AI Search Optimization that gets you cited — not just crawled

SEO gets your pages ranking in the ten blue links. GEO — Generative Engine Optimization — gets your brand cited and quoted inside the answers that ChatGPT, Perplexity, Gemini, and Google AI Overviews hand back to buyers. The mechanics overlap by about 70%: strong content, clean schema, and authoritative citations help both. The differences that matter are answer-first passage structure, entity consistency across the wider web, and off-site presence on the exact sources LLMs retrieve from.

ChatGPT (including ChatGPT Search), Perplexity, Google AI Overviews and AI Mode, Gemini, Microsoft Copilot / Bing Chat, and Claude. Aphra tracks share-of-voice on all six, refreshed daily. We prioritize where your buyers actually ask — a B2B SaaS audience skews Perplexity and ChatGPT, a consumer brand skews AI Overviews and Gemini, and we tune the prompt set and citation work to match.

First meaningful signals in AI answers typically show up 45-60 days in from on-page and schema work. Off-site citation lift — the Reddit threads, G2 profiles, and third-party listicles LLMs actually pull from — takes 2-3 months to move. Anyone promising you the #1 spot in ChatGPT next week is selling you something. LLM answers are non-deterministic and personalized; the honest goal is durable, compounding share-of-voice, not a single ranking number.

Aphra runs your curated prompt set — 50 to 200 buyer-intent queries — against six AI engines every day. For each prompt we log whether your brand was cited, which URL was quoted, which competitors were quoted, and how the answer positioned you. That rolls up into share-of-voice per engine, per prompt cluster, and per competitor. You watch it live in Aphra, not in a monthly PDF.

No — GEO layers on top of good SEO, it doesn't replace it. Roughly 70% of the underlying work is shared: technical foundation, entity clarity, schema, content quality, authoritative links. What GEO adds is answer-first passage structure, llms.txt, AI crawler access, third-party citation building, and prompt-level tracking. If we run both your SEO and GEO, we don't double-charge for the shared foundation.

Roughly two-thirds of AI answer citations come from off-site — Reddit threads, G2 and Capterra profiles, industry publications, listicles, and forum answers. We map which sources each engine actually retrieves for your priority prompts, then build presence there: real reviews, targeted placements, expert commentary in publications your buyers read, and honest participation in the communities LLMs quote. No astroturfing, no spam — that gets caught and it burns the brand.

llms.txt is an emerging convention — a plain-text file at your root that gives LLMs a clean, structured map of your most important content. Adoption is still early, and it isn't a silver bullet, but for AI crawlers that respect it, it's a cheap, high-signal win. Yes, we author and maintain it, and we pair it with robots.txt review so GPTBot, PerplexityBot, Google-Extended, ClaudeBot, and OAI-SearchBot can actually access what you want cited.

Yes, deliberately — as an accelerator, not a replacement for judgment. AI handles the grunt work: crawling, prompt fan-out across engines, first-pass audits, drift monitoring, draft passages. Senior strategists own the strategy, choose the prompts and citations to chase, and rewrite every answer passage before it ships. Zero pages are auto-published. If you've been pitched an 'AI SEO' tool that ships slop to your site on autopilot, we are the opposite of that.

A senior strategist owns your account end to end — the same person on your kickoff call is on your monthly strategy call and in Aphra between them. They are supported by a specialist writer for answer passages, a technical SEO engineer for schema and llms.txt work, and an off-site citation lead for third-party placements. No junior handoff, no offshore content mill, no ticket queue.

This is common — LLMs confidently hallucinate wrong pricing, old founders, discontinued features, and fabricated integrations. Aphra runs weekly drift checks on your brand facts across all six engines. When something is wrong, we trace it back to the source (usually an outdated third-party page, a stale Wikidata entry, or a misread schema), fix the root, and where the engine supports it, submit direct corrections. It's a maintenance loop, not a one-time fix.