Maciej Lukianski

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.

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.

Planning a new intranet requires a strategic approach and consideration of many factors – from the needs of different departments, through technical aspects, to a long-term and clear vision for development. A well-planned intranet site increases productivity and improves information flow. A poorly prepared project may end up as a tool that employees ignore.

Your AI chatbot works perfectly — it retrieves relevant documents, grades their quality, and generates accurate answers. But your monthly OpenAI bill shows $3,000, and when you analyze the logs, a disturbing pattern emerges: 30% of queries are simple questions like "What are you?" or "Hello" that trigger your entire expensive RAG pipeline. Each "Hi" costs $0.05 and takes 25 seconds to process a full vector search, document grading, and LLM generation for a greeting.

Your marketing team just published an important policy update. Two hours later, a user asks your AI chatbot about the new policy, and it confidently explains the old version—completely unaware that anything changed. The user, confused by the discrepancy with what they read on the website, opens a support ticket asking why the chatbot is "broken." Your team explains that the chatbot needs manual re-indexing, which happens nightly. The user's trust in your AI system drops significantly.

You're starting a new RAG project and face a decision that will shape your next 6-12 months: use a framework like LangChain, or build directly with the OpenAI API? The internet offers conflicting advice. X’s threads call LangChain "overkill" and "too much abstraction." Blog posts praise its mature patterns and ecosystem. Your team splits between "let's move fast with the framework" and "we should control our own code."

It starts like this: a user types, “How do I reset my password?” and waits… 25 seconds… 30 seconds… before giving up and emailing support. Behind the scenes, your AI chatbot does have the right answer — but it’s too slow, too costly, and users are walking away. The latest API bill? $5,000, mostly for answering the same dozen questions again and again.

Your AI chatbot may respond quickly, but are its answers correct? Many organizations implementing RAG (Retrieval-Augmented Generation) chatbots discover a frustrating truth: semantic similarity doesn't equal relevance. For example, a user may ask about "implementing zero-trust security architecture in hybrid cloud environments," and the system confidently returns articles about "cloud security," but they discuss basic firewall rules instead of zero-trust principles.