An AI-Powered Resume Analysis Suite

Authors

  • Waseem Ahmed Department of CSE-AIML, ABES Engineering College, Ghaziabad, India Author
  • Manish Rajora Department of CSE-AIML, ABES Engineering College, Ghaziabad, India Author
  • Hemant Kumar Department of CSE-AIML, ABES Engineering College, Ghaziabad, India Author
  • Naman Negi Department of CSE-AIML, ABES Engineering College, Ghaziabad, India Author

DOI:

https://doi.org/10.63503/acset.116

Keywords:

Artificial Intelligence, Resume Analysis, ATS, NLP, LangChain, LangGraph, Streamlit, AWS Bedrock, Skill Gap, Generative AI and Analysis

Abstract

DeepMatch is a resume analysis application built with AI that enables Applicant Tracking Systems (ATS) to converse with job applicants. It has an accessible, open resume comparison forum that presents resumes side by side, as an appealing job study. particular job descriptions. Fire at your resume through higher-level NLP and AI systems, including NLTK, Spacy and Lang chain, the LangGraph - DeepMatch retrieves, formats, and dissects the text of your resume so that it can locate the key requirements that are absent, calculate your own ATS compatibility measurements and offer suggestions for personal aspects of skill building. DeepMatch will give you personalised assistance on which route to upgrade in your occupation, how to improve your oeuvre before publishing it, and on discovering ability. When deployed on Streamlit Cloud, ensure it is available and functions efficiently. DeepMatch assists the candidate more closely towards their desires as the AI input to the idea of the candidate is combined with the precision of NLP, meeting the company requirements to better quality results, where the resumes can be more easily comprehended, and the procedures of automated recruitment are clearer. 

References

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Published

2026-09-15

Conference Proceedings Volume

Section

Articles

How to Cite

Waseem Ahmed, Manish Rajora, Hemant Kumar, & Naman Negi. (2026). An AI-Powered Resume Analysis Suite . Adroid Conference Series: Engineering and Technology, 2(3), 118-124. https://doi.org/10.63503/acset.116