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ResumePilot is a small AI job-application copilot built to compare one resume PDF with a specific job description. It organizes the match into dimensions such as skills, experience, projects, education, and keyword coverage, then suggests areas to clarify or investigate. Its creator, Srijita Ghosh, describes it as a way to help a friend stop repeating that manual comparison—not as a hiring predictor or a substitute for honest judgment.
Why ResumePilot was built
Ghosh says a friend applying for internships kept comparing the same resume with different job descriptions. For each role, the candidate had to identify relevant experience, matching skills, missing requirements, and ways to improve the application. ResumePilot puts those checks into one workflow: provide a resume PDF and a job description, analyze their fit, and review the results.
The underlying question is practical: which parts of an existing background matter for this particular role, and where does the resume fail to make that connection clear?
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What the analysis is designed to show
Rather than returning only a compatibility score, the prototype presents several kinds of information about the resume and listing:
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- Overall compatibility, alongside separate matches for skills, experience, projects, and education.
- Strong matches that connect the candidate’s background to the role.
- Missing skills and weak or underrepresented areas.
- Keyword coverage grouped as “Matched,” “Missing,” or “Needs more context.”
- Actionable recommendations that explain why a point may matter for the application.
The keyword categories are meant to help a candidate spot evidence that may be absent or unclear—not to encourage inserting terms without support. A missing keyword can be a prompt to check whether the candidate has relevant experience to describe. It is not evidence that they possess the skill.
How to use the results without overstating your experience
Ghosh’s stated principle is: “Improve the story your experience already tells. Don’t invent experience you don’t have.” A recommendation should help a candidate explain a real project, responsibility, or skill more clearly. It should not turn an unsupported suggestion into a claim on a resume.
The prototype can flag suggestions that need confirmation with “Verify before adding.” Treat that as a cue to check the underlying facts. If there is no experience to substantiate a listed skill, leave it off rather than adding it simply to improve a match.
What the compatibility score does—and does not—establish
The project description establishes that ResumePilot has a compatibility score feature, but it does not provide a benchmark, controlled evaluation, or validation study showing how accurate the score is. It should therefore be read as one organizing signal within the tool, not as a verified measure of qualification.
Nothing in the project description establishes that the score is an official applicant-tracking-system score, predicts interviews, or predicts hiring outcomes. The more useful reading is the surrounding breakdown: it can help direct attention to relevant evidence and potential gaps, while the candidate decides what is accurate and worth changing.
How the described prototype works
According to Ghosh’s project description, the frontend uses React, Vite, and Tailwind CSS, while the backend uses Python and FastAPI. The described processing path is:
- The user provides a resume PDF and a job description through the frontend.
- The FastAPI backend extracts text from the PDF with pypdf and constructs a prompt.
- The prompt is sent to Gemma through the hosted Gemini API, accessed with google-genai.
- The backend extracts structured JSON, validates model output with Pydantic, processes keywords and scores, and returns results to the frontend.
Ghosh describes the project as not using retrieval-augmented generation (RAG): it has no embeddings, vector database, or retrieval stage. These are details from the author’s description, not the results of an independent code review or performance test.
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What happens to resume data
The author says uploaded resumes are not stored on disk or in a database. However, the prototype sends extracted resume text and the job description to a hosted API provider for inference; the described setup uses Gemma through the hosted Gemini API rather than local inference.
The project description does not establish the provider’s retention policy, security controls, or how long data may be processed. So “not stored by the app on disk or in a database” should not be read as “never shared” or “fully private.” Anyone considering a real resume should review the service’s current data terms and avoid submitting personal information they do not want sent to a hosted service.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Availability and limits of what is known
The original project post links to a demo and a GitHub repository, but their current availability, maintenance status, and pricing are not established here. The post also does not demonstrate that using ResumePilot improves application outcomes or saves a measured amount of time. Its supported description is narrower: a prototype that structures the comparison between a resume and a particular job description, with suggested next steps.
There is also another project called ResumePilot on Devpost. It is a separate project and should not be confused with Ghosh’s internship-application copilot.
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