Cataloguing digital collections is a critical yet often underestimated process within digitisation workflows. While image or document capture has advanced enormously with high-resolution scanners and cameras, the creation of descriptive metadata remains a bottleneck that consumes hours of specialised work and significant budgets. In this context, artificial intelligence models are emerging as a viable alternative to automate —or at least assist— the generation of catalogue records. However, not all AI solutions are equally effective, and their implementation requires careful analysis of each institution’s specific needs.
From a technical perspective, the most promising models for automatic cataloguing rely on natural language processing (NLP) and computer vision techniques. For instance, transformer-based systems like BERT or GPT can analyse existing descriptive texts and suggest tags, summaries, or even thematic classifications. In the image domain, convolutional neural networks (CNNs) and multimodal models such as CLIP can extract visual attributes —colours, shapes, objects— and translate them into structured metadata. These approaches, combined with ontological knowledge bases, allow a machine to “understand” the content of a historical photograph or an ancient manuscript with a precision that seemed unattainable just a few years ago.
Nevertheless, adopting these technologies is not without challenges. Models require large volumes of expert-labelled training data, which can be paradoxical: manual work is needed to avoid manual work. Moreover, AI systems can make errors in ambiguous contexts or with dialectal variations, and their performance heavily depends on input data quality. Therefore, rather than proposing a complete replacement of the human cataloguer, the most sensible strategy is to design a hybrid workflow where AI generates proposals and the professional validates, corrects, or enriches the results. This human-machine collaboration not only accelerates the process but also improves consistency and reduces biases.
On the business and technology side, implementing AI models for cataloguing must be supported by a solid infrastructure. This is where cloud services like AWS or Azure come into play, offering elastic computing power, scalable storage, and pre-trained Machine Learning tools (e.g., Amazon Rekognition for image analysis or Azure Cognitive Services for text). A company like Q2BSTUDIO can advise on choosing the most suitable cloud platform —public, private, or hybrid— ensuring costs and latency remain under control. Furthermore, data security is paramount when handling digital collections with heritage value or access restrictions; hence, cybersecurity services —from pentesting to end-to-end encryption— are an indispensable complement in any project of this kind.
Integrating automated cataloguing with business intelligence (BI) systems opens an additional dimension. Once metadata is generated consistently, tools like Power BI allow creating dashboards that monitor digitisation progress, identify collections with higher information density, or detect usage patterns. These analyses feed strategic decisions about cataloguing and preservation priorities, turning a technical process into a management asset. And when we talk about AI agents, we refer to autonomous systems that can, for example, proactively search digital holdings, propose metadata enrichment, or even interact with users through conversational interfaces to facilitate collection queries.
Experience shows that no single model solves all use cases. For a national library cataloguing incunabula, a model trained on medieval manuscripts will be more useful than a generic one; for a photographic archive, computer vision will take precedence; for a repository of administrative documents, NLP will be key. Therefore, the custom software developed by Q2BSTUDIO allows adapting algorithms to the particularities of each collection, also integrating cloud, cybersecurity, and BI modules as per client needs. A concrete example would be creating a pipeline that receives scanned images, processes them with vision models to extract text (advanced OCR) and graphic elements, then uses language models to assign descriptors, and finally publishes the records in a collection management system with secure APIs.
Regarding the evaluation of these systems, measuring the accuracy of generated tags is not enough. Usability for staff, processing speed, scalability for large volumes, and return on investment (ROI) in terms of human hours saved are also relevant. A good practice is to conduct pilot tests on a representative subset of the collection, comparing different models and configurations, and documenting both successes and errors to iterate on the model. Only in this way can a long-term sustainable solution be guaranteed.
Finally, it is important to remember that digital cataloguing is not an end in itself, but a means to make cultural, scientific, or business heritage accessible. Well-implemented AI models not only reduce costs and times but also democratise access to knowledge by allowing collections previously buried in physical catalogues to emerge in search engines and digital platforms. In this horizon, collaboration between domain experts, software developers, data engineers, and companies like Q2BSTUDIO becomes essential to build solutions that are both technically and ethically responsible.
