Specification documents are fundamental tools for defining in detail the requirements, characteristics, and technical criteria that a product or process must meet.
These include product technical specifications, test specifications, or process specifications. These documents ensure uniformity, compliance with standards, and clear communication between designers, manufacturers, suppliers, and customers. Their analysis represents a complex challenge for many companies, particularly those in the manufacturing sector and in extended supply chains. These documents, often extensive and detailed, contain a vast amount of critical information, including regulations, functional requirements, and technical parameters, which suppliers must map and comply with to ensure proper customer satisfaction.
Their management is commonly entrusted to teams of technicians tasked with identifying and extracting critical information and synthesizing it into standard formats for forwarding to design teams. This ‘manual’ approach has several drawbacks: the volume of data is hard to process, the time available for analysis is limited, and the risk of errors or omissions is high. This creates an urgent need for technological solutions to support the process.
Ineffective management of specifications can lead to significant consequences, including decision-making delays, increased operational costs, regulatory non-compliance, and design errors. Incorrect identification of applicable regulations exposes the company to legal risks, and unrecognized or misinterpreted requirements can result in design errors.
The adoption of solutions based on artificial intelligence (AI) and natural language processing (NLP) promises to overcome many of these limitations, supporting data extraction and improving analysis processes.
Natural Language Processing Technologies for Technical Data Analysis
Natural language processing (NLP) represents a set of technologies designed to understand and analyze natural language. Among the main NLP tools and techniques for the automatic analysis of specifications are OCR (Optical Character Recognition, such as Google’s Tesseract), which allows paper documents or images to be digitized and transformed into editable text.
Tools for applying NER (Named Entity Recognition) techniques are indispensable, as they automatically identify relevant entities such as materials, standards (e.g., ISO), technical requirements, and regulations. Equally important are tools for table analysis, essential for extracting key parameters from the numerous datasheets attached to specification documentation. Large Language Models (LLMs), such as OpenAI’s GPT-4, also find applications in this domain, performing complex semantic analyses, answering questions about documents, or verifying consistency and compliance with predefined standards.
By integrating these technologies, it is possible to transform complex documents into structured and easily accessible data.
Discover our AI software for the automatic extraction of information from technical documents.
Use Case: Requirement Extraction and Evaluation in the Railway Sector
Specification documents take on different levels of complexity and criticality. In the railway sector, for example, they are fundamental documents for regulating the design, construction, and maintenance of systems, and their analysis requires the extensive involvement of expert technicians.
A case in which the application of NLP showed significant advantages relates to a project conducted by Erre Quadro with a major multinational in the sector. Here, the analysis of technical specifications exceeding 2,000 pages typically involved an average of four technicians working full-time for at least five days. The complexity of technical language, combined with the intricate organization of information within the documents, made the process inefficient and prone to frequent delays, highlighting the urgency of an automated solution. The project conducted by Erre Quadro aimed to reduce the time required for system requirements extraction, increase the reliability of results, and anticipate the identification of the most ambiguous and unclear requirements.
To address these challenges, the Erre Quadro team developed software based on Machine Learning algorithms capable of extracting requirements and estimating their level of ambiguity. This software consists of a document pre-processing module and an NLP module, which is further divided into a tokenization and POS-tagging system, a Requirements Recognition workflow applying a combination of OCR, LLMs, and ML classifiers, and a system for formally evaluating extracted requirements. For this latter purpose, a knowledge base was created, founded on technical elements such as units of measurement, functions, physical properties, and standards, enabling the evaluation of the content and formal quality of requirements, assigning scores based on clarity and completeness criteria.
Results Obtained
The project was evaluated on a corpus of over 2,000 pages derived from technical specification documents, from which the software was able to extract over 6,200 technical requirements. From these, the system identified more than 3,000 specific measurement units and 543 regulatory references, resulting in a list of over 50 regulatory acronyms previously unknown to the client. Sample evaluations, conducted on multiple random groups of 100 requirements (50 with references to standards and 50 without), determined that the system ensured 84% correspondence with the manual attributions of experts, thereby confirming the reliability of the process. This process, positioned as a support for technicians, promises to reduce by over 90% the time required to analyze an entire specification.
Conclusions: Toward More Effective Specification Analysis
The introduction of AI and NLP-based solutions represents a turning point for companies that need to analyze complex engineering documents such as specifications. Automated document analysis not only significantly reduces time and costs but also improves the accuracy and quality of results, standardizing processes and making data more accessible.
As technologies evolve, the integration of these tools into corporate workflows will further optimize the management of technical information, offering companies a lasting competitive advantage. AI is thus emerging as an indispensable ally for those operating in data-intensive sectors, transforming the challenges of document analysis into opportunities for growth and innovation.
Request a demo of our AI tools.
