What is the difference between AEO, GEO, and LLMO?
AEO, GEO, and LLMO are three distinct disciplines that each address a different layer of how AI systems find, cite, and represent your brand. AEO (Answer Engine Optimisation) targets specific question-and-answer moments — the work involves structuring content so AI engines select it as the response to particular queries. GEO (Generative Engine Optimisation) targets brand recognition in synthesised, multi-source AI answers — it is about building the citation signals and authority patterns that cause AI engines to include your brand when answering broad category questions. LLMO (Large Language Model Optimisation) targets the internal representation of your brand within language models themselves — fixing gaps, errors, and omissions in how LLMs understand and describe your business. They share a goal but operate through different mechanisms and require different tactics.
Which discipline should we start with?
The right starting point depends on your current AI visibility baseline, which is why every engagement begins with an audit. For most UK businesses entering this space, LLMO foundational work is the highest-leverage first investment — if language models hold inaccurate or incomplete data about your brand, AEO and GEO work built on top of that will produce inconsistent results. That said, brands in highly question-driven categories (such as professional services or SaaS) often find that AEO delivers the fastest visible return, while brands competing in crowded markets where AI mentions of competitors are already frequent will prioritise GEO. The audit removes the guesswork.
Do we need all three disciplines, or can we run just one?
Most businesses see meaningful results by starting with one or two disciplines and expanding over time. You do not need all three running simultaneously from day one. What you do need is a clear understanding of how each discipline affects the others — running GEO without LLMO, for example, means that the AI-generated mentions your GEO work earns may describe your brand inaccurately. Running AEO without GEO means your direct answers earn citations but your brand does not accumulate the authority signals that determine whether AI systems recommend you in broader industry discussions. The programme architecture is designed to allow sequenced entry while keeping the longer-term integration in view.
How long does it take to see results?
AEO typically shows measurable citation rate improvements within six to ten weeks of implementation, as AI engines update their index and retrieval systems relatively quickly when high-quality direct-answer content is published. GEO operates on a longer horizon — authority and citation signals accumulate over three to six months before they translate into consistent brand mention share in AI-generated answers. LLMO results are the most variable in timeline because they depend on how frequently the relevant AI models update their knowledge retrieval systems, but entity data improvements typically begin to surface within eight to twelve weeks. A realistic view of the full programme is six months to see all three disciplines performing at a meaningful level.
What does this cost?
AI visibility programmes with Ignited Nepal are priced in GBP and structured based on the number of disciplines in scope, the competitive complexity of the category, and the volume of content and entity work required. Engagements typically begin in the range of £2,500 to £4,500 per month for a focused single-discipline programme, with integrated three-discipline programmes priced from £5,500 per month. The AI Search Audit, which establishes the baseline and prioritisation framework, is priced separately and scoped to the business. Exact pricing is confirmed after the audit.
How does this relate to our existing SEO investment?
AI visibility work does not replace traditional SEO — it extends and strengthens it. The authority signals built through GEO (high-quality editorial citations, structured entity data, topical expertise signals) also reinforce traditional ranking factors. The content architecture built through AEO (clear, well-structured, question-anchored pages) improves organic CTR and dwell time metrics. LLMO work, which involves making content more legible to machine systems, also tends to improve featured snippet capture and structured data performance in traditional search. Businesses that run AI visibility programmes alongside existing SEO typically report that both channels perform more consistently, because the underlying signal quality improves across the board.
