Data Flow & Multimodel Reasoning

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• Key outputs are archived for historical reference and to refine future model performance.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.