A fashion company, with offices around the world, asked us to support them in a complex digitization and automation project in the field of document management.
The company had problems organizing contracts, invoices, DDT, and other accounting documents.

The fast pace of incoming documents to be processed, the growing number of customers, and the variety of documents made it difficult to integrate documents into the company database correctly and easily.
The acquisition required many manual steps, and previous digitization attempts had failed, unable to ensure the manipulability of the acquired data.

Additionally, there were two other problems: the documents received from various suppliers and commercial resellers were very heterogeneous in terms of formatting; and each document needed to be reviewed and revised before being entered into the company database to ensure the correctness of the stored data and the validity of the document itself.

The lack of an effective automated system slowed down the entire document management process: the administrative architecture was cumbersome (and indeed, very heavy) - and all the workflows involved were slow and cumbersome, negatively impacting overall operational efficiency and the company's ability to respond promptly to market demands.

Thus, after an initial consultation, the company decided to embark on a long-term renewal path with us: updating their document management processes, relying on software enhanced by the agility of Artificial Intelligence.

First, we addressed the root of the problem by developing a solution that could automatically acquire and standardize the company's documents: a system that ensured the compliance of each document with internal requirements and facilitated the extraction, reprocessing, and subsequent retrieval of data.

To achieve the result, our engineers developed software that revolves around the hybridization between a Large Language Model (LLM) and some more classic automation algorithms.
On one hand, a microservice collects the document and performs an initial reprocessing; on the other hand, the LLM, integrated with some Natural Language Processing (NLP) algorithms, interprets the content of the documents, extracts the information contained therein, and reorganizes it in a coherent and coded manner within standard templates.

The solution also includes the automatic classification of reformatted documents. To ensure not only great speed but also intuitiveness and immediacy in document management.
In this way, all documents are standardized and adapted to our partner's criteria - and the enriched and structured data is immediately available to the company, automatically acquired into the company databases.

After completing the first phase, the team then focused on developing a solution to validate and correct the information contained in the texts.
Again, we used NLP algorithms, but this time combined with Anomaly Detection algorithms. The combination of Machine Learning techniques made it possible to integrate an automatic error correction system into the document management software.

The system can identify entry errors, missing information, or out-of-norm data and correct errors (where possible) before acquisition into the database. If the error cannot be corrected - or requires human review - it instead signals its presence to the relevant person.

Overall, this solution has allowed us to almost completely automate our partner's document management: the process of acquiring, verifying, and organizing documents has been simplified and accelerated; the organization and management of the company's information capital have been improved; and employee productivity has been significantly increased, thanks to the simplification of administrative practices.