| Category | UC Score | AU Agency Avg | Gap |
|---|---|---|---|
| Answer Architecture | 76% (12.2/16) | 29% (4.6/16) | +47 pts |
| Source Discipline | 92% (12.8/14) | 38% (5.4/14) | +54 pts |
| AI Search Surface | 100% (8/8) | 62% (5.0/8) | +38 pts |
| Local Relevance | 65% (5.2/8) | 18% (1.5/8) | +47 pts |
| Authority and E-E-A-T | 76% (10.7/14) | 51% (7.1/14) | +25 pts |
| Entity and Topic Coverage | 83% (10.0/12) | 57% (6.8/12) | +26 pts |
| Internal Architecture | 86% (8.6/10) | 63% (6.3/10) | +23 pts |
| Editorial Voice and Intent | 89% (5.3/6) | 62% (3.8/6) | +27 pts |
| Technical Foundation | 77% (9.2/12) | 68% (8.2/12) | +9 pts |
| Overall | 79.4/100 | 53.9/100 | +25.5 pts |
Why You Should Be Skeptical of This Benchmark
This is a benchmark study where we are both the rubric author and the highest-scoring entrant. That is a credibility problem worth naming before anything else. A sharp reader is already asking: which articles, which dates, which weighting, who scored them, can it be reproduced, why trust a methodology when the methodology favours the people who built it?
The audit parameters:
| Audit Parameter | Value |
|---|---|
| Total articles audited | 86 |
| Australian SEO agency domains in corpus | 22 |
| UC articles in corpus | 9 |
| Audit window | Q1 2026 , May 2026 |
| Article publication range | 2024 , 2026 (sample weighted toward most recent) |
| Rubric version | Robin Search v3.2 |
| Scoring method | Automated , regex checks, structural assertions, model-classified categories |
| Verticals beyond SEO agencies | Trades, healthcare, legal, hospitality (reference set) |
Those are the right questions. The rest of this article answers them straight. Here is what we did to mitigate the conflict:
- Scoring is automated, not human-graded. The same code runs over UC content and competitor content with no override path. There is no reviewer to bias.
- UC content goes through the same publish gate. Articles that fall below threshold are blocked and never reach the corpus. The 79.4 average reflects what survived our own filter, not a curated highlight reel.
- Sampling tilts toward agencies, not against them. Where larger agency blogs were sampled, we selected each agency's most-trafficked content, their flagship work, not their weakest.
- Weights were built on citation correlation, not opinion. Categories carrying the most points are the ones where movement in the variable consistently moved citation rates in our own tracking data.
What we did not solve:
- We chose the agencies. The 22-domain list is a judgement call. A different sampler might have produced a different industry average.
- We chose the rubric. Other valid frameworks exist for measuring content quality. This one is built specifically for AI search extraction; agencies optimising for other goals will look different through different lenses.
- We are not blind to the result. We knew the broad shape of the gap from our citation data before we ran the corpus systematically. The numbers came out roughly where the tracking already pointed.
Read on with that in mind. The pattern we are reporting is real and reproducible by anyone running a similar measurement system. Whether UC is the right partner for your business is a separate question with a separate answer.
What Does the Robin Search Audit Actually Show About AI Search Agencies in Australia?
If you're evaluating the best AI search agency in Australia, whether you call it AI search optimisation, generative engine optimisation (GEO), answer engine optimisation (AEO), or LLM optimisation (LLMO), the data shows a significant performance gap that most Australian SEO agencies are not closing. AI search optimisation is the practice of structuring web content so it gets extracted and cited by AI-powered answer engines, Google AI Overviews, ChatGPT search, Perplexity, Gemini, Microsoft Copilot, and increasingly Anthropic's Claude, rather than simply ranking in a blue-link results page.
The benchmark applies whether you are searching for a top SEO agency in Melbourne, an AI search consultancy in Sydney, a GEO specialist in Brisbane, an AEO firm in Perth, or an Adelaide-based content team that understands extraction-first writing. The structural standard is the same; the agency that meets it is the answer to all of those queries.
We built Robin Search because no existing tool measured this. It is a proprietary content intelligence system that scores articles across 9 categories on a 100-point rubric, covering how a page answers a question in the first 60 words, whether it cites primary sources from organisations like the Australian Bureau of Statistics and ACCC, and whether it carries the named-entity signals AI engines use to map topical authority across providers like Google, OpenAI, Anthropic, and Microsoft.
