AI-powered recruitment software for HMS

Harnessing AI for Enhanced Candidate Matching and Streamlined Hiring





Data science


Product Design


AI Development


AI, Azure Cognitive Services, React

  1. Leveraged AI to analyze and match candidates from extensive Hebrew language data pool
  2. Streamlined and optimized HMS’s talent identification process
  3. Transformed existing, underutilized HR data into a powerful recruitment tool
  4. Eliminated redundancy in hiring procedures, enabling focused engagement with top-tier talent

→ The challenge

HMS, a leading consultancy powerhouse in Israel, boasts a rich history of serving the banking, financial, and governmental sectors. However, their internal HR processes faced a pressing challenge. Despite having an extensive database filled with tens of thousands of potential talents, each new hiring demand meant starting from square one.

Vital insights from past applications, interviews, and recruiter notes lay buried in this expansive sea of data. The crux of the challenge was twofold: efficiently leveraging this underutilized reservoir of information and refraining from wasteful, redundant external hiring processes. In a competitive landscape, HMS sought to optimize their recruitment, ensuring they consistently onboarded top-tier talent.

Streamline the Talent Identification Process: HMS grappled with efficiently identifying top talent from their vast pool of candidates. Traditional recruitment methods proved time-consuming and often missed the mark. The company aimed to optimize this process to quickly and accurately identify the best fits.
Utilize the Wealth of Existing HR Data: HMS possessed a wealth of untapped recruitment data, from interview feedback to recruiter notes. This valuable information remained underutilized, and the company sought an effective way to leverage these insights in their hiring strategy.
Reduce Redundancy in Hiring Procedures and Conserve Resources: HMS's repetitive hiring procedures consumed time and resources without always delivering optimal results. They aimed to eradicate these inefficiencies and adopt a streamlined, more resourceful hiring approach.

→ The Solution

Integrated AI-Driven Analysis: Tapping into the vast reservoir of data at HMS, we deployed sophisticated AI techniques to scan and analyze existing resumes, interview summaries, and all prior communications about potential hires. By effectively "teaching" the system the nuances of recruitment, it became a repository of HMS's collective recruitment intelligence.

LinkedIn Profile Augmentation: Recognizing the importance of up-to-date employment histories, the system was engineered to complement its existing data by pulling pertinent details from candidates' LinkedIn profiles. This ensured that the database was not just large, but also current.

Contextual Sentiment Analysis & Predictive Scoring: Beyond mere data collection and aggregation, our AI algorithms utilized contextual sentiment analysis. This meant that interview summaries and recruiter notes were interpreted for more than just their content - the AI considered the sentiment and context, understanding nuanced feedback better. As a result, each candidate was provided with a dynamically-generated matching score, streamlining the decision-making process for human recruiters.

Efficient Candidate Prioritization: With all the data analyzed and scores generated, the system was then tasked with the final and most crucial step: presenting the human recruiters with a ranked list of the most suitable and relevant candidates. The emphasis was on quality over quantity, ensuring that recruiters were not overwhelmed but empowered.

→ Success

60% Reduction in Hiring Time: With AI handling data analysis, the time spent on hiring was nearly halved.

45% Improvement in Candidate Matching: AI-driven insights ensured a significant uptick in matching the right candidate to the right role.

Enhanced Recruiter Efficiency: No longer mired in manual search, recruiters can now dedicate more time to engaging with top-tier talent.

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