Students and young professionals often struggle to plan their career paths effectively — not because of a lack of ambition, but due to a lack of structured, personalized guidance. We were inspired to build LaunchPad.AI to give every learner an intelligent, always-available AI career coach that helps them navigate from dream to job offer.

LaunchPad.AI is an autonomous AI career copilot built entirely on AWS.
It helps users plan, prepare, and apply for jobs through reasoning, learning, and automation.

Key features:

• Skill & Goal Analysis: Uses Amazon SageMaker to assess user strengths, interests, and gaps.
• Career Path Discovery: Employs Bedrock LLMs (Claude / Nova) to suggest tailored roles and industries.
• Learning Recommendations: Maps skill gaps to online courses and learning pathways.
• Autonomous Document Creation: Generates personalized resumes, cover letters, and LinkedIn summaries.

LaunchPad.AI qualifies as an AWS-defined AI Agent because it:

1. Uses reasoning LLMs (Bedrock Claude/Nova) to interpret user goals, reason about next steps, and make decisions.
2. Autonomously performs multi-step tasks — fetching data, generating content, and scheduling actions — without human input.
3. Maintains context and user memory through DynamoDB and S3 for persistent learning.
4. Employs Bedrock AgentCore primitives for planning, execution, and tool-calling — the hallmark of an AWS agentic system.

• Amazon Bedrock (Claude / Nova) — Reasoning LLM for planning and decision-making
• Amazon SageMaker — Custom ML models for skill-gap detection
• AWS Lambda + API Gateway — Task orchestration and automation
• Amazon Bedrock AgentCore — Agentic task execution
• Amazon DynamoDB / S3 — Memory and persistent data storage
• Gradio — Interactive web-based frontend
• Python (Boto3, LangChain) — Backend integrations

User → Gradio Frontend → API Gateway → Lambda
     → Bedrock AgentCore ↔️ SageMaker (Skill Analysis)
     ↔️ DynamoDB/S3 (Memory)
     ↔️ External APIs (Job Boards, Learning Platforms)

• Python 3.8 or higher
• AWS Account with appropriate permissions
• AWS CLI configured (optional but recommended)

1. Clone the repository

git clone <repository-url>
cd launchpad.AI

2. Create and activate virtual environment

# Create virtual environment and install dependencies
python3 create_venv.py

# Activate the virtual environment
# On macOS/Linux:
source venv/bin/activate
# OR run the convenience script:
./activate_venv.sh

# On Windows:
venv\Scripts\activate.bat
# OR double-click:
activate_venv.bat

3. Configure AWS credentials

   • Option 1: AWS CLI

     aws configure

   • Option 2: Environment variables in .env file

     cp .env.example .env
     # Edit .env with your credentials

4. Deploy AWS infrastructure

python deploy.py

5. Start the application

python run.py

6. Open your browser
   • Navigate to http://localhost:7860
   • Start using your AI Career Copilot!