Generative engine optimisation, or GEO, is the work of getting your agency, development or platform surfaced, described correctly and recommended inside answers from ChatGPT, Claude, Gemini and other AI tools. For real estate brands, this matters because buyers, tenants and investors increasingly use language models as the first filter when comparing options. By the time they reach your website, the shortlist is often already formed, and the brands inside that shortlist are the ones that AI search consistently surfaces with accurate locations, the right competitive set and a clear category position.

Answer engine optimization and SEO operate on different signals. SEO is about ranking pages on Google. AEO is about how language models describe your brand when a buyer asks for a recommendation. Google reads pages and links. Language models draw on training data, third-party citations and the consistency of how your brand is described across the web. You can hold page one on Google for your city and still be completely absent from ChatGPT, Claude and Gemini responses, and in most property markets the brands that dominate AI search are not necessarily the ones that dominate Google.

No, and this is one of the most common misconceptions in property marketing right now. AI tools do not read search engine results pages. They generate an answer from training data, from cited sources they trust and from how clearly your category, service areas and market position are described across the web. A strong Google footprint helps indirectly if it has produced authoritative external content that AI can draw on, but ranking alone does not translate into AI search presence.

The clearest starting point is to run a structured set of prompts across ChatGPT, Claude, Gemini and Perplexity covering the queries your buyers and investors actually use — neighbourhood recommendations, agency comparisons, developer reviews, best portals for a property type. If your brand does not appear in the first round of responses, or appears in the wrong context, that is a measurable gap. Our free AEO report gives you a baseline across the LLMs that matter most for your category.

This is a category signal problem. Language models draw on whatever descriptive language is most consistent across your website, third-party listings, press coverage and review platforms. When that language is vague or contradictory, AI fills the gap with generic groupings. The fix involves aligning category and positioning language across every surface where your brand appears, so the competitive set AI builds around you reflects how buyers actually choose in your market.

Yes, and it is one of the most common issues we see in real estate AI visibility. Inaccurate location attribution nearly always traces back to thin local pages, inconsistent citation data and insufficient location-specific content that external sources can reference. We rebuild the underlying signal layer, structured location content, corrected directory data, authoritative local citations, and track the change in monitored AI responses over time.

We configure the monitoring platform around your actual footprint, cities, districts, property types, client segments, and build a prompt library that covers the queries relevant to each. Larger footprints require more systematic coverage, but the methodology scales. We typically prioritise the markets and segments with the highest pipeline value first, then expand from there.

The honest answer is that it depends on how thin your current signal layer is. Brands with reasonable existing presence across reviews, press and local directories typically see measurable improvement in monitored responses within six to ten weeks of coordinated fixes going live. Brands starting from near zero should budget for a longer runway, usually three to four months before AI shortlist presence becomes consistent. Monitoring from day one is what makes the timeline legible rather than a guess.

It starts with a monitoring setup and baseline audit, we configure the platform around your brand, competitive set and the queries that matter in your market. That produces a diagnosis: where you appear, how you are described, where the gaps are and what is driving them. From there we build a 90-day roadmap and move into execution, covering content, structure, external signals and authority building. Monitoring runs throughout so every change is tracked and the roadmap is adjusted as results come in.

Some teams can run GEO in-house once the monitoring infrastructure is in place and the initial diagnosis is done. What most find difficult without external support is the ongoing execution, structured content production, citation management, external placement and the analytical layer that tells you which interventions are actually moving your AI visibility. We work in whatever model fits your team: full outsource, embedded support alongside your people, or a hybrid where we handle strategy and specialist execution while your team manages what they already own.