AI-Driven Legal Chatbot for Real Estate

Philer Inc is seeking to develop an AI-driven legal chatbot designed specifically for real estate professionals. The goal of this project is to create a chatbot that can provide instant legal guidance, helping real estate agents navigate the complexities of property transactions. This project will allow learners to apply their knowledge of artificial intelligence, natural language processing, and legal frameworks within the real estate sector. The chatbot should be capable of understanding common legal queries and providing accurate, concise responses. The project will involve designing the chatbot's conversational flow, integrating AI models, and ensuring compliance with relevant legal standards. By the end of the project, learners will have gained hands-on experience in developing AI applications tailored to industry-specific needs.

Real Estate Legal Chatbot Enhancement

Philer Inc. aims to enhance its Retrieval-Augmented Generation (RAG) chatbot to deliver personalized guidance for real estate transactions . While the current chatbot provides general information, this project focuses on refining its capabilities to offer relevant information based on user-specific scenarios . This initiative will involve integrating data sources to improve accuracy, enhancing natural language processing (NLP) to handle queries, and expanding coverage to include various real estate transaction types, such as buying, selling, and refinancing . Additionally, the chatbot will be designed to ensure compliance with legal standards, making it a reliable and valuable tool for users navigating real estate transactions. The project will analyze existing chatbot functionalities, identify gaps, and implement improvements to enhance response accuracy and relevance. Testing will be conducted to ensure the chatbot performs effectively in real-world scenarios .

Artificial Intelligence & Machine Learning Application

The central aim of this project is to conceptualize, design, and create a sophisticated and specialized Chatbot, uniquely customized to cater specifically to the multifaceted realm of Real Estate Law. This Chatbot aims to serve as a comprehensive and intelligent virtual assistant, proficient in offering precise and reliable information, as well as valuable guidance concerning the intricate legal intricacies inherent in various real estate transactions. The Chatbot will encompass a wide array of functionalities, capable of addressing inquiries regarding property transfers, contractual obligations, legal rights and obligations of buyers and sellers, tenancy laws, regulatory compliance, and other pertinent facets associated with real estate transactions. By offering accessible, timely, and precise legal insights, chatbot aims to streamline communication and empower clients, real estate professionals, buyers, sellers, and stakeholders involved in property dealings to make informed decisions and navigate through legal complexities seamlessly.

Node.js project for system integration

The overarching aim of this project is to streamline and facilitate the seamless transmission of Client Intake data collected through Philer's tool to LawLabs, a Conveyancing software, using Puppeteer—a headless browser automation tool. This integration seeks to enhance efficiency and accuracy in data transfer between these platforms, ensuring a smooth workflow for legal processes. To achieve this objective, we are eager to engage and collaborate with students, providing them with an opportunity to contribute to the development of this integration. The project involves several key steps that participating students will undertake: Getting Acquainted with Philer's Client Intake Forms: Students will familiarize themselves with the structure, layout, and data fields within Philer's Client Intake forms. This step is crucial for understanding the data that needs to be transmitted. Understanding the LawLabs Platform: It's essential for students to explore and gain a comprehensive understanding of the functionalities and data handling mechanisms within the LawLabs platform. This familiarity will aid in mapping data fields for seamless integration. Mapping Fields between Philer and LawLabs: Students will be tasked with identifying corresponding fields between Philer's Client Intake forms and LawLabs. This step involves aligning and matching data fields to ensure accurate data transfer. Utilizing Puppeteer for Data Transfer: The key technical aspect involves utilizing Puppeteer, a Node.js library, to automate the transfer of Client Intake data from Philer to LawLabs. Students will leverage Puppeteer's functionalities to enable the smooth transfer of data.