Blog /AI

Integrating AI with Drupal content creation works well for text fields, but taxonomy mapping remains a significant challenge. AI extracts concepts using natural language, while Drupal taxonomies require exact predefined terms and the two rarely match. This article explores why common approaches like string matching and keyword mapping fail, and presents context injection as a production-proven solution that leverages AI’s semantic understanding to select correct taxonomy terms directly from the prompt.

PDF data extraction quality directly determines AI accuracy. When building BetterRegulation’s document processing system, we found that naive extraction wastes 40-60% of context windows on PDF artifacts. After evaluating ChatGPT API, traditional Python libraries, and Unstructured.io, we achieved 30% token reduction and significantly improved document categorization. Here’s what we learned.

AI Automators transforms complex AI workflows from code to configuration. This case study reveals how BetterRegulation built production-grade AI workflows processing 200+ documents monthly with 95%+ accuracy – using multi-step chains, background queues, and admin-managed prompts. No custom integration code required.

Information gathering, content writing, proofreading, SEO optimization, tag preparation – all these tasks consume a significant portion of the editorial team’s time. What if you could reduce this research time by up to 90% through automated content creation? In this article, I present a practical Drupal setup that uses AI-powered modules to generate editorial content with minimal manual input. This includes automatic information retrieval based on the title, tag generation, content creation, and detailed data fetching – all directly in your CMS, without switching between different tools. Read on or watch the episode from the Nowoczesny Drupal series.

AI document processing is transforming content management in Drupal. Through integration with AI Automators, Unstructured.io, and GPT models, editorial teams can automate tedious tasks like metadata extraction, taxonomy matching, and summary generation. This case study reveals how BetterRegulation implemented AI document processing in their Drupal 11 platform, achieving 95%+ accuracy and 50% editorial time savings.

Attu is a powerful tool that greatly simplifies working with the Milvus vector database. Instead of writing Python code or using the API, you can manage collections, search for vectors, and monitor the system using an intuitive graphical interface. Thanks to Attu, working with Milvus becomes accessible not only to experienced developers, but also to data analysts and AI project managers.

Modern corporate intranets store vast amounts of documents, procedures, instructions, and organizational knowledge. Traditional keyword-based search often fails when users search for information using terms other than those found in the documents. Problem: an employee searches for "how to configure access to the payment system," but the document contains the phrase "payment integration configuration." Solution: RAG (Retrieval-Augmented Generation) with a vector database enables semantic search.

Vector databases have become a key component of modern AI applications in Drupal. Thanks to integration with the AI Search module, they enable semantic content search, reduction of hallucinations in AI chatbots, and implementation of advanced RAG (Retrieval Augmented Generation) functions. Choosing the right VDB provider can significantly impact the performance, cost, and scalability of your AI solution in your Drupal project.