Technology
4 months
United States
A company maintains a collection of policy documents in PDF format that it distributes to its users. Recently, the company updated the design and layout of these PDFs to improve readability and user experience. As a result, there is a need to convert all existing policy documents from the old format to the new one. This process of manually updating each PDF is not only time-consuming but also prone to human error, making it challenging to ensure that data is accurately and consistently placed in the new format.
To address this inefficiency, the client approached Tezeract seeking an AI-powered PDF extraction and conversion solution. The goal is to develop an AI PDF conversion tool that leverages LLM-powered PDF formatting techniques to extract data from old-format PDFs and seamlessly integrate it into the new design. This solution aims to automate the PDF to PDF conversion process, streamline workflows, and ensure accuracy across all documents.
Tezeract quickly grasped the client’s needs and developed an AI PDF conversion tool to update old-format PDFs to the new design. Our team began by examining the structure of the existing PDFs to fully understand how the data was organized.
We created a JSON script that utilized PDF parsing solutions to extract data from the old PDFs with AI-powered PDF extraction techniques. This data was then matched with the new format templates, and we employed LLM-powered PDF formatting techniques to ensure the old PDFs were converted into the most suitable new format.
Once all the PDFs were converted, we moved on to the next step, using LLMs to identify and correct text errors in the documents. In the final step, we focused on spelling and grammar mistakes. The system automatically corrected any grammar issues while highlighting typos for manual review. This process of PDF to PDF conversion automation ensured that the new PDFs were consistent in format and free of errors, significantly enhancing the quality of the policy documents
Developing an effective JSON script required a deep understanding of the data structure within the old-format PDFs. The challenge lay in ensuring the script was not only functional but also optimized for performance, allowing for efficient data extraction and integration into the new format.
Processing each page of the PDFs sequentially was time-consuming and inefficient. To address this challenge, we implemented threading techniques, enabling the processing of multiple pages simultaneously. This significantly reduced processing time and improved the overall speed of the conversion tool.
We faced the challenge of accurately interpreting user feedback regarding desired improvements. It was essential to thoroughly understand the underlying concepts behind each suggestion. This ensured that updates were both relevant and intelligent, aligning with user expectations while maintaining the tool’s functionality.
First, we collaborated with the FN-AD team to understand their business and goals, developing a detailed project brief with market research and competitor analysis. We then created a concrete plan, outlining key features like automated brand classification, profile creation, and lead management CRM to validate the MVP assumptions.
At this stage, our focus shifts to developing the AI PDF conversion tool by designing the architecture of the AI model and training it.
After the product launch, we collected feedback from end-users to refine and enhance the product. We introduced new iterations and features concurrently to ensure an optimal user experience.
The tool utilizes advanced AI techniques to accurately extract data from old-format PDFs. This ensures that essential information is seamlessly captured and prepared for conversion, reducing the risk of data loss during the process.
Leveraging large language models (LLMs), the tool intelligently matches extracted data with new format templates. This feature ensures that the converted PDFs not only maintain consistency in layout but also enhance readability and user experience.
The system automatically identifies and corrects grammar and spelling errors in the converted documents. By highlighting typos for manual review, it guarantees that the final output is polished and free of mistakes, improving the overall quality of policy documents.
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