Get Cited by ChatGPT.
Ranked by Google.

AI SEO fails when it's treated as a content bolt-on rather than an engineered programme. The order of operations matters: entity foundations before content structure, content structure before amplification, and measurement built in from day one rather than retrofitted once someone asks for proof.

Phase 1 — Entity and technical baseline audit

Two to three weeks. We assess how your brand currently presents as an entity — schema coverage, Knowledge Graph presence, author markup, and whether your existing content is even structured in a way an LLM could extract cleanly. In parallel we run a fixed set of queries against ChatGPT, Perplexity, Gemini and Google AI Overviews to establish a citation-share baseline. What you receive: an audit report scoring entity strength, schema depth and current citation share against a defined competitor set. Decision point: whether the gap is primarily technical, content-based, or authority-based — this determines the shape of the programme.

Phase 2 — Priority query and topic mapping

We map the queries and topics where being cited actually matters commercially — not every query is worth chasing. This blends classical SEO keyword research with AI-specific query patterns: comparison questions, "best X for Y" queries, and definitional questions that LLMs answer directly. What you receive: a prioritised topic map linking each priority topic to its commercial value and current citation status.

Phase 3 — Entity and schema build

We build out the structured data layer — Organisation, Person, FAQ, HowTo and Speakable schema where relevant — and address gaps in third-party entity presence. This is unglamorous, deliberate work: it's the substrate that both classical rankings and AI citation eligibility are built on. What you receive: implemented schema and a documented entity presence plan.

Phase 4 — Citation-ready content production

Existing pages are restructured and new pages produced with extractable definition blocks, comparison tables and clearly stated facts an LLM can lift without ambiguity. This work runs alongside — not instead of — solid content marketing fundamentals and the technical groundwork covered by technical SEO. What you receive: published or updated pages against the priority topic map.

Phase 5 — Measurement and iteration

We re-run the fixed query set on a regular cycle, sampling ChatGPT, Perplexity, Gemini and AI Overviews for citation share, alongside classical rank tracking. What you receive: a reporting cycle showing what moved, what didn't, and the next sprint's content and entity priorities. Book a scoping call if you want this mapped against your specific category.

AI SEO programmes vary in scale, but the components below are what separates a genuine programme from a content agency relabelling blog posts as "AI-optimised."

Entity and authority foundations

• Organisation and Person schema, including sameAs links to authoritative profiles
• Knowledge Graph and Wikidata presence assessment and build-out where warranted
• Author E-E-A-T markup — credentials, bylines and demonstrated expertise signals
• Third-party citation and mention audit to identify authority gaps

Content structure and production

• Definition blocks, comparison tables and statistic call-outs engineered for extraction
• Rewrites of existing high-value pages for citation-readiness, not just readability
• Net-new content against the priority topic map, built on solid content marketing craft
• FAQ and HowTo schema wired to genuinely answered questions rather than keyword filler

Technical and classical SEO foundation

• Site architecture and internal linking review via our technical SEO discipline
• Core crawl and indexation health, since an uncrawlable page can't be cited by anything
• Classical on-page and off-page signals that remain the backbone of both search layers

GEO and AEO workstream

• Dedicated optimisation for generative engines under our Generative Engine Optimisation service
• Featured snippet and People Also Ask targeting for classical answer-engine surfaces
• Query pattern analysis specific to how each AI engine phrases and sources answers

Measurement and reporting

• Fixed query-set sampling across ChatGPT, Perplexity, Gemini and AI Overviews
• Classical rank tracking and organic session reporting run in parallel
• Competitive citation audits showing where competitors are being quoted and you're not

Two things sit outside default scope: general brand PR and any paid placement designed to manufacture "citations" — AI engines devalue anything that looks like manipulation, and it isn't work we'll do.

AI SEO is quoted against a defined scope of content velocity, entity-building work and the GEO/AEO workstream — not a flat retainer that looks the same for every business. You should know exactly what's covered before you sign anything.

Audit-and-roadmap versus ongoing programme

An audit-and-roadmap engagement suits a business that wants clarity on its current citation gap and a prioritised plan before committing to ongoing spend. An ongoing programme suits businesses where consideration-phase content is a permanent part of demand generation — continuous entity building, content production and citation tracking, scoped in monthly or quarterly sprints.

What drives cost up

• Content volume and rewrite scope. Restructuring an entire existing content library for citation-readiness is more work than optimising net-new pages only.
• Entity-building depth. Building genuine third-party authority — beyond schema you control — takes sustained effort over quarters, not a single sprint.
• Number of tracked query sets. Sampling citation share across more topics and more AI engines increases measurement overhead.
• Category competitiveness. Categories where competitors have already invested in entity authority require more work to close the gap.

What brings cost down

• Starting with a narrower priority topic map rather than attempting every query at once
• An existing strong technical and content foundation that AI SEO can build directly on
• Reusing well-structured content rather than rewriting from scratch
• A realistic timeframe — entity authority compounds over quarters, not weeks

Budgeting for both search layers

The most common budgeting mistake is diverting the entire classical SEO budget into AI SEO, or vice versa. The two share infrastructure but aren't interchangeable — classical rankings still drive the bulk of commercial clicks today, while AI citation share is the layer being built for the next few years. We model both from the first scoping conversation rather than pitting them against each other.

The right answer depends on how much of your category's buying journey now runs through an AI answer before it reaches a search results page. Here's how the three options actually compare.

