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Cortex uses an in-built reasoning engine that understands complex queries and automatically breaks them down into manageable, logical steps. This sophisticated system can interpret multi-layered questions, determine the optimal sequence of actions, and orchestrate multiple parallel searches to deliver comprehensive, contextually-aware answers.

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In-Built Reasoning Engine

What it is:
Cortex’s in-built reasoning engine is a sophisticated system that understands the complexity and nuance of natural language queries. When you ask a complex question, the engine automatically:
  • Decomposes multi-part queries into logical sub-tasks
  • Analyzes the relationships between different components of your question
  • Orchestrates multiple parallel searches across your knowledge base
  • Synthesizes information from various sources into a coherent, complete response
  • Maintains context throughout the entire reasoning process
Instead of treating each query as a simple lookup, the reasoning engine “thinks” through multiple moves, just like a human expert would approach a complex problem.

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Multi-Step Query Processing

The reasoning engine excels at handling sophisticated workflows that require multiple steps: Sequential Reasoning: Understanding when one piece of information depends on another and executing searches in the correct order. Parallel Processing: Running multiple searches simultaneously when tasks are independent, dramatically improving response time. Context Preservation: Maintaining relevant context across all steps to ensure the final answer is coherent and comprehensive. Dynamic Adaptation: Adjusting the reasoning path based on intermediate results to optimize for the most accurate final answer. Benefit:
Your AI can handle sophisticated workflows, not just simple lookups. This means users can ask bigger questions and get full, intelligent responses that handle context switching, task breakdown, and complex reasoning chains. The in-built reasoning engine eliminates the need for users to break down their questions manually—they can ask naturally and get complete answers. Use Case & Scenario:
A manager asks, “Summarize all Q1 sales calls, extract the action items, and draft a follow-up plan.” The reasoning engine understands this requires three distinct but related tasks:
  1. Search and retrieve all Q1 sales call transcripts
  2. Analyze and extract specific action items from each call
  3. Synthesize the findings into a structured follow-up plan
Instead of returning a paragraph from one call, the reasoning engine orchestrates multiple searches, builds a comprehensive view of all calls, extracts actionable insights, and then creates a strategic plan based on the complete picture. This is how you build AI that actually works like a smart assistant—understanding intent, managing complexity, and delivering complete solutions.