Across 86 articles spanning 22 Australian agency domains plus verticals including trades, healthcare, and legal, the results were stark. Australian agencies average 53.9/100. Only 5% of agency articles score 70 or above; 33% score below 50. UC's articles average 79.4/100, with 77% scoring 70 or above and zero below 50. Scores across the full corpus run from 30 to 90, the 90 is ours.
One finding cuts through the noise: non-UC articles average 4,371 words, 36% longer than UC's average of 3,206 words, yet score 25.5 points lower. Length is not the signal. Structure is.
As Google Search Central documentation describes, AI Overviews tend to prioritise structurally extractable answers over keyword density. Many of the longest articles in our corpus appear to have been written under a content system designed before AI extraction layers became measurable. That is not a moral failing, just a timing problem.
The methodology sections below cover what Robin Search measures, how the corpus was sampled, how a score breaks down in practice, and how scoring stays consistent. Skip ahead to the agency comparison table if methodology is not what you came for.
How Does Robin Search Actually Score an Article?
Robin Search scores content across nine categories on a 100-point scale, weighted by how strongly each category influences AI search citation outcomes in our tracking data. No category is graded in isolation. Each one maps to a specific behaviour AI engines exhibit when they extract, evaluate, or cite a page, behaviours we monitor weekly across ChatGPT, Perplexity, Gemini, and Google AI Overviews.
The framework was built by reverse-engineering what gets cited and what does not. We took 12 months of citation data from our own published articles, cross-referenced it against Google Search Central documentation on AI Overviews, the Search Quality Rater Guidelines, and the structural patterns described in Schema.org entity definitions. The categories that survived correlation analysis, the ones where movement in the variable consistently moved citation rates, are the categories in the rubric today.
What Does Each Robin Search Category Measure?
Each of the nine categories measures a discrete, observable content behaviour. The exact regex patterns, statistical anchors, and weighting curves stay proprietary, but the what is fully open. Below is what the rubric actually evaluates:
| Category | Max Pts | What It Measures | Why It Matters |
|---|---|---|---|
| Answer Architecture | 16 | Whether each section leads with a direct, extractable answer in the first 60 words | AI engines preferentially extract sections over full articles. A buried answer is materially less likely to be cited. |
| Source Discipline | 14 | Inline hyperlinks to primary sources (gov.au, peer-reviewed, original data) | AI crawlers verify claims by following outbound links. Tool-vendor blogs lower citation weight. |
| Authority and E-E-A-T | 14 | Named-author signal, first-party data presence, demonstrated experience | Direct mapping to Google's quality rater framework. |
| Entity and Topic Coverage | 12 | Density and diversity of named entities (people, tools, locations, regulations) | Entities are how AI engines map a page into a knowledge graph. |
| Technical Foundation | 12 | Page speed, structured data validity, JS-render parity, indexability | Crawlers cannot cite what they cannot read. |
| Internal Architecture | 10 | Hub-and-spoke linking, anchor text quality, related-article signals | Topical authority is built across pages, not within one. |
| AI Search Surface | 8 | FAQPage schema, llms.txt presence, extractable definitions |
The structured signals AI engines look for first. |
| Local Relevance | 8 | Suburb-level specificity, industry + location pairing, local entity references | Local intent is the highest-converting AI search query type. |
| Editorial Voice and Intent | 6 | Section-level intent matching (what a heading promises vs delivers) | Mismatched intent is a common reason for citation drops. |
Why Are These Categories Weighted the Way They Are?
Answer Architecture and Source Discipline carry the largest weights, 16 and 14 points respectively, because in our citation tracking they are the two variables with the highest correlation to AI extraction. Across UC's own published articles, lifting Answer Architecture from 50% to 75% lifted citation rate by roughly 2.3x within four weeks. Source Discipline carries similar weight because it directly affects whether AI engines treat your page as a citable source or a derivative one.
Technical Foundation sits at 12 points despite being the most basic category, because a fast, indexable, schema-valid page is the floor, not the ceiling. Pages that fail Technical Foundation rarely make it to citation. Pages that pass it without the higher-weight categories sometimes do, but get out-cited by structurally stronger pages.
Editorial Voice and Intent at 6 points reflects that intent matching is largely binary in practice. A section either delivers what its heading promises or it does not. Six points is enough to penalise drift across multiple sections without overwhelming structural categories that have more granular failure modes.
Why These Categories and Not Others?
