Generative AI Document Extractions Using Composite Approach: An External Data Integration

Generative AI models comes with lot of accurate generations and extractions. There are many AI models which are extracting data sets from Images, documents, and PDFs but accuracy (Safder et al. in Inf Process Manag 57, 2020 [1]) and precision have been an outgoing challenge for businesses. This paper is an attempt to solve that business challenge using multiple generative AI models. Integrating and automating external data, such as procurement documents, involves identifying and implementing patterns that streamline data flow and processing. Organizations often use technologies like Optical Character Recognition (OCR) to extract text and structured data from these documents. Data integration platforms and APIs are employed to connect these documents with internal systems, such as Enterprise Resource Planning (ERP) software. Machine learning models can be utilized to classify and extract relevant information from diverse document formats. Workflow automation tools help route documents for approvals and actions. By establishing these patterns, organizations can enhance efficiency, reduce manual data entry, and ensure the seamless integration of external data, ultimately improving the accuracy and speed of procurement document processing. Different Generative AI-based document extraction tools have varying levels of accuracy for different types of documents. By comparing the confidence level of each field extracted by each tool, we can select the tool that provides the highest quality extraction for each field.