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Explore How Mariapps Achieved Automated, Accurate, and Scalable Document Processing Within Minutes

What if your crew documentation could be verified automatically eliminating manual data entry, reducing human errors by 98%, and accelerating compliance checks across all departments?

Let’s explore how Pixl.ai made it possible.

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INTRODUCTION

Transforming Crew Documentation Through Automated Document Parsing

Mariapps a global maritime solutions provider, manages thousands of crew and employee documents daily including passports, seaman’s books, licenses, training certificates, and visas.However, their manual document management process created significant operational challenges, leading to delays, errors, and compliance risks.

To overcome these bottlenecks, Mariapps partnered with pixl.ai to build and deploy a reliable, high-accuracy Automated Document Extraction and Classification System designed to streamline document handling, improve accuracy, and ensure compliance at scale.

CHALLENGES

The Challenges Faced by Mariapps

Mariapps encountered several operational hurdles due to manual document processing:

1. Manual Data Entry

Teams were manually reading and typing details such as Name, Document Type, COC, Issue Date, and Expiry Date causing delays and inconsistent inputs.

2. High Error Rates

Different document formats, languages, and layouts frequently led to typing errors, incorrect dates, and mismatched details.

3. Compliance Risks

Incorrect or missed expiry dates increased the risk of regulatory non-compliance during crew onboarding and audits.

4. Operational Delays

Manual document verification created bottlenecks during large crew rotations, slowing onboarding and internal workflows.

5. Limited Scalability

As document volume grew, the existing process required more manpower instead of enabling automation and efficiency.

SOLUTION

How pixl.ai’s Automated Document Parser Transformed Mariapps’ Document Workflow?

Pixl.ai engineered a powerful, modular, and scalable OCR-powered Document Parsing Platform customized for Mariapps’ needs. The solution automated extraction, detection, and classification for the following initial document sets:

  • Travel IDs: Passports, Seaman’s Books, US Visas

  • Licenses

  • Training Certificates

OCR-Based Data Extraction

The system automatically extracted Name, Document Type, COC, Issue Date, and Expiry Date handling multiple formats with high accuracy.

Automated Document Classification

Intelligent classification instantly identified whether a document was a Travel ID, License, or Training Certificate.

Data Standardization

Extracted data was converted into a clean, uniform structure for seamless downstream processing.

API-Driven Integration

The parsed and verified data was pushed directly into Mariapps’ internal crew management and compliance platforms.

Future-Ready Expandability

The modular design allows quick addition of new document types like medical records and supporting documents without major redevelopment.

Transformational Results

The Outcomes Speak for Themselves

90% Reduction in Manual Workload

Teams no longer needed to manually read or type document details.

Improved Accuracy

Automated extraction significantly minimized errors, especially in critical fields like dates and document numbers.

Faster Processing

Document verification became faster and smoother, enabling quicker crew onboarding and audit preparation.

Strengthened Compliance

With accurate expiry data, Mariapps can now proactively monitor document validity and reduce compliance risks.

High Scalability

Large volumes of documents can now be processed without additional manpower or operational strain.

Accurate, Automated, and Scalable Document Parsing in Minutes

With Pixl.ai’s Automated Document Parser Mariapps successfully transformed its documentation workflow achieving speed, accuracy, and operational efficiency at scale.
The platform eliminated manual effort, minimized errors, improved compliance readiness, and created a strong foundation for future automation across additional document types