Be the answer AI gives.

SEO, AEO, GEO, LLMO: what each one actually means

Four acronyms, one question: where does your brand show up when someone is looking? SEO earns a ranking, a blue link in a list. AEO, answer engine optimization, earns the quote: it structures passages so an answer engine can lift them straight into its response. GEO, generative engine optimization, works the retrieval layer around the page, the prompts buyers actually type, the sub-queries engines expand them into, and the third-party sources those engines cite.

GEO and LLMO are used interchangeably across the industry, and for most conversations they mean the same work. Where we do separate them: GEO shapes what gets retrieved at answer time, while LLM optimization works the memory layer above any single answer, what a model already holds about your brand in its weights and connected knowledge graphs.

They stack rather than compete. Crawlability, indexation and authority are shared foundations, so none of this replaces SEO. A brand that ranks but never gets quoted has an AEO gap. A brand that gets quoted on its own site but never appears in a head-to-head recommendation has a GEO gap. The diagnostic below finds which one you have before anyone writes a line of content.

The shift: from ranking to being mentioned

AI Overviews now appear across a large share of Google results and cut clicks dramatically, yet assistants like ChatGPT have become a top-3 discovery source for many brands while sending almost no referral traffic. The visibility battle moved: the goal is no longer only to rank, it's to be the brand AI systems mention and recommend.

Large-scale studies of AI answers show brand mentions across the web correlate with AI visibility roughly twice as strongly as domain authority, and YouTube mentions correlate strongest of all. Small, low-authority sites get cited daily. Authority still matters; presence matters more.

That is an engineering problem, and it's exactly the kind we solve: NMG has spent 15+ years building the mention, citation, and authority infrastructure that AI systems now retrieve from.

The six-phase AI visibility framework

Every engagement opens with a diagnostic scored 0–100: a baseline check (does AI know you exist at all) followed by six phases, extractability (accurate facts when asked directly), category formation (named in the right category unprompted), attribute recall (differentiators surfacing on their own), proof and trust (reviews and authority cited), competitive selection (recommended head-to-head), and amplification (consistency across every model). Each phase gets a pass, stuck, or fail, plus the specific fix.

The score's job is to locate the wall: the exact phase where your visibility stalls. A brand AI describes accurately when asked directly but never surfaces in category queries has an entity problem, not a content problem. A brand that gets named but never recommended head-to-head has a proof problem. Different walls take different playbooks, and skipping ahead burns budget on tactics whose prerequisites are not in place.

The diagnostic maps the full retrieval surface, because a single prompt fans out into 5–12 hidden sub-queries before an answer is assembled, and each engine retrieves, chunks, and cites differently. The overwhelming majority of top-cited sources are NOT shared between ChatGPT, Perplexity, and AI Overviews. Winning one engine doesn't win the others, so we score them separately.

What we engineer

Entity architecture: a canonical entity record, consistent brand identity, schema, and a complete sameAs chain across every owned surface, so AI systems know exactly who you are, what you do, and whom you serve, and stop hedging when they describe you.

Citation-ready content: extractable answers, structured FAQs, statistics with sources, and information-gain content models prefer to quote. Retrieval favors fresh, information-dense, well-structured pages: most cited pages run 350–2,000 words, cited list-style sources are overwhelmingly ones updated in the current year, and listicles and comparison pages dominate the sources for 'best X' prompts. So every commercial page opens with a direct answer, and every H2 is written as a self-contained block a model can quote whole.

Mention infrastructure: digital PR engineered for AI citation, placements in the exact publications, listicles, communities, and YouTube surfaces each engine retrieves for your category, including the pages you don't own.

Measurement: AI share of voice, mention and citation counts per platform, AI referral traffic, and AI-influenced pipeline, reported monthly, benchmarked against competitors.

AI agents run the cadence

The program is operated by a fleet of specialist AI agents, each owning one job with one data feed, one cadence, and one output. A visibility monitor reads every tracked prompt-by-engine cell daily and files drop alerts within 24 hours with a diagnosis attached. A citation agent maps which domains AI pulls answers from weekly; the gaps become PR targets. An accuracy agent traces every wrong claim about you to the source responsible and queues the correction monthly.

Nothing ships unreviewed. Every agent output passes a named human gate, a strategist, an editorial reviewer, or a technical engineer, before it becomes work. Agents draft at machine speed; humans make the calls and own the result.

The cadence is a loop: measure, prioritize, execute, ship, re-measure. Daily prompt runs feed an action queue ranked by the revenue relevance of each prompt, actions route to the team that ships them, and the same prompts re-run the next day. Leadership reads one slide a month: thirteen numbers, one trend chart per number.

We publish the full mechanics as four topic deep-dives, AEO, GEO, LLMO, and AI agents, for teams that want to go deeper before they talk to us.