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Benchmarking a Resume parser with a 70% accuracy rate using Machine Learning


Project Brief

Experts at Agira technologies always start by carefully analysing the existing system and examining the client’s requirements, we decided to build a new application. The new application was architectured focusing on two components; Web-based UI, and the Parsing Engine

Technology Stack
Django,PostgreSQL,Flask
Services
Web - Resource Augmentation
Industry
Human Resources
Deliverables
UX, UI, Axure Wireframe

Challenges We Faced

  • Inefficient

    The client fully relied on the existing system which parses resumes only in three different formats and quite inefficient.

  • Non Customizable

    The HR-XML output of the existing system was in-capable to let the users customize it for their requirement.

  • Time Consuming

    The existing system was lagging and highly time-consuming to parse a resume, as the volume of resumes increased.

  • Unscalable

    Inadequacy of the existing system and conventional technology stack to meet the requirements of the rapidly growing company.

Key Features and Solution
Web
                      development

Notifications Integration

Conducted security audits for AWS Cloud as well as on servers

Mobile
                      development

Analytics Integration

Implementation of the Prerender for ideal AngularJS SEO

Blockchain
                      development

Requirement Analysis

We identified the gaps in the requirements and enhanced the web portal functionalities within a minimal time limit

Cloud
                      Computing

Payments Integration

Reduction of third-party dependencies and Deployed an Application in AWS Cloud to minimize the cost without compromising on the performance of the application

DevOps
                      development

Server Setup & Backup

Leverage the caching mechanism to achieve faster loading time

Rescue
                      projects

Security Compliance Check

Delivery a seamless application with 24*7 server monitoring and cloud support with patch management for servers

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