{"id":17798,"date":"2026-06-05T13:27:09","date_gmt":"2026-06-05T11:27:09","guid":{"rendered":"https:\/\/ai-matters.eu\/?p=17798"},"modified":"2026-06-05T13:27:10","modified_gmt":"2026-06-05T11:27:10","slug":"ai-rfq-automation-document-intelligence-manufacturing","status":"publish","type":"post","link":"https:\/\/ai-matters.eu\/it\/ai-rfq-automation-document-intelligence-manufacturing\/","title":{"rendered":"Streamlining the RFQ Process: How AI-Driven Document Intelligence is Transforming Requirement Management and PLM Integration"},"content":{"rendered":"\n
PLM Solutions’ Experience:<\/div>
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The Challenge: The Burden of Unstructured Data in Technical Procurement<\/strong><\/strong><\/h2>\n\n\n\n

In complex industrial sectors such as automotive, aerospace, and machinery, the initial phase of a project often begins with a Request for Quotation (RFQ). Historically, analyzing these documents has been a manual, time-consuming, and error-prone process. Technical requirements are typically buried within unstructured formats like PDFs, Word documents, Excel files, making it difficult to maintain consistency, ensure traceability, and quickly ingest data into management systems.<\/p>\n\n\n\n

To address these inefficiencies, two interconnected projects were developed to leverage Artificial Intelligence for automating the extraction and lifecycle management of these critical requirements.<\/p>\n\n\n\n

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The Solution: AI-powered document intelligence with tech reveal technology<\/strong><\/strong><\/h2>\n\n\n\n

Both projects center on the integration of Tech Reveal, a Document Intelligence technology that utilizes advanced AI models to extract, classify, and interpret structured and unstructured information from complex technical documents.<\/p>\n\n\n\n

With AI MATTERS financial support, MADE 4.0 is testing the solutions in a realistic environment, optimizing the automatic extraction of technical requirements from RFQ documents, reducing analysis time, and improving the quality of the information processed.<\/p>\n\n\n\n

Project 1: Automated Extraction and RM Ingestion<\/strong><\/strong><\/h2>\n\n\n\n

The first project focuses on the semantic interpretation<\/strong> of technical requirements. By configuring machine learning models specifically for RFQ parsing, the system can automatically identify key technical data.<\/p>\n\n\n\n