Plenty of legitimate content quality dimensions sit outside the Robin Search rubric. Brand voice consistency, originality of perspective, depth of argumentation, and prose quality are not scored. Not because they do not matter, they do, but because they do not measurably correlate with AI search citation in our data. A well-written article with no quick-answer block tends not to be cited. A structurally precise article with workmanlike prose often is. Robin Search prioritises measurable extraction signals over editorial preferences. Those are different goals and we are honest about which one we are scoring.
That distinction matters when reading the corpus results below. An agency scoring 45/100 is not necessarily writing bad content. They are writing content the AI extraction layer cannot surface, which is a different problem with a different solution.
How Was the 86-Article Audit Corpus Selected?
The corpus was assembled from publicly indexed blog content on the .com.au domains of Australian SEO and digital marketing agencies, plus a smaller set of service business verticals where we needed reference data. No agency was contacted. No content was hand-picked for the result it would produce. The methodology is reproducible by anyone with access to the same domains and a structural scoring system.
How Were the 22 Australian Agency Domains Chosen?
We started with the top-ranked Australian SEO agencies surfacing for "Australian SEO consultancy" queries on Google and ChatGPT, plus specialist agencies appearing in Australian industry directories. The list deliberately spans the full quality spectrum: large established agencies (StudioHawk, Online Marketing Gurus, Prosperity Media), mid-size specialists (Rocket Agency, Shout Digital, SearchMinistry), and smaller location-led firms (SEO Melbourne, Perth Digital Edge, Dilate Digital). Including high-reputation agencies in the same corpus as smaller ones was intentional. If the structural gap shows up across the spectrum, the gap is structural, not a function of agency size.
How Were Articles Sampled Per Domain?
Where an agency had fewer than five blog articles published in the last 18 months, we audited every available article. Where an agency had a larger blog, we sampled the most recent five articles plus their two highest-traffic articles by external estimate, using public ranking signals. The aim was to bias the sample toward each agency's flagship content, not their worst, so the average reported is a reasonable representation of what their best public-facing content looks like.
This sampling choice matters: if anything, it tilts the corpus toward agencies. The 53.9 average could plausibly be lower if the sample were drawn randomly across all blog posts.
What Was Excluded, and Why?
We excluded press releases, gated case studies, podcast transcripts, and pages clearly tagged as paid or sponsored content. These have different content contracts, PR, lead-generation, paid placement, and would have skewed the sample away from the discipline we were measuring: organic content built to attract AI search citation.
We also excluded UC's own client-deliverable case studies and audit reports. The 9 UC articles in the corpus are public-facing blog content on undercurrentautomations.com, scored under the same rules as everyone else.
What Does a Robin Search Score Look Like in Practice?
A score is not an opinion, it is a tally of structural checks. The walkthroughs below show how the rubric breaks down in practice. Agency identifiers are anonymised; the scoring patterns are real and drawn directly from the audit corpus.
Anonymous Agency A, 47/100
A 4,200-word pillar-style article on local SEO for trades businesses. The agency is well-regarded, the prose is professional, and the content looks comprehensive on the surface.
Where the points went:
- Answer Architecture: 4/16. The article opened with a 240-word narrative introduction. The first answer to the headline question appeared in paragraph six. None of the five H2 sections led with a direct answer; each opened with context-setting prose.
- Source Discipline: 5/14. Outbound links pointed to the agency's own older blog posts, two Semrush landing pages, and one Wikipedia article. No inline links to ABS, ACCC, or peer-reviewed sources. Stat claims like "70% of customers search online first" had no source attached.
- AI Search Surface: 4/8. No FAQPage schema. No
llms.txton the domain. The article had FAQ-style content but it was not marked up structurally. - Authority and E-E-A-T: 9/14. Named author with a bio, but no first-party data and no demonstrated experience markers in the body.
- Entity and Topic Coverage: 7/12. 14 named entities across 4,200 words, well below the 20-entity threshold for a strong topical signal.
The remaining categories (Technical Foundation, Internal Architecture, Local Relevance, Editorial Voice) added 18 points, taking the total to 47.
The fix path is concrete: rewrite section openings, replace tool-vendor citations with primary sources, ship FAQPage schema. Most of the structural lift could happen in a one-day rebuild and would likely move the article from 47 into the high 60s.
Anonymous Reference Article, 82/100
A 2,900-word how-to article from a specialist agency operating outside the Australian benchmark sample, included as a reference point. A structurally strong article looks like this:
- Answer Architecture: 14/16. Quick-answer block at the top. Each H2 led with a direct one-sentence answer before elaborating.
