AI/ML driven talent acquisition

Recruiters are currently facing a daunting task of sifting through large numbers of job applications, with many candidates lacking the necessary employability. As a result, many firms are struggling to maintain a structured and efficient recruitment process, leading to a reduction in the quality of new hires. This is largely due to the manual screening process, which can result in false negatives resulting from skill word matching.  

To put things in perspective, studies show that 75% to 88% of resumes received in response to a job opening are unqualified, and the average cost of screening resumes is about 4-5 USD. Furthermore, talent acquisition leaders report that the screening of candidates from a large applicant pool is the most challenging aspect of recruitment, with companies spending an average of 25-30 hours (at 6 minutes per resume) screening resumes for a single hire. (Source: Ideal, Glassdoor Economic Research) 

What an AI-based recruiting platform offers recruiters 

High reliability - Advanced techniques can be put to use to detect impersonations by giving recruiters the right details to verify candidates 

Faster reach - AI can extract data from large applicant pools intelligently and help you reach the right candidates quicker. 

Timely Recruitment - AI-based automation allows the process of recruitment much faster and sends timely responses to the chosen candidates. 

The TalentTapp way of acquiring right talent, faster 

TalentTapp is an AI-based recruiting technology that helps you improve your quality of hiring be it high-volume hiring or niche hiring. The key is to reach the right candidates sooner and BigTapp’s proprietary semantic-based match enabler, TalentTapp, does just that. It uses Natural Language Processing (NLP), Artificial Intelligence (AI) and Deep Learning (DL) hybrid framework to provide an integrated profile screening, profile matching and candidature scoring platform. This recruiting technology leverages NLP and AI/ML to not just collect candidate preferences but also predict candidate intent. 


Hybrid Profile Parser to improve parsing accuracy

TalentTapp parser uses a hybrid combination of NLP, Deep Learning, smart agents and pattern recognition and much smaller components to do Lexical Analysis, Syntactic Analysis, and Semantic Analysis to improve the parsing accuracy. 

Customizable Skills Taxonomies to improve flexibility

Our built-in skills taxonomy starts with over 20,000 skills that you can add to, modify or swap out to suit the needs of your company. 

User Configurable Parsing & Matching needs

Users can decide how they want the parsing needs for a transaction and matching process to score, rank and sort the best matches. 

Reverse Scoring to mimic recruiter thinking

The AI-based profile-position match engine exactly understands and recognizes the profiles as humans with career aspirations - not just a bunch of keywords. Our Scoring engine recognizes that a Senior Programmer is a more pertinent match for a programming role than an Architect / Manager with previous programming experience. It does both machine & recruiter thinking! 

Multi-fold matching

The AI-based profile-position match engine can be configured in multiple ways. It can match available jobs to profiles or match profiles to relevant jobs, for example. 

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