AI SEO Services: Be the Brand AI Assistants Recommend

It's different enough to matter, though there's heavy overlap. Traditional SEO optimises for ranked lists of links; AI SEO optimises for synthesised answers that may or may not link to you. Different ranking signals carry different weights — depth and structure matter more, exact-match keyword density matters less, citation-worthiness (do other sources reference you?) matters enormously. The fundamentals of good content overlap, but the tactical layer is genuinely distinct.

Three metrics. First, citation count — how often your brand or content is cited in AI assistant responses for priority queries. Second, citation quality — are you cited as the primary source, secondary, or footnote? Third, downstream traffic — clicks, sessions, and conversions attributed to AI referral sources (which now show up in GA4 as "chatgpt.com," "perplexity.ai," "gemini.google.com," etc.). Tools like Profound and Otterly automate citation tracking; GA4 handles the traffic side.

Almost always let them in — at least the major ones (GPTBot, ClaudeBot, PerplexityBot, Google-Extended, Bingbot/CCBot). If AI assistants can't crawl your site, they can't cite you. The only reason to block them is if you have a genuine commercial issue (e.g., a paywalled archive you don't want feeding free LLM responses). For 95% of brands, blocking AI crawlers is a strategic mistake.

Some traffic, yes — informational queries that used to drive blog clicks will increasingly resolve in AI without a click-through. But high-intent commercial queries still drive clicks: people researching a purchase want to verify, compare, and ultimately visit a brand site before deciding. The brands that get cited by AI become the ones prospects click through to verify. Brands that don't get cited disappear from consideration entirely.

Faster than traditional SEO in some ways, slower in others. Schema and content structure changes can affect AI extraction within days as models recrawl. Building enough authority and citation density to consistently appear in AI answers usually takes 2—4 months of focused work. Wikipedia / Wikidata entity work has the longest lag (3—6 months) but the highest ceiling.

llms.txt is a proposed standard (similar to robots.txt) for telling LLMs how to interpret your site — pointing to canonical content, key documentation, and structured summaries. Adoption is still early. I implement it for clients where it makes sense, but it's not a magic bullet. The real work is making your existing pages parseable, not adding a sidecar file.

Yes. LLMs heavily weight Reddit, Quora, and niche community discussions as ground-truth signals — meaning a strong, organic presence in relevant communities can move the needle on AI citations significantly. I help craft strategy that's compliant with each community's rules (no spam, no astroturfing) and earns genuine engagement.

Yes — and it's usually cheaper and more effective than buying them separately. The Authority Build package covers GEO + AEO + LLM SEO + content optimisation as a single 90-day engagement, integrated with whatever traditional SEO work you're already doing. See packages.