# PDF Crawling ## Alhena AI Uses Advanced AI Models to Extract and Present Data from PDFs At Alhena AI, we leverage cutting-edge artificial intelligence technologies to enhance data extraction capabilities from PDF documents. Our goal is to transform raw PDF content into structured data that is easily interpretable and actionable by AI systems. ### Understanding PDF Data Extraction PDFs, while widely used for document sharing, often present challenges due to their unstructured nature. Our AI models are specifically designed to tackle these challenges by: #### 1. Crawling PDF Documents Alhena AI employs sophisticated crawling techniques to locate and retrieve PDF files from various sources such as websites, databases, and cloud repositories. #### 2. Extracting Tables Tables embedded within PDF documents contain valuable structured data. Our AI models utilize optical character recognition (OCR) combined with machine learning algorithms to accurately extract tabular information. #### 3. Data Structuring Once extracted, the data undergoes a structuring process where AI algorithms categorize and organize information into coherent datasets. This step ensures that the extracted data is in a format suitable for further analysis and understanding. #### 4. Presentation for AI Comprehension To facilitate AI understanding, Alhena AI transforms the extracted data into formats such as JSON or CSV. These formats are designed to be machine-readable, enabling AI systems to process and derive insights from the extracted content effectively. ### Advantages of Using Alhena AI * **Accuracy:** Our AI models are trained on diverse datasets to ensure high accuracy in data extraction and transformation. * **Scalability:** Alhena AI can handle large volumes of PDF documents efficiently, making it suitable for enterprises with extensive document processing needs. * **Integration:** The structured data can seamlessly integrate with existing AI applications, enhancing automation and decision-making processes. **How to use it?** Go to AI settings screen and upload pdf as a files alternatively you can upload multiple pdfs in google drive and share that link in the url --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://alhena.gitbook.io/docs/ai-configuration/data-sources/pdf-crawling.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.