Enhancing Job Portal Efficiency with Automation

Ever thought assembling a cutting-edge software team in just 16 days was impossible? Think again. DAC.digital not only achieved this but also set a new benchmark for rapid team formation. Join us as we unveil the story behind this incredible achievement – a blend of strategy, collaboration, and innovation that will reshape your perception of what's attainable in the tech world. Ready to be inspired? Keep reading!

About the client

Name: Swiftly
Line of business: Automated Recruiting and Unbiased Recruitment tools
Founding year: 2020
Country: Sweden

Problem overview

Swiftly, a Stockholm-based startup, grappled with two significant challenges within their job portal. Firstly, accurate categorization of job listings posed difficulties, leading to suboptimal user experiences and ineffective job matching. Secondly, the manual job application process was time-consuming and resource-intensive, restricting scalability.

Proposed solution

Our approach comprised two pivotal components:

Web Scraping Tool: We developed a sophisticated web scraping tool to extract precise keywords from job listings, enhancing categorization accuracy.
SOTA Presentation: We created a visionary state-of-the-art (SOTA) presentation, demonstrating automated field auto-fill capabilities to streamline the application process.

Applied technologies:

Python, Selenium and FastAPI were used to implement a service able to scrap form fields from a given website, and to fill automatically the forms once the data are provided
Neo4J and PostgreSQL were databases used for storing graph data describing relations between job offers, job seekers and other data which can be used to look for mutual associations, as well as more general and structured metadata of job offers.
Sklearn was used to implement a recommendation engine looking for best matches between job seekers and job offers.

Pre-existing Challenges:

Before implementing the SOTA and POC solutions, Swiftly faced several challenges:

Inaccurate Categorization: Swiftly encountered difficulties in accurately categorizing job listings, causing mismatched job offers and candidates.
Manual Application Process: Manual application processes consumed time and resources, impeding scalability.
Insufficient Automation: The absence of automated keyword extraction led to imprecise job listing categorization.
Scaling Issues: Manual processes and categorization limitations hindered scalability.
Lacking Technological Strategy: Swiftly lacked a comprehensive technology-based strategy to enhance categorization accuracy and streamline processes.

Implementation Approach.

Our implementation strategy followed these steps:

Initial Talks and Kickoff

Collaborative discussions between Swiftly’s leadership and DAC.digital’s technical team laid the groundwork for a productive partnership, aligning expectations and goals.

Team Composition

An 8-person team, comprising ML Engineers, Embedded Systems Engineers, Data Scientists, and Fullstack Developers, came together to tackle the project.

Agile Collaboration

Daily stand-up meetings and ongoing communication facilitated iterative development and enhancements.

Results and Impact:

The project concluded with the creation of an advanced SOTA solution that effectively addressed Swiftly’s challenges. This solution improved job listing categorization precision and streamlined the application process. The SOTA also offered a proof-of-concept for refining job listing keywords and automating application field population.

Results

Swiftly’s collaboration with DAC.digital resulted in the successful resolution of their job portal challenges through the implementation of innovative automation solutions. The web scraping tool and the SOTA presentation highlighted the potential of technology to enhance processes, elevate user experiences, and pave the way for future enhancements.

Key numbers

Project Duration: Successfully completed within 16 days!

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