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Before AI DataBase
What hiring looked like before automation
slow, biased, and inefficient.
slow, biased, and inefficient.
Manual Candidate Filtering
HR teams spend valuable time filtering manually irrelevant candidates due their poor qualification.
Redundant Screening Efforts
Repeating screening processes — duplicating efforts that could have been automated.
Outdated Candidate Outreach
Reaching out to unavailable candidates — outdated or unresponsive profiles.
Inefficient Job Reposting
Failing to capitalize on employer brand, increasing dependency on job portals.
Scattered Data Management
Managing spreadsheets, emails, and forms with no integration or traceability.
Manual Processes Waste Time
HR teams spend valuable time on manual processes where the human factor adds no value. Since over 95% of screened candidates are unqualified, automating this stage increases efficiency by over 85%.
CV Screening Is Obsolete
Resumes don’t reflect candidate’s true skillsets - CV Screening is obsolete.
Assessments Easily Manipulated
Conventional candidate assessments are highly vulnerable to AI manipulation.
Outdated ATS Limitations
Reliance on Traditional ATS Platforms - These tools focused on managing candidate pipelines, not analyzing candidates. Filtering was keyword-based, with not semantic understanding or contextual analysis
Lost Candidate Data
Inefficient Candidate Database - Rejected candidates forgotten in the ATS
Poor Candidate Experience
Iconsistent Candidate Experience - Employer branding suffered due to the perception of inefficiency and disorganization.
