Scrutiny-AI
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Overview:
Inspiration
In today’s competitive job market, identifying the right talent efficiently is a significant challenge for companies. Traditional interview processes often lack objectivity and can be time-consuming, especially for roles requiring technical expertise. We wanted to build a tool that would streamline and improve the hiring process by combining the power of AI, coding evaluation environments, and scoring analytics, helping companies make more informed hiring decisions.
What it does
Scrutiny is an AI-aided interview platform that evaluates candidates’ technical skills through an interactive online coding environment. The platform allows hiring teams to assess candidates in real-time with AI-driven scoring and evaluation metrics, ensuring an objective and consistent assessment process. Candidates can complete coding challenges, answer interview questions, and receive a score based on their performance, providing recruiters with an efficient way to gauge skills accurately and quickly.
How we built it
Scrutiny was built using Next.js for its fast and flexible front-end capabilities, and TypeScript to ensure a type-safe development experience. We utilized Supabase as our primary database and authentication solution, allowing us to handle data storage and user management seamlessly. The interview scoring and evaluation components were powered by Go, which enabled high-performance data processing. The AI components are integrated to analyze responses, code submissions, and interaction patterns, providing insights into each candidate’s strengths and potential areas of improvement.
Challenges we ran into
One of the main challenges we faced was developing a fair and effective scoring algorithm that accurately reflects candidates’ skills while minimizing potential biases. We also encountered some integration hurdles while connecting the various parts of our tech stack, especially around real-time data handling in the coding environment. Ensuring that the AI-driven assessments were reliable and consistent across different use cases was another significant challenge.
Accomplishments that we’re proud of
We are proud of creating a cohesive and functional platform that simplifies the interview process for both candidates and recruiters. Our scoring system is robust and efficient, and the real-time coding environment integrates smoothly with the overall platform. Additionally, we are proud of the AI implementation, which provides a reliable layer of analysis, helping to reduce subjectivity in the hiring process.
What we learned
Throughout this project, we learned a lot about AI-driven assessment methods and the importance of building fair evaluation tools. We also gained insights into the complexities of handling real-time data within a coding environment and the importance of ensuring seamless integration across different services in a full-stack application. Working with Supabase, Next.js, TypeScript, and Go allowed us to explore new capabilities and appreciate the versatility and power of each technology.
What’s next for Scrutiny
In the future, we aim to improve Scrutiny’s AI capabilities by implementing more advanced natural language processing (NLP) for non-technical interview questions. We also plan to add support for more programming languages and offer customizable interview workflows for different roles and industries. Additionally, expanding our analytics and reporting features will help companies gain deeper insights into their hiring pipelines.