Optimization of Data Collection Process Thanks to AI Algorithms

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Optimization of Data Collection Process Thanks to AI Algorithms


In an era of progress and numerous achievements in natural language processing, we see increasing possibilities in the analysis and inference based on data collected in unstructured textual archives and documents. Therefore, streamlining business operations and implementing NLP (Natural Language Processing) and LLM (Large Language Models) tools become essential. The digitization of business processes moves many tasks into the virtual world, and controlling and analyzing a large volume of collected data becomes a significant challenge.

Save time

Of course, time is crucial. In the age of constant haste, laborious analytical tasks requiring focus, precision, and diligence become a burden for efficient and often dispersed teams. The modern business operates swiftly. Many industries, such as accounting, law firms, insurance, or transportation, place significant emphasis on the quality of documentation, which significantly impacts group efficiency.

Let’s take the transportation sector as an example. TSL companies handle dozens of freight shipments daily, each generating a large amount of documentation, including invoices, contracts, policies, delivery notes, and driver logs. When added to the internal administrative workflow, it’s easy to overlook an unreadable document (e.g., hastily scanned), which can disrupt the document circulation process at a later stage and require corrections, thus significantly extending the task list. This could affect cash flow. Therefore, optimizing the data collection process with the right quality becomes crucial.

Traditional methods are becoming less effective

Traditional software development methods are becoming less effective when dealing with large databases. They often rely solely on structurally processed data and predefined rules, which may not be suitable for working with unstructured text and the subtleties of natural writing. The classical approach to software development faces challenges when dealing with large knowledge bases.

Working on DocsQuality, we lower the barrier for teams to access NLP (Neuro-Linguistic Programming) and LLM (Large Language Model) tools, enabling them to easily retrieve data from unstructured texts and documents.

The role of DocsQuality software is to assess document attributes before they are imported into the accounting workflow. It evaluates real-time factors such as readability, clarity, and file quality. The software immediately highlights issues, for example, with email attachments, signaling that the system should not accept the document into the workflow at this stage.

NLP and LLM models

Combining and applying NLP and LLM models is an innovative method for assessing document quality. During software development, NLP can be used to extract text from image documents, utilizing Optical Character Recognition (OCR) techniques to transform image text into editable text, which can then be evaluated for quality. On the other hand, LLM models can read and understand text in PDF documents, allowing for assessment of readability, grammar, syntax, and spelling correctness, followed by content analysis, inference, and understanding.

The engineers at Inero Software are continuously developing the functionalities offered by DocsQuality. Creating new software in such a dynamic environment requires constant improvement and searching for optimal solutions. The potential of integrating NLP and LLM tools into this software will enable even more precise documentation analysis in the future.

DocsQuality software

If you are interested in DocsQuality software, we invite you to visit our website at https://docsquality.com/ and contact us via email at hi@docs-quality.com. We would be happy to answer all your questions and provide information about its functionalities and implementation. DocsQuality can be successfully integrated with an existing ERP system, expanding the offered functionalities to include effective monitoring of document quality, and allowing the identification of problematic files before they are introduced into the accounting workflow.


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