Bulk Documents Classification & Extraction Automation with Python and AI

USE CASE

Documents Classification

INDUSTRY

Loans & Real Estate

HOURS RECLAIMED

40 man-hours per month

COST SAVINGS

$1500+ per month

Algorythum Engineered Solution

At Algorythum, we pride ourselves on crafting automation solutions designed to deliver maximum impact with minimal ongoing maintenance. In the case of A&M Investment, this meant tackling their document workflow challenge with a two-pronged approach: : one end-to-end automation for seamless processing and a human-in-the-loop solution to handle any edge cases.

1. End-to-End Automation:

  • Google’s Document AI: Trained on A&M Investment’s historical data, this AI accurately recognized different document types within bulk PDFs.
  • Python Integration: Handled file uploads from the client’s web app and deployed the solution on Google Cloud Platform.
  • DocPro AI (client-named): This intelligent system classifies and splits the documents, and uploads them to the designated CRM and database tables.
  • Email Notifications: Alerted internal teams when documents were ready for evaluation.

2. Human-in-the-Loop Automation:

  • Attended Bot: Addressed edge cases where AI-based splitting was unsatisfactory. Users could specify page numbers for manual document extraction.
  • Python Flexibility: Developed this extension, offering more control than typical RPA tools.
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Technologies Used

google-cloud
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Impact & Benefits

  • Cost savings: Eliminated 40 hours/month manual repetitive work per resource, and saving over $2,500 annually from SaaS and RPA subscriptions.
  • Turnaround time: Reduced by 95%, completing tasks within minutes instead of days.
  • Reduced human dependency: Increased process independence with minimal human intervention.
  • Employee satisfaction: Freed employees from repetitive tasks, fostering a sense of purpose and improved satisfaction.

Bespoke Automation,
Maintenance in Hibernation

Our solutions cut your business automation expenses by 90%.
Because that's the real cost.

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