Enhancing quality and efficiency in the RFP to Production Lifecycle.

Today millions of hours, human resources, and euro, are spent to manually produce documents of different forms. The process lifecycle demands collaborative efforts in deciding to answer a Request For Proposals (RFP), writing the Proposal, writing the Contract and executing the Contract. Sales organizations spend considerable human resources and expend many man hours in analyzing and answering RFPs, and writing the Contract if it is accepted. The delivery organization subsequently exhausts further significant amounts of time understanding the written Contract and following up what should be delivered. By leveraging and enhancing XaitPorter's unique capabilities, Xait with this project aims to redefine the document process industry through developing an intelligent integrated solution for processing RFPs from the time an RFP enters the systems of the organization until the completed production of the Contract. By transforming XaitPorter from automation to intelligent assistance, Xait expects to dramatically reduce both the calendar time, and resource time and manpower, consumed by this process lifecycle to efficiently analyze, write, understand and follow up the documents. So far the project has worked with implementing language models and methods for deconstructing incoming documents, and implementing this in an ?engine?. The engine and the deconstruction, also known as "shredding", are productified in XaitAnalyzer, a separate product to show the results of this process. Q2/Q3 this year has contained testing/verification of the analyzer, and in Q4 we have begun connecting the shredded text with suggested content from a content library to automatically help authors find content to reuse. This will help authors reduce the time spent writing a document.

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Today millions of hours, human resources, and euro, are used to manually produce documents of different forms. The process lifecycle demands collaborative efforts in deciding to answer an RFP, writing the Proposal, writing the Contract and executing the Contract. Sales organizations spend considerable human resources and expend many man hours in analyzing and answering RFPs, and writing the Contract if it is accepted. The delivery organization subsequently exhausts further significant amounts of time understanding the written Contract and following up what should be delivered. Xait aims to dramatically reduce both the calendar time, and resource time and manpower, consumed by this process lifecycle to efficiently analyze, write, understand and follow up the documents. Xait will utilize AI, Machine Learning (ML), Natural Language Processing (NLP) and Natural Language Understanding (NLU) techniques to analyze, categorize, trace appropriate content between requirements and delivery obligations, and suggest additional or replacement content for the identified necessary human participants. In addition, these techniques will be employed to improve the quality and completeness of documents to assist the decision making of human actors for ultimately better Proposals and Contracts. However, neither Xait, nor any of its competitors, have yet developed AI-based NLP/NLU solutions within document processing software for professional users. Existing solutions on the market help partially automate the process and take the contributors through a set of stages, but these software do not yet realize the value of intelligent automation by either leveraging the users' own text at a granular level, or taking the contributor's competency and role into account, for analyzing or classifying the incoming RFP or consequently producing suggestions for the outgoing Proposal.