CareerPilot AI

Inspiration

Career guidance is often scattered across different websites, making it difficult for students to find quick and reliable answers. We wanted to build an AI assistant that students could use directly inside Slack to ask career-related questions, learn new technologies, and receive guidance without leaving their workspace.

What it does

CareerPilot AI is a Slack-based AI assistant that helps students and beginners with career guidance. Users can ask questions such as:

What is Full Stack Development? Explain Python. Suggest an AI Engineer roadmap. Recommend learning resources. Explain programming concepts.

The bot instantly replies inside Slack with clear and structured answers.

How we built it

The project was developed using:

Python Slack Bolt SDK Slack Socket Mode Ollama with Llama 3.2 (local AI model) Visual Studio Code python-dotenv

The Slack bot listens for mentions in a Slack channel. When a user asks a question, the bot sends the prompt to the locally running Llama 3.2 model through Ollama and returns the generated response back to Slack.

Challenges we ran into

During development, we encountered several challenges:

OpenAI API quota exceeded errors. Invalid Gemini API key issues. Slack event handling and Socket Mode configuration. Setting up Ollama and integrating the local Llama model. Debugging Python and Slack SDK errors.

Switching to a local AI model with Ollama allowed us to avoid API limitations and continue development successfully.

Accomplishments that we're proud of Successfully built a working AI assistant inside Slack. Integrated Slack with a local Large Language Model. Eliminated dependency on paid cloud APIs. Built an AI chatbot capable of answering career-related questions in real time. What we learned

Through this project we learned:

Slack App development Slack Bolt framework Event-driven programming Python backend development Local AI deployment using Ollama Prompt engineering API integration and debugging What's next for CareerPilot AI

Future improvements include:

Resume analysis and feedback. Mock interview preparation. Personalized career recommendations. Learning roadmap generation. Job and internship recommendations. Integration with LinkedIn and GitHub. Voice interaction. Multi-language support. Student progress tracking dashboard.

Built With

• 3.2
• ai
• api
• bolt
• code
• git
• language
• learning
• llama
• machine
• mode
• natural
• ollama
• processing
• python
• python-dotenv
• slack
• socket
• studio
• visual