Contract data extraction is the process of automatically identifying and pulling out specific/relevant information from contracts or legal documents. This process transforms unstructured contract text into structured data that is much more convenient to analyze, helping businesses find and use key details hidden in their contracts. Contract data extraction can be challenging due to the complexity of legal language, industry-specific terminology, and ambiguous legalese used in contracts. Recent developments in machine learning have given rise to solutions capable of handling complex data extraction tasks, leveraging a combination of NLP, LLMs, and AI to read and understand contracts. Two types of contract data extraction tools are specialized LLMs trained on legal data, such as Harvey AI or Robin AI, which can handle complex legal analysis, and AI-powered rule-based intelligent document processing (IDP) solutions like Nanonets, which can automate existing contract data extraction workflows without disturbing existing processes. To extract data from contracts using LLMs, users must create structured prompts, input the contract text, and refine their prompts based on the extracted results. IDP software like Nanonets allows users to build end-to-end automated workflows, auto-capturing incoming contracts, refining extracted data, customizing final output, setting up approvals or validations, and exporting it to downstream contract management software or ERP.