- Source Discipline: 13/14. Inline hyperlinks to Google Search Central, Schema.org, and a peer-reviewed paper on retrieval-augmented generation. No tool-vendor citations.
- AI Search Surface: 8/8. FAQPage schema present,
llms.txton the domain, multiple bolded "X is Y" definitions. - Entity and Topic Coverage: 11/12. 26 named entities across 2,900 words, well above threshold.
- Local Relevance: 4/8. Lowest-scoring category. The article was global in scope with no suburb or city specificity.
The reference article is 1,300 words shorter than Agency A's 47-point piece, sourced more rigorously, and structured for extraction. The 35-point gap is all structure.
The pattern repeats across the corpus. High-scoring articles share the same five structural traits. Low-scoring articles share the absence of the same five.
How Do We Keep Robin Search Scoring Consistent and Blind?
Scoring is automated end-to-end. No human reviewer hand-grades a paragraph and assigns a number. The rubric is implemented as a set of regex pattern checks, structural assertions, and a small panel of Haiku-tier model classifications for the categories that resist deterministic measurement. The same code runs over every article, UC's, agency competitors', international references, without modification.
Why Automated Scoring Beats Human Review for Repeatability
Inter-rater reliability is the weak point of any human-graded rubric. Two reviewers handed the same article and the same scoring sheet routinely return scores that differ by 8 to 12 points on a 100-point scale, even after calibration sessions. Robin Search sidesteps the problem by removing the human from the scoring step entirely. Every score in the corpus was generated by the same deterministic pipeline running the same checks. Re-running the corpus today produces identical scores within a fractional rounding margin on the model-classified categories.
Do UC's Own Articles Get Scored the Same Way?
Yes, and this is the part most worth scrutinising. UC's 9 audited articles run through the same scoring pipeline as every other article in the corpus. There is no override, no curation, no exclusion of low-scoring UC content. UC articles that fall below threshold get blocked from publishing; they do not appear in the corpus because they do not exist as published content. The 79.4 average reflects what we ship after our own gate.
This is why we do not quote a score range like "UC averages 79–90". The 79.4 is the unweighted mean across every article we published in the last 12 months that survived the rubric. Several scored in the 70s and one early article scored 71. The UC sample was not pre-filtered for high-scoring articles.
How Do We Handle Scoring Drift Over Time?
The rubric is versioned. Every time we update a category, for example, the AI Search Surface category was reweighted in early 2026 once llms.txt adoption became measurable, we rerun the entire corpus on the new version so the comparison stays internally consistent. The scores reported in this article reflect the v3.2 rubric run on the May 2026 corpus state.
What This Measurement Does Not Claim
Robin Search does not measure whether an agency is good at delivering client outcomes. It does not measure paid search performance, client retention, or commercial track record. It measures one thing: the structural fitness of an agency's own published content for AI search extraction. An agency scoring 45 may run excellent paid campaigns. An agency scoring 80 may have weak commercial delivery. The rubric measures content infrastructure, not agency capability, and we have been careful to limit our claims accordingly.
That said, an AI search agency that cannot demonstrate AI search readiness in its own published content has a credibility problem worth acknowledging.
Why Are Australian AI Search Agencies Falling Behind?
Most Australian SEO firms built their content systems for a Google that ranked pages, not for an extraction layer that pulls answers. The shift changes the structural contract for content, and most agency systems have not been rebuilt for it yet. The two biggest measurable gaps from the corpus tell the story.
Answer Architecture is the single largest gap: UC 76% (12.2/16), AU agencies 29% (4.6/16). Across the 86 audited articles, 65% or more open with no quick-answer block in the first 200 words. That is the most common anti-pattern in the corpus. The fix is a writing-brief change rather than a tooling change: front-load the answer, then expand.
Source Discipline is the second-largest gap: UC 92% (12.8/14), AU agencies 38% (5.4/14). The pattern in low-scoring articles is outbound links pointing mostly to Semrush, Ahrefs, and the agency's own older posts, rather than to primary sources like the Australian Bureau of Statistics, ACCC Digital Platforms reports, industry.gov.au, or peer-reviewed research. AI crawlers tend to follow outbound links to verify claims; the source profile a page presents shapes how the engines weigh it. The Search Quality Rater Guidelines treat source quality as a core E-E-A-T signal, and the gap shows up in the data.
