Transforming Dropbox-Style Document Storage with AI for Aviation MRO

The aviation MRO (Maintenance, Repair, and Overhaul) industry operates in a landscape where precision, compliance, and efficiency are critical. Yet, most organizations still rely on traditional file storage solutions like Dropbox, Google Drive, or shared network drives. These systems, while useful for document storage, lack the intelligence needed to automate workflows, ensure compliance, and integrate seamlessly with enterprise operations.

Enter AI-Powered Document Processing: a technological leap that transforms static folders into dynamic, context-aware systems that integrate directly with your business flow.

The Current Problem: Passive Document Repositories

• Lack of Context: Traditional folder structures are static, relying on manual organization and retrieval.
• Inefficiency: Users waste time searching, tagging, and verifying compliance-critical documents.
• Missed Opportunities: Metadata tagging alone cannot unlock the full potential of the data trapped in aviation documents.

The AI Advantage: Intelligent Document Systems

With ePlaneAI, passive repositories are transformed into intelligent, context-aware systems capable of automatically classifying, processing, and routing aviation documents.

Unlike traditional solutions, ePlaneAI leverages advanced AI models to understand the structure, layout, and context of aviation-specific documents such as Airworthiness Certificates, Invoices, and Work Orders. This allows for instant classification, targeted data extraction, and seamless integration into enterprise workflows.

Take the example of multi-page PDFs:

• Instead of manually splitting files, ePlaneAI can virtually segment them, creating granular links to specific pages while preserving the document’s integrity.
• Whether it’s extracting line items from an invoice or tracking numbers from a release certificate, ePlaneAI ensures 99%+ accuracy, even with high document volumes.

Solving the Current Problems

Traditional Document Repositories suffer from:

1. Lack of Context: Folder hierarchies and metadata tagging are rigid and error-prone.
2. Inefficiency: Users spend hours searching for or tagging compliance-critical documents.
3. Missed Opportunities: Metadata alone cannot unlock the full value of data trapped within documents.

ePlaneAI’s Approach: By leveraging AI-powered document processing, ePlaneAI transforms static repositories into proactive, dynamic systems that automatically identify and process documents, saving time, reducing costs, and ensuring compliance.

1. Making Documents Instantly Smart

Step 1: Use AI Models to Classify and Extract Data

AI can analyze document content, layouts, and metadata to understand the document type and extract actionable data, all in real-time.

Key Technologies:

• Pre-trained Transformers: Models like LayoutLMv3, Donut, and DocTR are designed for document understanding and layout-based classification.
• OCR with NLP: Combine Optical Character Recognition (OCR) tools like Tesseract or Google Vision API with NLP to handle scanned documents.
• Integration with File Systems: Automate processing upon document upload in a cloud drive or file system.

Code Example: Automating Classification on Upload:

import os
from transformers import LayoutLMv3ForSequenceClassification, AutoTokenizer

model = LayoutLMv3ForSequenceClassification.from_pretrained("microsoft/layoutlmv3-base")
tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlmv3-base")

def classify_document(file_path):
    with open(file_path, 'r') as f:
        text = f.read()
    inputs = tokenizer(text, return_tensors="pt", truncation=True)
    outputs = model(**inputs)
    return outputs.logits.argmax(dim=1).item()

# Watch folder for new uploads
watch_folder = "/path/to/dropbox/folder"
for file_name in os.listdir(watch_folder):
    file_path = os.path.join(watch_folder, file_name)
    document_type = classify_document(file_path)
    print(f"Classified {file_name} as type {document_type}")
        

Step 2: Document Splitting Without File Separation

Multi-page PDFs can be virtually split into sections, allowing users to interact with specific pages without duplicating or separating the file.

How It Works:

• Indexing Pages: AI creates a searchable index for each page.
• Granular Linking: Every page or section gets a unique, shareable URL.

