struct.answer is an AI-powered tool tailored specifically for structural engineering. Unlike general-purpose language models, it’s trained on structural engineering data, enabling it to:

• Analyze, summarize, and extract insights from structural and construction documents.

• Answer technical questions accurately based on the provided documents, such as PDFs, reports, calculations, and engineering drawings.

• Provide engineers with efficient ways to manage, tag, and organize documents, including features for highlighting text, taking notes, and generating reports like desk studies.

struct.answer supports a variety of common engineering document formats, including:

• PDF: The most commonly used format for engineering reports, drawings, and technical documents.

• DOCX: Microsoft Word files for reports and textual data.

• XLSX: Microsoft Excel files, often used for data tables, calculations, and borehole logs.

• CSV: Commonly used for data export and tabular data.

struct.answer uses a combination of machine learning and natural language processing to analyze structural engineering documents. Here’s a simplified workflow:

1. Upload Documents: Users upload relevant engineering documents (PDFs, reports, etc.).

2. Processing and Analysis: The tool reads the documents, extracting data and analyzing it for technical insights.

3. Question and Answer Function: Users can ask specific questions, and the tool generates answers based on its structural engineering training and the content of uploaded documents.

4. Organizing and Managing Content: Users can assign tags, take notes, and highlight critical information for easy access and organization within projects.

struct.answer’s AI leverages both general language understanding and industry-specific knowledge from training on structural engineering datasets. This approach ensures it offers precise responses and insights relevant to engineering contexts.

struct.answer differs from general LLMs like ChatGPT in three major ways:

1. Engineering-Specific Training: Unlike ChatGPT, which is a broad, general-purpose model, struct.answer is trained on a curated dataset focused on structural engineering. This specialization allows it to generate more accurate and technically relevant answers for engineering queries.

2. Document-Centric Functionality: While ChatGPT and similar models answer general questions without document analysis, struct.answer allows users to upload specific documents, making it capable of precise document-based Q&A. Engineers can ask questions about specific documents, and the tool analyzes only the content within those documents.

3. Industry-Specific Tools: struct.answer includes built-in tools such as document tagging, highlights, desk study reporting, and note-taking, which are not available in general LLMs. These tools help structural engineers manage and make sense of large volumes of technical documents.

The application is packaged as an .exe file for Windows. Here’s a summary of recommended system requirements:

• Operating System: Windows 10 or higher

• Memory: At least 8GB of RAM (for handling multiple documents and large data sets)

• Disk Space: 500MB for the application plus additional space for document storage

• Internet Connection: Required for AI model analysis and answer generation

These requirements ensure smooth operation, allowing engineers to handle complex documents and queries efficiently.

We ensure privacy by not using any customer data to train our LLM and ensure we comply with GDPR requirements. Data privacy is a priority for struct.answer:

• Local Processing: Documents are processed locally on your device. Only essential data (like extracted text) is sent for analysis, ensuring sensitive information stays secure.

• No Data Retention: struct.digital doesn’t store your documents or retain any personal information.

• Optional Usage Data: Basic, non-personally identifiable usage statistics (e.g., number of questions asked) may be collected to help improve the tool. This data is anonymized and only used for product development purposes.

Please get in touch with us to get a quote. We have a range of pricing options depending on how many licenses are needed, subscription length etc.