Automation of document management based on AI
stack
React, Python
service
IT product development
AI model development
AI model development
development period
5 months
A client from a large organization approached us with a problem of processing a large volume of incoming documentation. Employees were spending up to 40% of their working time checking, sorting, and sending documents to the appropriate destinations. Errors and delays led to inconsistencies between departments, increased costs, and dissatisfaction among partners.
Task
Create a system that automatically analyzes documents, distributes them into categories, checks compliance with corporate requirements, and sends them to responsible employees.
Start of work
At this stage, we conducted interviews with key employees:
- Document processors who handle incoming documents
- Department heads who depend on timely delivery of documents
- IT specialists responsible for the current infrastructure
Identified main problems:
- Incoming documents were processed manually, which took too much time
- Errors occurred due to human factor (duplication, incorrect distribution)
- There was no unified system for checking the format and structure of documents
Current stages of document processing:
- Receiving the document via mail, scanning, or physical delivery
- Checking for mandatory attributes (signature, date, details)
- Manual sorting by department
- Entering into the database
Development Stages
Create a system that:
- Automatically receives documents from different sources (email, scanners, digital mail)
- Analyzes their content and format
- Classifies documents by category and department
- Checks for errors and notifies responsible employees
We agreed upon our methodology with the client:
- Task DecompositionEach stage (reception, analysis, classification, verification) was developed separately to minimize risks
- Phased ImplementationTesting on small volumes of documents before full launch
- Integration of the Customer in the ProcessRegular demonstrations, feedback collection, and adjustments
The client provided an archive of documents (50,000+ files). Scans of physical documents and electronic documents from email and other systems.
AI Training
We developed a model that:
- Recognizes text from images (OCR)
- Analyzes the structure of the document (presence of signatures, dates, details)
- Determines the category of the document (contract, report, letter, invoice)
- Verifies compliance with corporate standards
For users, we created a user-friendly interface:
- Document UploadReceives files from scanners, email, cloud storage
- Verification PanelSystem displays the status of the document (accepted, rejected, sent to department)
- NotificationsAutomatic distribution to responsible employees with the results of the check
Began testing on pilot group
Launched the system in two departments. Over the course of a month:
- Compared the efficiency of the new system with manual processes
- Collected feedback from employees using the system
- Fixed bugs and improved the interface
Full Launch
After successful testing, the system was rolled out across the entire company. Employee training was conducted and instructions were issued.
Results
- Speed of document processing increased by 50%
- Accuracy of classification reached 98%
- Reduction of errors by 70%Automated verification eliminated most human factors
- Time savingsEmployees were able to focus on more important tasks instead of performing routine work
- TransparencyEvery document is tracked within the system, eliminating the possibility of loss
Future Plans
- Integration with CRM systems and internal portals for automatic document processing
- Adding functionality for analyzing large volumes of historical data
- Using AI for automatic generation of reports on document workflow
This project demonstrated how AI technologies can improve efficiency and transparency in such a traditional process as document management.
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