Technology should help you get a job, not block you. Use our free tools to check job posts, find inclusive companies, and learn how to beat the online filters.
Paste a job description here. We will check if it looks like a scam or data-mining trap.
Search for a company name. See if they hire immigrants and use fair hiring technology.
Learn how to write your CV so the computer robots (AI) can read it and pass it to a human.
Come learn with us! We will teach you how to use LinkedIn and online job sites safely.
📍 Location: TBA
📍 Location: TBA
📍 Location: Online (Zoom Link Provided)
Explore how Applicant Tracking Systems (ATS) process non-traditional resumes. This data represents Phase 1 of our doctoral methodology, highlighting algorithmic friction points.
Select a candidate profile to see how an NLP (Natural Language Processing) parser extracts their data. Notice how non-standard formatting breaks the extraction.
This funnel simulates an audit of 10,000 diverse CVs passed through standard ATS filters. It visualizes the disproportionate drop-off rates for non-traditional candidates.
Stage 1: Total Applications Received (10,000)
Stage 2: Passed Structural Parsing (6,200)
Stage 3: Passed Semantic Matching (2,100)
Stage 4: Reaches Human Recruiter (450)
Our simulation utilizes a customized instance of the SpaCy NLP library, tuned to replicate the entity-recognition behavior of leading commercial ATS platforms. We specifically test for 'Algorithmic Friction'—points where human creativity or international norms conflict with rigid machine logic.
Downloads a sample 50-row CSV file of our test parameters.