Apply.ai

Apply.ai generates intelligent job applications which evaluate candidates' portfolio, technical skills, and cultural fit in addition to their resume to make the hiring process more equitable.

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Apply.ai: A Unique Take on the Education Track

Knowledge is the same whether learned off of Youtube for free or in a college lecture hall. Young adults who choose not to go to college should not have their job applications filtered out because they acquired the knowledge and skills for the position without going into debt and spending 4+ years sitting in a classroom, yet 70 million+ young Americans do (PRNewswire). Apply.ai is the solution.

Inspiration

As the cost of college soars, young adults are turning to online courses, bootcamps, and other alternatives to prepare themselves for rewarding and secure careers. Unfortunately, many of individuals struggle to get jobs due to not making it through online screening platforms' college degree requirements. Employers are willing to higher people with alternative training once they meet them and see their portfolios, however interviews only occur after these automated screenings.

The solution

Apply.ai allows employers to get a more holistic view of applicants. In addition to their resume, applicants submit a link to their portfolio and responses to questions designed to assess their technical skills and cultural fit with the company they are applying for. AI analyzes and condenses these responses into a rich dashboard HR people can use in choosing who to move forward in the application process.

How I built it

I began by prototyping features I wanted to include in the program--GitHub repo scraping and analysis, resume questioning, and technical question generation and evaluation--in a Colab notebook. I then went around to each of the sponsors and asked them about the HR software used in their companies, discussed the features I planned to implement with them, and asked what they'd like to see added/changed. I took this feedback and applied it to building the final hack in Streamlit.

Challenges I ran into

A significant portion of the proof of concept code I created in Google Colab used the Langchain library. I was unable to get the library to function locally in a timely manner due to dependent package version conflicts and had to simplify the program's functionality to what could be achieved using OpenAI's base library.

Accomplishments that I'm proud of

This was my first hackathon. I'm really proud of the idea and think it has the potential to help a lot of people with educational backgrounds other than college not get overlooked in the hiring process. I enjoyed having such direct access to recruiters to discuss the platform with and being able to iterate on their feedback rapidly.

What I learned

This was my first time using LLMs in a project. I'm very impressed by their capabilities and am excited to push them further in future applications.

What's next for Apply.ai

I plan to turn this into a full web application this summer and see if I can get some companies to use it. Whether through this project or something else, I am passionate about ending discrimination based on education in the hiring process.

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