How Notamify Uses GPT (and other LLMs) and AI Agents to Revolutionize NOTAM Interpretation

In today's fast-paced aviation world, making sense of NOTAMs (Notice to Air Missions) quickly and accurately is crucial. At Notamify, we've harnessed the power of GPT and specialized AI agents to transform complex NOTAM data into clear, actionable briefings. Let's dive into how our technology makes this possible.

The Challenge with Traditional NOTAMs

Consider this typical NOTAM:

Q9314/24 NOTAMNQ) ULLL/QRTCA/IV/BO/W/000/050/6426N05524E036
A) ULLL B) 2408250500 C) 2408301700
D) 25-30 0500-1700
E) AIRSPACE CLSD AS FLW:
1. WI CIRCLE RADIUS 1KM CENTRE 642838N0551543E SFC-1220M AMSL.

Cryptic, right? This is where our GPT-powered solution comes in. Our system transforms this into clear, structured information that looks like this:

{
  "location": "ULLL",
  "type": "AIRSPACE CLOSURE",
  "timing": {
    "start": "2024-08-25 05:00Z",
    "end": "2024-08-30 17:00Z",
    "daily": "0500-1700"
  },
  "impact": {
    "type": "CLOSURE",
    "area": "CIRCLE",
    "radius": "1KM",
    "center": "642838N0551543E",
    "vertical_limits": {
      "lower": "SFC",
      "upper": "1220M AMSL"
    }
  }
}

How Notamify's GPT Integration Works

1. Structured Data Extraction

Our system uses GPT with custom function calling to break down NOTAMs into structured data. Here's a simplified example of how we define our function calls:

interface NOTAMExtraction {
  extractStructuredData: {
    parameters: {
      type: "object",
      properties: {
        raw_notam: { type: "string" },
        required_fields: { 
          type: "array",
          items: { type: "string" }
        }
      }
    },
    returns: {
      type: "object",
      properties: {
        location: { type: "string" },
        timing: { type: "object" },
        impact: { type: "object" },
        coordinates: { type: "array" }
      }
    }
  }
}

This structured approach ensures we capture:

Timing details
Geographic coordinates
Altitude restrictions
Operational impacts

2. AI Agents for Contextual Understanding

Our AI agents are role-specific neural networks trained on millions of historical NOTAMs and their interpretations. Each agent specializes in:

interface RoleSpecificAgent {
  role: "PILOT" | "ATC" | "GROUND_OPS";
  interpretation_level: "SHORT" | "DETAILED";
  priority_factors: string[];
  relevant_operations: string[];
}

These agents process the structured data to generate role-specific interpretations like:

// Example output for a pilot
{
  short_brief: "Airspace closure around ULLL up to 1220M AMSL, daily 0500-1700Z Aug 25-30.",
  detailed_brief: "Temporary restricted area established within 1KM radius of 642838N0551543E, affecting approaches to ULLL. Closure extends from surface to 1220M AMSL, active daily 0500-1700Z between Aug 25-30. Consider alternative routing during active hours.",
  impact_level: "MODERATE",
  affected_operations: ["APPROACH", "DEPARTURE", "LOW_LEVEL_ROUTES"]
}

Dual-Layer Briefing System

Our briefing system uses a sophisticated pipeline to process NOTAMs:

graph LR
    A[Raw NOTAM] --> B[GPT Extraction]
    B --> C[Structured Data]
    C --> D[AI Agents]
    D --> E1[Short Brief]
    D --> E2[Detailed Brief]

Concise Briefings

Perfect for quick checks, our short briefings provide:

Essential information only
Clear action items
Time-critical alerts

Example of a concise briefing generation:

interface ConciseBriefing {
  max_length: 150;
  priority_items: ["CLOSURE", "RESTRICTION", "HAZARD"];
  format: "BULLET_POINTS" | "SINGLE_PARAGRAPH";
  include_timing: boolean;
}

Detailed Briefings

For comprehensive planning, detailed briefings offer:

Full context and implications
Related NOTAMs correlation
Extended validity periods
Operational recommendations

The Technical Edge

What sets Notamify apart is our unique combination of:

Custom GPT function calling for precise data extraction
Role-specific AI agents for contextual interpretation
Structured output formatting for consistent delivery
Real-time processing capabilities

Our system processes NOTAMs in milliseconds:

interface ProcessingMetrics {
  extraction_time: "~100ms";
  interpretation_time: "~200ms";
  briefing_generation: "~150ms";
  total_processing: "~450ms";
}

Future of NOTAM Processing

As GPT technology evolves, we're continuously enhancing our system to:

Improve interpretation accuracy through advanced training
Expand language support with multilingual models
Develop more specialized briefing types
Integrate with more aviation systems

Ready to Experience Smart NOTAM Processing?

Don't let complex NOTAMs slow down your operations. Join the growing number of aviation professionals who trust Notamify for clear, accurate NOTAM interpretations.

Try Notamify's AI-Powered NOTAM Briefings Now →

Want to integrate our AI-powered NOTAM processing into your systems? Check out our API documentation to get started.