Three disciplines. One goal: get your brand into AI answers.

AEO, GEO, and LLMO are three distinct disciplines that address different layers of AI search visibility, and each requires a different approach to improve. AEO operates at the query level: it makes your content the direct answer when a buyer asks an AI engine a specific question. GEO operates at the category level: it builds the citation signals that cause AI engines to name your brand when generating industry or market summaries — the kind of responses that drive early-stage consideration. LLMO operates at the model level: it shapes the internal knowledge representation that AI engines hold of your company, which determines how accurately they describe you regardless of what source they are drawing from. In B2B and SaaS contexts, all three layers are active during a typical buying cycle, which is why gaps in any one of them create coverage holes at specific stages.

The right starting point depends on your current gap analysis, not a generic recommendation. SaaS companies that have recently repositioned, expanded their product, or changed their go-to-market focus should typically lead with LLMO — because model inaccuracy at this level means AI engines are describing the wrong version of your product to buyers who would otherwise be strong fits. Companies with accurate entity data but low citation share in analyst and industry source AI summaries should lead with GEO. Companies with specific high-intent evaluation queries where competitors are winning the direct answer should lead with AEO. The AI Search Audit produces this determination with data specific to your competitive situation.

All three are necessary for sustained AI visibility, but running all three simultaneously from day one is not required. The issue is the ceiling each discipline hits without the others: AEO content that lacks GEO authority signals is less likely to be trusted by AI engines as a citable source. GEO brand mentions are less valuable if LLMO inaccuracy means the brand description that accompanies the mention misrepresents the product. LLMO accuracy without AEO content means the model knows who you are but has no structured content to surface in answer contexts. A phased approach that leads with the highest-impact discipline and builds the others over six to twelve months is the most practical and cost-effective path for most US B2B companies.

AEO results are the fastest to appear. Well-structured content with appropriate schema can begin producing AI citation appearances within four to eight weeks for queries with existing search volume in your category. GEO results — appearing in category-level AI summaries generated by the major AI engines — typically require three to six months of citation signal development across US industry authority sources. LLMO results are tracked through the accuracy score and typically show measurable improvement over three to six months as entity data corrections propagate through AI retrieval and model update cycles. The programme structure delivers AEO results early, with GEO and LLMO showing progressive improvement as the programme matures.

Integrated AEO, GEO, and LLMO programmes for US B2B and SaaS companies are priced from USD 4,500 per month, depending on the scope of the audit, the breadth of question mapping and citation source development across US industry verticals, and the complexity of the LLMO entity work required for current product positioning. Companies with particularly complex product histories or recent repositioning may require additional scoping for the LLMO audit component. All programmes begin with the AI Visibility Audit, which is scoped and priced separately so you have a documented baseline and business case before committing to an ongoing programme.

Traditional SEO and demand generation share foundational inputs with AI search optimisation — structured content, authoritative external references, and clean technical implementation benefit all three channels. But AI search optimisation targets a distinct output: inclusion in generated text responses that are increasingly appearing before, above, or instead of traditional search result pages for the research queries that B2B buyers run during category evaluation. For US B2B companies, the most effective model is to run AI search optimisation as a parallel programme alongside existing SEO and demand generation, with specific KPIs — AEO citation rate, GEO mention share, LLMO accuracy — tracked separately from organic traffic and keyword ranking metrics. The programmes compound each other: strong SEO infrastructure improves AI search foundations, and AI citation authority supports organic authority over time.