Scalable Cloud Backend Implementation for Lexata Inc.

Lexata Inc. aims to enhance the scalability of its backend infrastructure by leveraging sophisticated cloud computing databases and other backend technologies. The project involves assessing the current backend setup and determining whether to use AWS or Azure for cloud services. The team will need to explain the chosen technologies to the CEO, including hosting solutions for the website and databases for various datasets. Additionally, the project will assess the feasibility of using a vector database for embeddings. The final implementation will involve writing Python code in a GitHub repository, which will be integrated with the chosen cloud infrastructure. This project provides an opportunity for learners to apply their knowledge of cloud computing, database management, and backend development. Key tasks include: - Assessing Lexata's current backend infrastructure. - Evaluating AWS and Azure for suitability. - Explaining chosen technologies to the CEO. - Implementing databases, including potential vector databases. - Writing and integrating Python code in a GitHub repository.

Cloud for Lexata

The project is to make Lexata's back end more scaleable by implementing sophisticated cloud computing databases and other back end technology. Also link cloud solution to github for running front end code. Implement databases in mongo db or other system including vector embeddings.

Data mine for legal texts, create smart database and use GPT-3 for search.

Lexata's MVP is almost ready for commericialization and we also just acquired a GPT-3 license, so we are at a pivotal point in our start-up development. Lexata's Riipen project involves expanding Lexata's MVP database of laws and working on the semantic search capabilities, including by experimenting with GPT-3. GPT-3 has been trained generally on the internet's body of text; whereas Lexata's use case involves a specialized legal topic. We want to test the speed and efficacy of training GPT-3 in Lexata's specialized domain. If we are sucessful in establishing a use case for GPT-3, it could exponentially speed up Lexata's product development and establish Lexata as a market leader in intelligent legal research. Although Lexata's topic domain is currently limited to capital markets regulation, our methodology has significant implications for access to justice more generally, because of the potential for users to ask any legal question in plain language and obtain not just legalese results, but plain language answers that non-experts can comprehend.