Case Study: Upmonth
Upmonth is a cutting-edge autonomous document management system designed specifically for investors and investment companies. Its primary goal is to streamline document organization by minimizing the burden of filing and managing files. Upmonth automatically uploads all documents & emails in a single repository while organizing & tagging the files for easy tracking & management. With the help of machine learning technology, itʼs able to find the document in an instant.
Project Overview
Inefficient file organization and locating processes can result in significant time loss within a company. On average, employees spend 1.8 hours per day organizing files and establishing systems, according to a McKinsey study. Additionally, employees dedicate 5-20% of their time to recreating existing documents due to difficulties in locating them, as found by IDC. Searching for documents can consume approximately 8.8 hours per week for employees, according to the same IDC study, and up to 50% of their time, as estimated by PwC.
As per the stats mentioned above, one thing is evident – inefficiencies and time management are major issues in almost every office, and they often go unnoticed. Upmonth is the optimal solution to this issue. Not only does it automatically & seamlessly upload all the documents & emails into a single repository, but it also tags the files depending on the nature of the files.
Thanks to advanced machine learning and AI capabilities, it can locate the desired files instantly. So, nothing is holding back your productivity. With tickers you compound performance instead of lost time.
Key Findings
Following were the key findings engineers at Xeltec
identified:
Understanding Tickers' significance:
The team thoroughly comprehended the role of Tickers within the project, ensuring a solid foundation for subsequent enhancements.
System mapping techniques:
Techniques such as Event Storming, Example Mapping, and Persona Analysis were utilized to gain a holistic understanding of the system’s requirements and identify gaps in both functional and technical areas.
Leveraging NLP techniques:
Advanced Natural Language Processing (NLP) techniques were applied to extract meaningful information from hedge fund names. This involved breaking down fund names into constituent parts, such as keywords, acronyms, or abbreviations, to enhance their comprehensibility
Problem Statement
Lack of consistency in Fund names pattern:
Inconsistent fund name patterns can cause confusion and hinder effective search and comparison of funds in the document management system. Hence can disrupt the application performance and user experience of investors and investment companies.
02
Lengthy and non-descriptive Tickers:
Long and vague tickers make data entry error-prone and impede efficient fund identification and analysis in the autonomous document management system for investors and investment companies.
03
Uniqueness concerns of tickers:
Non-unique tickers lead to conflicts, data integrity issues, and inaccurate reporting in the autonomous document management system designed for investors and investment companies.
Implemented Solution
Following were the solutions implemented by Xeltec while developing Upmonth:
Architecture revamp & Database construction
The entire backend architecture was revamped to enhance performance, scalability, and maintainability. Rest API enables communication between different systems over the internet. A comprehensive database was constructed to store all Tickers and their respective components for future use, enabling streamlined retrieval and management of investment documents.
Test-driven development and automation
Test-driven development methodologies were adopted, ensuring system reliability and robustness. Automation techniques were leveraged to streamline testing processes. Automation techniques were also leveraged in the development phase of the application. Deployment on AWS SES for automatic email communication provided a reliable and scalable solution for sending emails programmatically, such as notifications, newsletters, and transactional emails.
Normalization of Fund Names and Ticker creation
Fund names were normalized by dividing them into meaningful components and removing punctuations from it. An algorithm was developed to convert these components into acronyms, which were then used to generate unique and descriptive Tickers. A Django application and Admin dashboard were built to facilitate efficient management of these Tickers.
Results
An investment office creates an average of +3000 folders and 21,000 unnecessary duplicated files to find their documents. Imagine this much inefficiency in a workplace where every zero matters.Upmonth, developed by our expert team, revolutionizes how companies handle their documents, providing unparalleled efficiency and savings. Upmonth empowers businesses to streamline document processes, saving valuable time and resources. Upmonth ensures compliance and security with robust features such as automated version control and granular access permissions. Experience accelerated decision-making with quick access to relevant information, leading to better business outcomes.
Upmonth provided companies with cost-effective solutions, reducing administrative overheads, printing costs, and optimizing resource utilization. Collaborate seamlessly and access documents anytime, anywhere with Upmonth’s advanced collaboration and accessibility features.
