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Before AI Talent Sourcing
What talent sourcing looked like before semantic AI — slow, fragmented, and inefficient.
Manual Candidate Filtering
Recruiters manually browse job boards and platforms one by one, spending hours searching for profiles that may not even match the role.
Platform-by-Platform Sourcing
Talent teams repeat the same searches across LinkedIn, Indeed, InfoJobs, and dozens of other portals — duplicating effort with inconsistent results.
Keyword-Based Matching
Traditional filters rely on rigid keywords, missing qualified candidates who don’t match exact terms despite having the right experience.
Limited Talent Visibility
Sourcing is restricted to a handful of platforms, leaving valuable candidates undiscovered across thousands of job sites and databases.
Slow and Outdated Results
By the time candidates are found, profiles are often outdated, unavailable, or already engaged elsewhere.
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CV Screening Is Obsolete
Resumes don’t reflect candidate’s true skillsets - CV Screening is obsolete.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Outdated ATS Limitations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.
