Blog /AI

Sharing Drupal code across country sites does not sync product specifications, documents, or company claims.

Drupal multisite vs multilingual teams must decide where authoritative facts live before AI assistants encounter contradictory pages. Maciej Lukianski compares multisite, one multilingual Drupal, and Domain Access for market-aware publishing.

Every update on a multilingual Drupal site must reach each market language - not just the source page.

Drupal multilingual websites can use AI-assisted translation drafts inside Content Translation workflows so editors review and publish without copying text between tools. Maciej Lukianski explains shared fields, editorial ownership, document links, and keeping localized pages consistent for people and AI search.

AI crawlers already fetch product pages during live conversations, but citations stay rare. Drupal sites cited by AI need one fact in fields, then the same value on the page, in JSON-LD, in feeds and through JSON:API or MCP tools.

Maciej Lukianski walks through what fetchers need, which Drupal modules cover Markdown, llms.txt and MCP Server today, and where configuration still decides whether a bot can quote your catalogue.

To write content for AI search, answer the main question near the start of the page or section. Use a self-contained paragraph that names the subject, gives the direct answer and makes sense without the text around it.

Answer-first writing is that editorial pattern for the first 40 to 60 words. Here are before/after rewrites, H2 audits, Drupal direct-answer fields, and a pre-publish checklist.

Asking ChatGPT about your own brand proves nothing. Logged-in memory, one-off prompts, and shortlist shuffles that change run to run produce screenshots, not data.

How to measure whether AI recommends you needs a fixed question set, clean sessions, four metrics, and a scoring rule you wrote before you looked at the results. Here is how to run the baseline, choose spreadsheet versus SaaS versus self-host, and report what boards actually need.

For an assistant to quote your site, three things have to work on production: named bots allowed through robots.txt and the CDN, HTML back fast enough for a live fetch, and facts in the first response.

Can an AI actually read your website is mostly technical SEO with extra agents and typed data. Here is how to verify each layer on production, from Cloudflare AI-bot policies to JSON-LD in Drupal.

A buyer describes what they need to a chatbot and gets three supplier names back. You are not one of them, though your product fits, your certifications are current and your lead times beat all three.

Getting your company recommended by AI depends on publishing the facts a chatbot can verify during a live conversation - specs, prices, lead times, and industry pages. Here is why qualified suppliers get filtered out, and what to put on your site so you stay in the shortlist.

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.