Do you implement, or do I implement?
Three of the four components are blueprints — GEO/AEO Discovery Audit, AI-Ready Content Architecture, and Entity & Schema Optimization. You receive a developer-ready document that your dev team (or your CMS admin) implements. One component is delivered as a finished file: the LLMs.txt Config. We write the custom llms.txt for your site; you upload it to your site root and you're done — no developer needed for that piece. If you want hands-on implementation of the full bundle (schema deployed, content templates rolled out, entity graph built), upgrade to Leverage → for a custom done-for-you quote.
Is AEO/GEO a real thing or hype?
It's measurable. ChatGPT cites about 1 million sources per day. Perplexity surfaces citations on every answer. Google AI Overviews appear on a growing share of commercial queries and have their own citation pattern that doesn't match the classic blue-link ranking. The traffic shift is documented in Search Engine Land, Pew Research, and Google's own announcements. The work isn't magic — it's structured content, schema, entity signals, and a clean llms.txt. The same fundamentals SEO has used for 20 years, applied to a new ranking surface.
How do you measure if my site got more visible to AI?
Baseline: we run prompt panels across ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews using your category and competitor questions. We log citation rate (how often you appear), share of voice (you vs named competitors), and answer position (cited first vs buried). The audit deliverable shows your starting numbers and the gap to the top-cited competitor. After implementation, you re-run the same panel — that's the comparison. We give you the prompt list and the methodology so you can re-test any time.
Will this work for ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews?
Yes. The audit covers all five surfaces. The architecture and schema work targets the underlying signals all of them use — clean structured data, extractable Q&A and TL;DR blocks, an entity graph with proper sameAs links, and a discoverable llms.txt. Each surface weights signals differently, but the foundation is shared. We document which lever moves which surface so you can prioritize.
What's an llms.txt file and why do I need one?
llms.txt is a plain-text file you place at yoursite.com/llms.txt that tells AI crawlers what your site is about, which content to prioritize, which sections to skip, and how you want to be quoted. Think robots.txt, but written for LLMs instead of search bots. It's the cheapest AI-search win available — a small file with a measurable lift in citation accuracy. We write the custom file for your brand and structure; you upload it to your site root.
Money-back guarantee — what's covered?
30 days, full refund, no friction. If we don't deliver the four components as scoped — the audit with measured baseline, the content architecture spec, the entity + schema blueprint, and the custom llms.txt file — you get every dollar back. We do not promise specific citation counts or ranking outcomes (no honest SEO firm can — that's the AI engines' decision based on your implementation and competitive market). We promise the work, the methodology, and the deliverables.
What's the difference vs hiring an AI-SEO agency?
Agencies typically charge $4,000–$12,000 for the same scope, with a 4–10 week timeline and an AI-SEO retainer commitment. The AI-Ready SEO Bundle is a fixed-price one-time engagement done by senior SEO consultants with 20+ years of experience and an AI-search practice grounded in measured citation baselines. No retainer, no scope creep, money-back guarantee.
Do I need to provide anything?
Site URL, the list of category and competitor names you want benchmarked in the prompt panel, and a 30-minute onboarding call to gather context on your business, target audience, and current state. Everything else is on us.