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Project: Loka.ai

Members: Quan Nguyen, Duc Nguyen, Long Bui

I. Motivations

An innovative platform to identify ideal locations for food & beverage enterprises, focusing on customer demographics, potential markets, costs, and competition. This approach addresses the needs of the F&B industry, where 97% are firms facing operational and economic challenges. With the U.S. packaged food market projected to reach $1.6 trillion by 2030, optimizing location decisions is crucial for success

II. Milestones & Challenges

System Diagram

  • Aggregate data by leveraging APIs from multiple sources" (GoogleMapAPI, Census Bureau, NY Open Data)
  • Integrate and clean pulled data into MongoDB
  • Develop a responsive web interface using React
  • Enhance server-side performance by implementing Flask
  • Develop a machine learning model that captures essential parameters using Cerebas API to rank most optimized locations
  • Leverage PropelAuthority to authorize user management
  • Design, reorganize, and visualize metrics into meaningful and understandable insights

III. Takeaways

Pros

  • Operational and Functional: The app is fully operational, allowing users to access its features seamlessly, enabling immediate utilization for business enhancement.

  • Showcases Essential Capabilities: It effectively demonstrates the necessary skills and functionalities, providing valuable insights for optimizing processes and decision-making.

  • Room for Improvements: The project shows solid foundation that could potentially develop new features and continous improvability in the future.

Cons

  • Potential Model Biases: Some biases have been identified in the model, which may affect accuracy. Further testing and fine-tuning are needed to improve reliability.

  • Capacity Limitations: The app currently has restrictions on handling larger datasets and accommodating more users, necessitating enhancements for scalability.

  • Data Dependencies: Fetching data from different sources and does not have inernal data may lead to disrupt the system if sources collasped.

IV. Future Enhancements

Model Improvements:

  1. Market Equilibrium Adjustment: Implement dynamic Supply & Demand balancing algorithms to reflect real-time market conditions.
  2. Logistics Analysis Implementation: Incorporate advanced logistics modeling to optimize supply chain operations and reduce costs.
  3. Data Backup Sources: Utilize redundant cloud storage, regular snapshots, and blockchain technology for secure and reliable data backups.
  4. Features Added: Introduce machine learning price predictions, real-time sentiment analysis, customizable alerts, and interactive visualizations.

Platform Integrations:

  1. Market Data Providers: Establish connections with providers like Bloomberg for comprehensive market data feeds.
  2. Integration with CRM Systems: Connect with platforms like Microsoft Business Central (launched in 2021 to serve small business's data solutions) to retreive more accurate financial data.