Common Components Framework (CCF) Enabled Intelligent Data Processing

Request for proposals (RFP) documents are formal documents used by organizations to outline the requirements, scope of work, expectations and evaluation criteria of a project. Creation of response of these proposals require organizations to rewrite the entire proposal which is a time consuming process. The digitization of the response for such client proposal documents have numerous advantages that can significantly improve the efficiency and reduce the manual labour required for creating response to these proposals. Real-time access to vital data significantly cuts response time for similar projects, leading to substantial cost savings by automating processes and optimizing resource allocation. Digitizing documents enhances accessibility, enabling continuous engagement and global reach. Digital platforms offer strong analytical tools for informed decision-making. Automating these processes enhances organizational agility, efficiency, and competitiveness, facilitating quick decision-making, meeting deadlines, attracting top vendors, and optimizing resources. The common components framework makes use of fine tuned large language models to extract relevant business entities from dedicated sections of response for client proposal documents, standardize it using auto mapping techniques and add it to a vector database and a graph database making relevant connections according to a graph model. These databases can further be utilized for analytics and search operations. Both the graph and vector databases are leveraged to devise a customized learning to rank logic to provide relevant semantic search results. This hybrid approach increases the relevance of the search results considerably. The proposed pipeline handles the challenges of bulk processing of documents of varying formats and structures efficiently, by normalizing all data sources into a single format and addressing engineering issues of scalability, logging, error handling and cost optimization through optimal utilization of resources, thereby, fostering green computing.