Inspiration

Online recruitment has made finding opportunities easier than ever, but it has also made job scams more convincing. Fraudulent recruiters often impersonate legitimate companies, request registration or processing fees, create artificial urgency, and exploit the trust of job seekers.

I wanted to build a solution that could help users verify job offers before sharing personal information or making financial decisions. The goal behind HireWise was simple: provide an accessible tool that helps people recognize potential recruitment scams within seconds.


What it does

HireWise is a machine learning-based web application that analyzes job offers, recruitment emails, and hiring messages to identify potential scams.

Users simply paste the content into the application, and HireWise generates:

  • Safe / Suspicious / High Risk verdict
  • Confidence score
  • Email and domain extraction
  • Scam red flag detection
  • Risk summary
  • AI-assisted explanation
  • Recruiter verification message

Instead of only predicting whether an offer is suspicious, HireWise explains why it reached that conclusion, helping users make informed decisions before responding to recruiters or sharing personal information.


How we built it

HireWise was developed as a full-stack web application.

The frontend was built using React.js, while the backend uses Node.js and Express.js to communicate with a Python-based Machine Learning model.

The Machine Learning model analyzes recruitment content and works alongside a rule-based detection system that identifies common scam indicators such as:

  • Registration or processing fees
  • Urgent deadlines
  • Suspicious email domains
  • Pressure tactics
  • Unrealistic recruitment language

The project is deployed using Vercel for the frontend and Render for the backend, making the application accessible online.


Challenges we ran into

One of the biggest challenges was improving prediction quality without relying only on keyword matching. Legitimate job offers can sometimes contain similar language to fraudulent ones, so simply detecting words was not enough.

To improve reliability, I combined Machine Learning predictions with rule-based scam detection, allowing the system to evaluate multiple indicators before producing a verdict.

Another challenge was presenting technical results in a way that everyday users could easily understand. Instead of showing only a prediction, HireWise provides clear explanations, highlighted red flags, and a verification message that users can immediately use.


Accomplishments that we're proud of

  • Successfully trained and integrated a Machine Learning model into a full-stack web application.
  • Built a complete end-to-end workflow from job offer analysis to risk assessment.
  • Designed a clean and intuitive user interface focused on usability.
  • Deployed the application using Vercel and Render for public access.
  • Created a solution that addresses a real-world cybersecurity and recruitment problem.

What we learned

Building HireWise strengthened my understanding of Machine Learning workflows, backend integration, REST APIs, full-stack deployment, and designing applications with the end user in mind.

More importantly, I learned that technology becomes far more valuable when it provides transparency alongside predictions. Helping users understand why a decision was made builds confidence and trust in the system.


Future Improvements

HireWise has several opportunities for future expansion, including:

  • Company database verification
  • Browser extension for detecting scams directly from emails
  • Resume and recruiter authenticity verification
  • Improved Machine Learning models trained on larger datasets
  • Mobile application support
  • Real-time scam intelligence and reporting

Our long-term vision is to make HireWise a trusted companion for every job seeker, helping people verify opportunities before becoming victims of recruitment fraud.

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