# BharteeAI: Revolutionizing Technical Hiring with AI-Powered Interviews

## Inspiration

In today's fast-paced tech world, finding the right talent efficiently is crucial. Traditional hiring processes are time-consuming and often biased. We created BharteeAI to revolutionize technical hiring, making it faster, fairer, and more effective.

## What it does

BharteeAI is an end-to-end AI-powered interview platform that:

* Screens resumes using advanced NLP and vector search
* Conducts real-time video interviews with an AI interviewer
* Assesses coding skills with an in-browser IDE
* Generates comprehensive candidate evaluations
* Provides a seamless experience for both employers and candidates

## How we built it

We leveraged cutting-edge technologies to create a robust, scalable solution:

* Frontend: Reflex (Python-based reactive framework)
* Backend: Reflex (FastAPI)
* Database: SingleStore for lightning-fast queries
* Vector Database: ChromaDB for efficient similarity search
* AI/ML: Groq for natural language processing
* Voice Processing: VAPI (Cartesia for TTS, Deepgram for STT)
* Video Integration: Agora
* Cloud Storage: AWS S3
* Deployment: DigitalOcean App Platform

## Challenges we ran into

* Integrating multiple AI services seamlessly
* Ensuring real-time performance for video and coding assessments
* Balancing AI automation with a human-like interview experience

## Accomplishments that we're proud of

* Created a fully automated yet personalized interview process
* Developed a scalable architecture capable of handling high-volume hiring
* Implemented bias-reduction techniques in candidate evaluation

## What we learned

* The intricacies of AI-human interaction in high-stakes scenarios
* Optimizing performance in distributed systems
* Balancing technical assessment with soft skills evaluation

## What's next for BharteeAI

* Expanding language support for global hiring
* Integrating more specialized technical assessments
* Developing an AI training module for interviewers

Join us in reshaping the future of technical hiring with BharteeAI!
## Inspiration

In today’s competitive job market, it's alarming to see MBA graduates from top schools struggling to secure employment, while many immigrants find themselves overlooked despite their qualifications. For these candidates, landing a job can often hinge on a single interview—the make-or-break opportunity that can determine their future.

Breaking into Product Management can feel like a formidable challenge without the right guidance. The role demands a deep, holistic understanding of business, collaboration across cross-functional teams, and a strong obsession on delivering an exceptional customer experience. There’s no one-size-fits-all formula to measure a candidate’s potential as a product manager, making the path even more intimidating for those without prior experience. Inspired by this gap, we’re driven to provide a solution—one where candidates can upload their resume and the job description of their desired role, and in return, receive a tailored interview experience that helps them effectively prepare, sharpen their skills, and build confidence for their actual interview.

## What it does

Our product allows customers of all backgrounds to interview for PM roles and receive feedback on their interview. We also perform an analysis on the interview transcripts and give feedback on the interview on a scale of 1 to 10.

## How we built it

* We used *Reflex* for both the front-end and back-end web development, enabling us to build a fully functional web application entirely in Python without needing to handle separate frameworks or languages for the front-end.
* We built a Full stack app that uses *vapi ai* to record and transcribe interviews.
* We further exploit the capabilities of vapi to generate questions and follow ups to ask the user using gpt 4.0 model
* We also use the gpt4.0 model and prompt engineer the vapi app to analyse the transcribed interviews and rate the users feedback based on our defined rubric.

## Challenges we ran into

* Zeroing in on a voice agent was very difficult as there were a lot of options and each model had their own pros and cons.
* Prompt engineering to get the right feedback from the voice agent was hard to perform.

## Accomplishments that we're proud of

* We were able to build an AI voice agent that can conduct product manager interviews, capable of providing target company based interviews.
* Gained exposure to voice agents and explored the prompt generation to enhance the accuracy of the ai model.

## What we learned

* We learnt about the extensive capabilities of vapi and voice agents in general.
* We learnt how to use AI agents to take interviews and give feedback to customers.
* Learnt the python reflex framework and used it develop the FE & BE.