Traditional SEO retainer only

Works when: your category's research phase is still overwhelmingly classical Google search, and your traffic and conversion data back that up. A well-run SEO retainer covering technical health, content and authority remains the foundation everything else is built on — including AI citation eligibility. Breaks when: competitors start appearing in ChatGPT and AI Overview answers for your commercial queries while you're absent, because a classical-only retainer has no workstream tracking or targeting that surface. Real cost: steady classical performance, with a growing blind spot in consideration-phase visibility that won't show up in rank trackers until it's already lost.

Doing AI visibility in-house

Works when: you have a content or marketing team with genuine schema and structured-data capability, time to build a repeatable citation-tracking process, and patience for the entity-authority work to compound over quarters. Some businesses with strong existing technical teams can absorb this credibly. Breaks when: AI SEO becomes a side project squeezed between other priorities — entity authority and citation tracking both require consistent effort, and inconsistent effort produces inconsistent results. Cross-engine measurement in particular is easy to under-resource because there's no single dashboard that does it for you. Real cost: no agency fee, but internal hours that compound slower than a dedicated programme, and a real risk the initiative quietly stalls after the first few months.

Dedicated AI SEO agency

Works when: the category has genuine commercial value in consideration-phase queries, you want classical SEO and AI citation work run from one roadmap rather than two disconnected plans, and you want cross-engine measurement handled by people who already have the sampling process built. You're buying the entity-building discipline, the content-structure expertise, and the measurement infrastructure — not just extra hands. Breaks when: your category has no meaningful AI-answer presence yet and the budget would outperform sitting in classical SEO or paid media instead.

The honest test

Run your highest-value commercial queries through ChatGPT, Perplexity and Google AI Overviews today. If competitors are already being named and you aren't, that gap widens the longer it's left — entity authority is slow to build and slow to lose once established. If nobody in your category is being cited yet, there's more time, but the businesses building that authority now will be the incumbents AI engines default to later. A scoping call can tell you which situation you're actually in.

The engineering is consistent across categories. What changes is which queries matter, how citation-worthy expertise is demonstrated, and how quickly the category's research behaviour is shifting to AI answer engines.

Professional services

Buyers in legal, accounting, and consulting categories ask AI engines highly specific comparison and definitional questions before ever contacting a firm — "difference between X and Y structure," "what does a [service] typically cost in Australia." Citation-worthy content here means genuinely explanatory pages with clear definitions and named practitioner expertise, not generic service descriptions. Author E-E-A-T markup matters disproportionately, since LLMs weight demonstrated expertise heavily in professional and regulated categories.

SaaS and technology

Comparison queries — "best [category] software for [use case]," "X vs Y" — are exactly the query pattern generative engines are built to answer directly, and buyers increasingly stop at the AI-generated comparison rather than clicking through review sites. Structured comparison tables, transparent feature and pricing information, and clear category positioning are the highest-leverage content types. This is one of the categories where AI citation share is moving fastest, making it a priority sector for dedicated GEO work.

Healthcare

AI engines apply heightened caution to medical and health queries, favouring sources with clear author credentials, institutional authority, and unambiguous factual accuracy over promotional framing. Content needs to read as genuinely informative rather than marketing-led, with practitioner expertise clearly attributed. This is also a category where getting the facts wrong carries real reputational and compliance risk, so content review discipline matters as much as citation-readiness.

Ecommerce

Product-comparison and "best [product] for [need]" queries are increasingly answered by AI engines pulling from review content, category pages and structured product data. Clear product schema, genuine comparison content against alternatives, and category-level guides that AI engines can cite alongside product recommendations all matter — this sits alongside dedicated ecommerce SEO fundamentals rather than replacing them.

Trades and local services

Local and trade queries are still predominantly answered through Google's local pack and Business Profile signals rather than generative engines, so AI SEO carries less relative weight here than in consideration-heavy B2B or ecommerce categories. Where it does apply — "what does it cost to [service] in Australia," "how to choose a [tradesperson]" — clear, factual answer content still helps, but local SEO fundamentals typically deliver more return for the same investment in this sector.

Most AI SEO failures come from treating it as a content trick rather than an engineering discipline. The mistakes below are consistent across categories.

Stuffing content with AI-sounding buzzwords

Rewriting existing copy to mention "AI Overviews" and "ChatGPT" without changing the underlying content structure achieves nothing — LLMs don't cite pages because they mention AI, they cite pages because the facts are extractable. Good looks like: content restructured with genuine definition blocks, comparison tables and clearly stated facts, regardless of whether the word "AI" appears anywhere on the page.

Ignoring the technical foundation

Investing in content structure while the underlying site has crawl and indexation issues means the content can't be discovered by any engine, human or AI. Good looks like: technical health addressed alongside content work through disciplines like technical SEO, not as an afterthought.

Expecting citation lift within weeks

Entity authority compounds over quarters, not sprints — a handful of schema changes and two new pages won't move an LLM's trust in your brand overnight. Good looks like: a roadmap with realistic milestones and measurement checkpoints, not a promise of immediate citation share.

Treating GEO as separate from classical SEO

Running AI citation work through a different team with a different content plan than classical SEO produces conflicting priorities and duplicated effort. Good looks like: one roadmap covering SEO, GEO and AEO, since they share the same technical and content infrastructure.

No measurement baseline

Starting content and entity work without first sampling current citation share means you can't prove — or disprove — whether anything worked. Good looks like: a fixed query set sampled before work begins and on a regular cycle afterward, reported alongside classical rankings.

Manufacturing fake authority signals

Attempting to game entity authority through low-quality directory submissions or manipulated citations tends to backfire — AI engines, like Google, are built to discount manipulation over time. Good looks like: genuine expertise demonstrated through named author credentials, original content, and third-party mentions earned rather than engineered.