Key takeaways
- CutShort is an AI-powered recruitment platform streamlining the hiring process for companies.
- The startup utilises machine learning to analyse candidate profiles and skills effectively.
- A unique matching engine assesses candidates' compatibility with job profiles based on various criteria.
- CutShort aims to enhance engagement between employers and relevant candidates swiftly.

- Machine learning to analyse candidate data and understand their profile, skill set, key profile traits, interests
- A matching engine that calculates a match score of the candidates with the jobs based on the job profile, key skills (including their related skills), desired industry/educational background, proficiencies and hard to know profile traits such as “strong aptitude”, “interest in problem-solving”
- Natural Language Processing (NLP) and Natural Language Processing (NLU) to help companies engage with the candidates faster
The Big Idea
As Verma shares, they saw big inefficiencies in the recruitment space both from the candidate and company sides. “Our first hypothesis was to create a peer to peer referral platform to solve the discovery problem here. We started getting users, but soon we realised that the bigger pain point is getting relevant candidates,” says Verma. This is how CutShort started, which actually began with simple algorithms to test key hypotheses and gradually started using more of AI-driven approach. The founding team has the right balance of experience and creativity. While Verma leads the Product and Marketing with his 15 years of experience and an MBA in Marketing from XLRI Jamshedpur, Anubhav Gupta and Priyank Agrawal lead the design and technology and growth areas with their 3 years of experience in their previous startups.How Does The Match-Making Algorithm Work?
CutShort uses a lot of machine learning and deep learning techniques. As Verma explains:- The foundation of the matching algorithm is identifying attributes (“features”) of candidates based on their profile info, resume, social profiles, code repos and so on. Candidates get scores on different features such as key skills (including their related skills), desired industry/educational background, proficiencies and hard to know profile traits such as “strong aptitude”, “interest in problem-solving” and so on.
- Next part of the algorithm is to suggest the features a recruiter will find relevant. Based on a rich data layer of the universe of companies, the algorithm suggests the features and the weightages assigned to those features. Some of these weightages are also customisable by the recruiters themselves
- The final part of the algorithm is to suggest the matches and learn from the actions to change the features and their desired weightages




