Any document, from any source, on the right desk
Structured data does not represent the totality of the data and thus of the documents circulating within the company. On closer inspection, the data we find on the ERP must have been entered by someone. It is into this automation vacuum that Intelligent Document Processing fits. It is useful in all those information flows and processes that are triggered by data residing on an unstructured document. From paper to PDF and e-mails, the combination of OCR, NLP, RPA, AI and ML extracts data from any type of medium, whether physical or digital, and unstructured, recognises the nature and meaning of the extracted data, places it in the appropriate systems, such as ERP, and conveys the document within business flows.
80 to 90 per cent of data and business documents are unstructured and require the integration of different technologies to be transformed into a format readable by business systems, and this is where Intelligent Document Processing comes in.
What is intelligent document processing?
Intelligent Document Processing (IDP) is a technological solution that enables companies to automate document processes even when dealing with unstructured data from different input channels (such as paper, email, certified email, mobile, electronic data streams , etc.).
How does IDP/RPA work?
IDP and RPA solutions exploit different technologies that combine in a 'tailor-made' manner to create an end-to-end process solution that is fully integrated with your existing systems. Some of the technological components used are:
- Next-generation AI-based OCR: to extract reliable data even from unstructured documents and thus make them usable for your organisation.
- Computer Vision: ability to process content and identify specific characteristics that are then used in the automatic classification and separation of documents.
- Machine Learning: the software is capable of generating models with which it is able to classify and separate documents according to their content. Sophisticated machine learning algorithms are employed, capable of learning even automatically and unsupervised from just a few examples.
- RPA Desktop Automation: to automate integration processes with third-party systems by processing and manipulating the necessary data, but also by creating automatic reports (e.g. Excel spreadsheets for your Business Intelligence and to take corrective actions by improving your document-based processes).
- No-Code Interfaces: the use of visual interfaces instead of custom code guarantees rapid development, which translates into a return on investment in the short term. At the same time, it provides greater protection against implementation errors, offering greater ease of maintenance and future evolution.
- BPM: to convey documents within your business flows in a targeted elastic and intelligent way.
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