This paper presents a Relative Scoring-Based Profile Analyser designed to automate and improve resume screening using advanced Natural Language Processing (NLP) techniques. The system addresses key challenges in recruitment by combining keyword matching, semantic similarity, and candidate comparison through relative scoring. Leveraging tools such as Named Entity Recognition (NER), TF-IDF vectorisation, and Word2Vec embeddings, it accurately extracts and analyses candidate qualifications. What sets this system apart is its ability to evaluate applicants not just in isolation, but about their peers-closely mirroring how recruiters assess candidates in real scenarios. Visual feedback through skill-gap analysis and score breakdowns enhances transparency and usability for both job seekers and hiring teams. Experimental results demonstrate the model’s robustness across diverse resume formats, achieving 92% accuracy in profile matching while outperforming baseline models in speed and interpretability. By offering contextual insights rather than static rankings, this approach enhances the fairness, scalability, and quality of decision-making in recruitment pipelines.