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[Audio] The company has carried out extensive customer and user research to validate its approach. The results show that business owners from various sectors are willing to use the company's solution, as long as it uses simple language. All participants expressed their intention to use the product, but only if it used clear and concise language. This finding has been confirmed by thorough market research, which demonstrates that the company's language model layer is effective in addressing this need. By using real-world data and expert opinions, the company has developed a solution that meets the requirements of its target audience..
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[Audio] Our system relies heavily on a combination of technologies to function effectively. The tools and frameworks used for both frontend and backend functionalities are crucial to its operation. We utilize React.js and Next.js for the frontend, along with Tailwind CSS and Recharts for visualization purposes. In the backend, we employ Python and FastAPI for building scalable applications. Moreover, we utilize Celery for managing background tasks. The AI and machine learning aspects of our system are also integral to its functioning. We rely on scikit-learn and LangChain/LlamaIndex for natural language processing and knowledge graph construction. Furthermore, we integrate OpenAI and Anthropic APIs for advanced NLP capabilities. For storing and retrieving large datasets, we leverage Vector DB and ChromaDB/Pinecone. This enables us to manage and process vast amounts of data efficiently. We also utilize PostgreSQL as our primary database management tool. This allows us to store and retrieve data in an organized manner. External APIs play a significant role in our system's functionality. We tap into NewsAPI, World Bank API, and FRED for real-time economic data, which is then fed into our risk scoring model. These APIs provide us with critical information that informs our predictions. To ensure seamless communication between systems, we utilize SendGrid for email notifications and Twilio for WhatsApp integration. Finally, we deploy our application using Docker and Railway/Render for efficient project management. This enables us to streamline our workflow and operate our system more efficiently..