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Landing Page
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Admin Dashboard
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Admin adding Companies that subscribes to the platform
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Company creates the Interview and the Rounds Available
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The Rounds Set by HR
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Question that are generated by AI for aptitude and can also generate for coding rounds too
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We can respond through speech and not only during verification the camera is on but for all rounds both the camera and mic is on .
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For Reference when verification failed due to wrong person attending the interview
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Candidates must Verify themselves by uploading the ID proof and their onspot image and AI compares the face and name in ID and actual name
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After both Face and Name verifies then only he can proceed to the interview that is scheduled
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Tech Interview will be conducted by AI it asks questions based on Resume and the role they are taking
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When Multiple Faces detected there comes a warning. If the warning exceeds 3 then they are disqualified.
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When you exit the full screen there comes a warning even if you switch tabs there will be warning
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HR's can view the response
Inspiration
Hiring processes are often time-consuming and inconsistent. Recruiters must screen many candidates, conduct multiple interview rounds, and manually evaluate responses. This can introduce delays and potential bias in decision making. We wanted to build a system that could automate early-stage interviews while maintaining structured evaluation and fairness.
What it does
HireMind is a multi-modal AI interview agent that automates candidate screening through multiple rounds including aptitude tests, group discussions, coding assessments, AI-driven HR and technical interviews, and final live human interviews, while generating structured and unbiased evaluations to help recruiters make scalable hiring decisions.
How we built it HireMind is built as an AI-driven interview system where Gemini serves as the core LLM backbone for generating interview questions, analyzing candidate responses, and producing structured evaluations. The platform supports multiple interview rounds including aptitude tests, group discussions, coding assessments, AI HR interviews, AI technical interviews, and final live human interviews.
The frontend is built using Next.js to provide an interactive interview interface for candidates. The backend is developed with FastAPI, which manages interview workflows, processes candidate responses, and communicates with the AI model. We used PostgreSQL to store candidate information, interview responses, and evaluation results.
Key libraries and tools used in the system include SQLAlchemy for database operations, Pydantic for data validation, and Uvicorn to run the backend server. Together, these technologies enable the system to dynamically adapt interview questions, analyze responses, and generate structured scoring reports for recruiters.
Challenges we ran into
One challenge was designing a system that could dynamically adapt interview questions while maintaining structured evaluation criteria. We also faced challenges integrating multiple services across cloud platforms and ensuring smooth communication between the frontend, backend, and AI components.
Accomplishments that we're proud of
We built a working AI-powered interview platform that automates multiple stages of the hiring process. HireMind can conduct several interview rounds including aptitude tests, group discussions, coding assessments, AI HR interviews, AI technical interviews, and even support a final live human interview. We successfully integrated Gemini as the core LLM to dynamically generate interview questions, analyze candidate responses, and produce structured evaluation reports that help recruiters screen candidates more efficiently.
What we learned
During the development of HireMind, we learned how to design AI-driven workflows that manage different interview stages and interact effectively with large language models. We also gained experience building full-stack systems, integrating AI for dynamic analysis, and designing structured evaluation pipelines that simulate real-world hiring processes.
What's next for HireMind AI
Next, we plan to add an intelligent resume shortlisting stage before the interview pipeline begins. The system will automatically filter candidates based on criteria such as CGPA, required tech stacks, number of internships, and relevant skills, allowing recruiters to quickly identify suitable candidates before proceeding to the interview rounds.
Built With
- axios
- cloud
- fastapi
- gemini-api-(llm-backbone)
- jwt-authentication
- platform
- postgresql
- python
- react-(next.js)
- sqlalchemy
- tailwindcss
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