From Boxes to Bytes
Indexing and Data Extraction
Solution
The project had an implementation deadline of three weeks to achieve the following:
- digitize a backlog of 7 million pages of HR records stored in 3500 boxes
- each HR file averaged 100+pages
- each file contained up to 64 sub-types
- 14 indexable categories were needed
The client, a public school, faced the monumental task of digitizing and indexing 60 years' worth of HR files. With 3500 full boxes containing a diverse array of documents, ranging from hiring documentation and salary notifications to letters of reprimand and personnel contracts, the client sought to transition from a paper-based system to a more efficient Document Management System (DMS). However, the manual process of sifting through each file, identifying document types, and categorizing records was proving to be time-consuming, averaging over one day per box.
The complexity of the project was driven by the diverse content within each HR file and the need for accurate categorization of documents into 14 distinct types. The system had to handle a variety of document sub-types efficiently.
The Aluma automated document processing solution revolutionized the digitization workflow by employing advanced machine learning algorithms. Its intelligent document recognition capabilities allowed it to autonomously identify and categorize each document type within HR files, reducing the manual effort required. Aluma's adaptability to diverse document sub-types ensured accuracy across the spectrum of HR records. The implementation also included a user-friendly interface for the scanning bureau staff, facilitating seamless interaction with the system and enabling real-time monitoring of the digitization progress.
The scalability of the solution not only addressed the immediate backlog, but also positioned the client to efficiently manage ongoing document processing needs with sustained efficiency.
Impact
Automation Accuracy
With Aluma in place, the scanning bureau experienced consistent automation rates of over 95%. The system autonomously sifts through each HR file, separates distinct documents, and labels them based on the client's 14 document types. This high accuracy significantly reduced the need for manual intervention and error correction.
Operational Efficiency
The implementation of Aluma streamlined the entire digitization process, enabling the scanning bureau to process up to 20 boxes per day with only 3 dedicated staff members. This marked a significant improvement over the previous manual approach, which required over one day per box.
Enhanced Accessibility and Searchability
The transition to a digital HR document management system enhanced accessibility and searchability of records. The categorized and indexed digital records facilitated quick retrieval of specific documents, contributing to improved overall efficiency in HR processes.
