseek r1 for textual reasoning, or Grok for advanced sentiment analysis, etc.
Collaboration: Multiple LLMs can be used concurrently for complex, multi-part questions.
Tool Interactions
The orchestrator invokes relevant tools in the Orchestration Intelligence (e.g., on-chain, sentiment, or financial analytics) for data gathering or specialized computations.
Aggregation & Finalization
Partial outputs (from LLMs + Orchestration Intelligence) are merged into a cohesive final answer.
Logic checks by AI Research Orchestrator ensure data consistency and integrity.
Dataset Logging
Key outputs are archived for historical reference and to refine future model performance.
Output Delivery
Final insights appear in web app chat reply, an interactive dashboard, PDF/HTML report, or as a structured API response.
