Create an AI-based compliance analysis system for corporate documents

Software Configuration Recommendations: Python: Python is a popular programming language that has a variety of libraries and frameworks that are useful for natural language processing and machine learning tasks. We will be using Python as the primary programming language for this project. spaCy: spaCy is an open-source NLP library that provides pre-trained models and tools for performing various NLP tasks, such as entity recognition and dependency parsing. scikit-learn: scikit-learn is a machine learning library that provides various algorithms and tools for building and evaluating machine learning models. Flask: Flask is a lightweight web framework that will be used to create a simple web interface for uploading and analyzing corporate documents. Project Description: The goal of this project is to create an AI-based compliance analysis system for corporate documents. The system will be able to automatically analyze corporate documents and identify potential non-compliance issues with relevant laws and regulations related to corporations. Here are the main steps involved in creating this system: Collect and preprocess the corporate documents: The documents can be in any format, but it's likely in standardized format such as PDF or DOCX. Clean up the documents to remove extraneous formatting and characters, and ensure that each document is clearly labelled and organized. Compliance documents are posted in a source library historically accessible by the public and is updated daily. Identify the relevant laws and regulations: identify the relevant laws and regulations related to corporations and corporate compliance that apply to your business based on publically available libraries posted on regulator websites. Compile these laws and regulations to easily read and analyzed by the AI with daily update query. Train the AI model: Use spaCy and scikit-learn to train a machine learning model to recognize key phrases, terms, and concepts related to compliance issues. For example, you might use supervised learning techniques to train the model to recognize phrases such as "fiduciary duty," "conflict of interest," or "insider trading." Use the sample corporate documents and the compiled laws and regulations as training data. Create a web interface: Use Flask to create a simple web interface that allows users to upload their corporate documents and analyze them using the trained AI model. The interface should allow users to view the results of the analysis and identify any potential non-compliance issues. Test and validate the AI model: Test the AI model on a representative sample of corporate documents and compare its output to manual reviews by legal experts. Refine the model's performance by incorporating feedback from these reviews and iterating on the training process. Deploy the system: Deploy the system on our server and configure it to automatically scan new documents as they are added to your system. Set up alerts or notifications to notify you of any compliance issues that are detected, and develop a plan for addressing these issues as they arise. It's important to work closely our legal and technical experts throughout the project to ensure that the system is accurate, reliable, and effective. Additionally, it's important to regularly review and update the system to ensure that it remains compliant with relevant laws and regulations as they evolve over time. Provide us with your qualifications and experence in working on this or simular projects.