## What's next for Prep.AI

* We plan on analysing the performance of the candidate in the interview and assigning a schedule that he/she can follow to improve his interview score.
* Introduce a progress tracker that updates iteratively based on the mock interviews taken by the candidates.
* Use Prompt engineering to further refine the evaluation rubric iteratively to generate more accurate and authentic reports.
* Take in the resume and job description of the candidate into the knowledge base creating context for each user that interacts with the AI voice agent, and tailor interview experiences.
## Inspiration

In the exciting world of hackathons, where innovation meets determination, **participants like ourselves often ask "Has my idea been done before?"** While originality is the cornerstone of innovation, there's a broader horizon to explore - the evolution of an existing concept. Through our AI-driven platform, hackers can gain insights into the uniqueness of their ideas. By identifying gaps or exploring similar projects' functionalities, participants can aim to refine, iterate, or even revolutionize existing concepts, ensuring that their projects truly stand out.

For **judges, the evaluation process is daunting.** With a multitude of projects to review in a short time frame, ensuring an impartial and comprehensive assessment can become extremely challenging. The introduction of an AI tool doesn't aim to replace the human element but rather to enhance it. By swiftly and objectively analyzing projects based on certain quantifiable metrics, judges can allocate more time to delve into the intricacies, stories, and the passion driving each team

## What it does

This project is a smart tool designed for hackathons. The tool measures the similarity and originality of new ideas against similar projects if any exist, we use web scraping and OpenAI to gather data and draw conclusions.

**For hackers:**

* **Idea Validation:** Before diving deep into development, participants can ascertain the uniqueness of their concept, ensuring they're genuinely breaking new ground.
* **Inspiration:** By observing similar projects, hackers can draw inspiration, identifying ways to enhance or diversify their own innovations.

**For judges:**

* **Objective Assessment:** By inputting a project's Devpost URL, judges can swiftly gauge its novelty, benefiting from AI-generated metrics that benchmark it against historical data.
* **Informed Decisions:** With insights on a project's originality at their fingertips, judges can make more balanced and data-backed evaluations, appreciating true innovation.

## How we built it

**Frontend:** Developed using React JS, our interface is user-friendly, allowing for easy input of ideas or Devpost URLs.

**Web Scraper:** Upon input, our web scraper dives into the content, extracting essential information that aids in generating objective metrics.

**Keyword Extraction with ChatGPT:** OpenAI's ChatGPT is used to detect keywords from the Devpost project descriptions, which are used to capture project's essence.

**Project Similarity Search:** Using the extracted keywords, we query Devpost for similar projects. It provides us with a curated list based on project relevance.

**Comparison & Analysis:** Each incoming project is meticulously compared with the list of similar ones. This analysis is multi-faceted, examining the number of similar projects and the depth of their similarities.

**Result Compilation:** Post-analysis, we present users with an 'originality score' alongside explanations for the determined metrics, keeping transparency.

**Output Display:** All insights and metrics are neatly organized and presented on our frontend website for easy consumption.

## Challenges we ran into

**Metric Prioritization:** Given the timeline restricted nature of a hackathon, one of the first challenges was deciding which metrics to prioritize. Striking the balance between finding meaningful data points that were both thorough and feasible to attain were crucial.

**Algorithmic Efficiency:** We struggled with concerns over time complexity, especially with potential recursive scenarios. Optimizing our algorithms, prompt engineering, and simplifying architecture was the solution.

*Finding a good spot to sleep.*

## Accomplishments that we're proud of

We took immense pride in developing a solution directly tailored for an environment we're deeply immersed in. By crafting a tool for hackathons, while participating in one, we felt showcases our commitment to enhancing such events. Furthermore, not only did we conceptualize and execute the project, but we also established a robust framework and thoughtfully designed architecture from scratch.

Another general Accomplishment was our team's synergy. We made efforts to ensure alignment, and dedicated time to collectively invest in and champion the idea, ensuring everyone was on the same page and were equally excited and comfortable with the idea. This unified vision and collaboration were instrumental in bringing HackAnalyzer to life.

## What we learned

We delved into the intricacies of full-stack development, gathering hands-on experience with databases, backend and frontend development, as well as the integration of AI. Navigating through API calls and using web scraping were also some key takeaways. Prompt Engineering taught us to meticulously balance the trade-offs when leveraging AI, especially when juggling cost, time, and efficiency considerations.

## What's next for HackAnalyzer

We aim to amplify the metrics derived from the Devpost data while enhancing the search function's efficiency. Our secondary and long-term objective is to transition the application to a mobile platform. By enabling students to generate a QR code, judges can swiftly access HackAnalyzer data, ensuring a more streamlined and effective evaluation process.