Code Example: Granular Page Linking:

from PyPDF2 import PdfReader

def extract_page_links(pdf_path):
    reader = PdfReader(pdf_path)
    page_links = {}
    for i, page in enumerate(reader.pages):
        page_links[f"Page {i+1}"] = f"https://yourdomain.com/pdf/{os.path.basename(pdf_path)}#page={i+1}"
    return page_links

# Example usage
pdf_file = "document.pdf"
links = extract_page_links(pdf_file)
print(links)
        

2. Seamless Integration into Business Flows

Custom Workflows Using AI

Traditional storage solutions do not integrate natively into aviation workflows. AI-enhanced systems can:

• Automatically route documents to relevant teams (e.g., PO to procurement, Work Orders to MRO teams).
• Trigger workflows based on document content (e.g., creating repair tasks upon receiving a Work Order).

Example: Real-Time Workflow Triggering:

import requests

def trigger_workflow(document_type, data):
    if document_type == "Work Order":
        # Example: Create a task in an ERP
        response = requests.post("https://your-erp.com/api/tasks", json=data)
        print("Workflow Triggered:", response.status_code)

# Process a new Work Order
document_type = "Work Order"
data = {"part_number": "12345", "quantity": 2, "status": "Repair Needed"}
trigger_workflow(document_type, data)
        

Real-Time Data Updates in ERP Systems

By integrating directly with your ERP, AI-powered document systems ensure all extracted data (e.g., part numbers, quantities, approvals) is updated in real time.

Code Example: ERP Integration:

import requests

def update_erp(data):
    url = "https://your-erp-system.com/api/update"
    response = requests.post(url, json=data)
    if response.status_code == 200:
        print("ERP updated successfully!")
    else:
        print("Error updating ERP:", response.status_code)

# Example update
data = {"part_number": "12345", "status": "In Stock"}
update_erp(data)
        

3. Advanced Features for Top Companies

Event-Driven Processing

Instead of manual uploads or schedules, event-driven processing triggers workflows the moment a document is uploaded or updated.

Technologies:

• AWS Lambda or Google Cloud Pub/Sub for event handling.
• Webhook Integration for real-time updates.

Continuous Machine Learning

AI models continuously adapt by learning from each new document processed, improving classification and extraction accuracy over time.

Key Tools:

• AutoML frameworks like Hugging Face AutoTrain or Google AutoML Vision.
• Feedback Loops to refine model predictions using human validation.

4. Security and Compliance

For the aviation industry, compliance is non-negotiable. AI-powered systems are built with security at their core:

• End-to-End Encryption: Protect sensitive aviation data.
• Audit Trails: Maintain detailed logs for every document interaction.
• Regulatory Compliance: Ensure all outputs align with FAA, EASA, or other regulatory standards.

Code Example: Generating an Audit Log:

import json
from datetime import datetime

def log_document_action(document_name, action, user):
    log_entry = {
        "timestamp": datetime.now().isoformat(),
        "document": document_name,
        "action": action,
        "user": user
    }
    with open("audit_log.json", "a") as log_file:
        log_file.write(json.dumps(log_entry) + "\n")

log_document_action("Work_Order_123.pdf", "Reviewed", "technician_01")
        

Real ROI and Impact

Adopting AI-driven document processing yields measurable benefits:

1. Time Savings: Reduce processing times from minutes to seconds.
2. Cost Efficiency: Save up to 75% on document handling costs.
3. Error Reduction: Achieve over 99% accuracy in data extraction.

Future Vision: Dynamic Document Systems

As AI evolves, document systems will:

• Predict Workflows: Automatically suggest next steps based on document content.
• Enhance Collaboration: Link documents to collaboration tools like Slack or Teams.
• Enable Real-Time Monitoring: Provide dashboards to track document status across workflows.

By leveraging AI, aviation companies can transform their document workflows, ensuring efficiency, compliance, and scalability in even the most demanding environments.

Let’s discuss how ePlaneAI can empower your aviation document management systems. Contact us for a demo!