A falling traffic line has four different causes that look identical on one chart. The four Search Console cuts that tell them apart, in language that needs no SEO background.
Google gives llms.txt no ranking role, and no major AI assistant has adopted it either — the honest case for publishing one anyway, and where the real effort belongs.
B2B buyers now research categories in ChatGPT and Perplexity before reaching your website. What the AI research phase is and how mid-market companies get visible.
How to measure AI visibility: track AI citations, share of AI voice, and AI referral traffic — connect it to pipeline, and report it to a board each quarter without a vanity number.
A practical AI readiness framework for B2B marketing teams: five dimensions — strategy, workflows, skills, data, governance — plus a quick self-assessment.
Why AI cites some B2B companies and ignores others — how E-E-A-T, earned media, named authors, and consistent brand facts build authority AI trusts.
How to structure B2B content for AI citations: answer-first sections, question-led headings, cited data, and FAQ schema that AI search can extract.
AI search reasons about entities, not keywords. How B2B companies use schema markup, consistent positioning, and sameAs links to become legible to AI search.
AI search vs. traditional SEO for B2B: what carries over (technical health, authority), what’s new (extraction, entity clarity), and where budget should go.
How AI assistants like ChatGPT choose which B2B vendors to recommend — the citation, corroboration, and entity-consistency signals that decide who gets cited.
How B2B companies get found and cited by ChatGPT, Gemini, and Claude — five pillars of AI search readiness, from crawl access and schema to authority, reviews, and the page that converts the citation.