AI in Document Management: How LogicalDOC Uses Artificial Intelligence to Improve Document Workflows

Artificial intelligence is becoming an increasingly integral part of document management systems, yet LogicalDOC goes beyond simply adding AI-based features. Our approach integrates models, data, and automation directly into the processes our users use to store, classify, search, and use their documents.
AI capabilities are managed through a suite of components that seamlessly link data preparation, machine learning models, natural language processing, and document automation.

Some examples

A prime example is semantic search. In traditional search, users typically look for documents using words or phrases that must appear—at least in some form—within the indexed content. With embeddings, however, text is transformed into a numerical representation, enabling content comparison based on semantic proximity.

In LogicalDOC, an "Embedding Scheme" links the model used to generate embeddings with the Vector Store that houses them. Documents and search queries are represented within the same vector space, enabling the identification of conceptually similar content even when the same terms are not used.

AI also supports document classification and enrichment.
LogicalDOC’s Fillers enable populating structured data using various strategies. For instance, a document can be assigned tags via a zero-shot model or by comparing its content with already classified, semantically similar documents.

This approach is particularly valuable for large-scale repositories, where much of the work involves not just storing files, but making them easier to identify, organize, and retrieve. Regarding visual document analysis, LogicalDOC can employ YOLO-family object detection models to identify specific elements within images or scanned pages—such as signatures, stamps, logos, tables, or designated document areas. The detected regions can subsequently be processed via OCR and used to populate document attributes.

This architecture also includes automatic content summarization. Specifically, LogicalDOC distinguishes between an extractive approach—which selects the most representative sentences from the original text—and a generative approach based on the ChatGPT API. In the former case, the system uses MiniLM to create semantic representations of sentences and the MMR algorithm to select the most relevant ones while minimizing repetition.

Our goals

A key aspect of LogicalDOC’s design is the flexibility to use different technologies depending on the specific task. Some components can be trained on an organization's own content, while others leverage pre-trained models; some processing tasks run locally, while others rely on external services.
LogicalDOC’s primary goal is not to treat Artificial Intelligence as an element separate from the document management system, but to integrate it where it can genuinely help interpret, organize, and retrieve information more effectively.
From this perspective, document management is shifting away from mere file storage and increasingly toward understanding documents and turning their content into information that drives business processes.