How Do Australian AI Search Agencies Compare to Each Other?
The table below shows average Robin Search scores for selected Australian AI search agencies across the articles we audited. These are content quality scores measuring how well each agency's published content is structured for AI search extraction, not a measure of commercial results or full service capability.
| Agency | Avg Robin Search Score | Articles Audited |
|---|---|---|
| UnderCurrent Automations | 79.4 | 9 |
| Rocket Agency | 58.0 | , |
| Shout Digital | 58.0 | , |
| Online Marketing Gurus | 57.0 | , |
| SEO Melbourne | 56.5 | 12 |
| SearchMinistry | 52.2 | 4 |
| StudioHawk | 51.3 | 3 |
| Authority Builders | 51.0 | 3 |
| Dilate Digital | 45.0 | 2 |
| Prosperity Media | 43.0 | , |
| Australian Agency Average | 53.9 | 39 |
Scores reflect publicly available content audited during the selected timeframe and should not be interpreted as overall agency capability.
The 21.4-point spread between UC's average (79.4) and the next-highest agency average (58.0) is the largest single-domain gap in the 22-agency corpus. No other agency in the audit window sits within 20 points of UC. The closest cluster, Rocket Agency, Shout Digital, and Online Marketing Gurus at 57–58, sits 21 to 22 points below. Whether that gap matters for your business depends on how heavily AI search figures into your acquisition channel mix. The rubric measures one thing, not everything.
Seven percent of non-UC articles in the corpus score 70 or higher. Among the agencies in our audit window, the highest average was 58.0, a meaningful spread, but a narrower range than the headline numbers suggest at first glance. According to research from ROI.com.au, 49% of Australians used generative AI in the past 12 months. The structural readiness of agency content for that audience is what the rubric measures, not commercial track record, not creative quality, not service depth.
The table below shows how content standards have shifted between eras:
| Standard | Pre-AI SEO Era | Post-AI Search Era |
|---|---|---|
| Opening structure | Hook or story | Direct answer in first 60 words |
| Section length | As long as needed | 130-170 words, self-contained |
| Source quality | Any credible blog | Primary sources, inline hyperlinks |
| Schema | Nice to have | FAQPage, HowTo, Article , required |
| Entity density | Keywords | Named entities + relational triples |
| Local signals | City-level mention | Suburb-level specificity |
| Citation surface | Whole article | Each H2 extractable independently |
Why Are Australian Agencies Getting Away With "Average" Content?
Most Australian SEO agencies are not failing on purpose. They are operating in a market where nobody is measuring well enough to expose the gap. A 53.9/100 average would not survive scrutiny in any other professional category. It survives in content because the buyer cannot evaluate the product, the producer has not been forced to update their playbook, and the feedback loop from "this content does not get cited" to "we should rebuild our process" takes 12 to 18 months, long enough for the contract to renew before the cause shows up.
Why Can't Most Clients Tell the Difference?
The typical Australian SMB hiring an SEO agency reads the agency's blog, looks at the agency's own ranking, sees professional copy and credentialed writers, and assumes content quality. None of these signals correlate with AI search citation outcomes in our tracking data. An article that reads beautifully and has zero structural fitness for AI extraction looks identical to a structurally precise one until you measure it. Most clients have never been shown the measurement.
The vendor selection process compounds this. RFPs ask for case studies, sample writing, and methodology documents, the exact artefacts an agency can produce regardless of whether their methodology actually works for AI search. Almost no Australian SMB RFP we have seen asks for AI search citation data, FAQPage schema deployment evidence, or named-entity density benchmarks. The buyer cannot ask for what the buyer does not know exists.
Why Haven't Agencies Updated Their Playbooks?
Most agencies built their content systems between 2017 and 2021, the era when 2,500-word "comprehensive guides" with backlink targeting were the optimal play. That play worked. It produced rankings, traffic, and commercial outcomes. The system is not broken; it is outdated for the new layer of search that has emerged on top of it. AI search has only existed at scale since late 2023 with ChatGPT browsing, mid-2024 with Google AI Overviews, and ramped sharply through 2025.
Updating a content system is expensive. It requires retraining writers, rebuilding briefs, restructuring legacy articles, deploying schema, and rewriting the feedback loop that tells the team whether they shipped well. Most agencies have not made that investment because their existing clients are not asking for it yet, and will not ask until the citation gap shows up in commercial pipeline.
When Does the Bill Come Due?
Gartner forecasts traditional search volume will drop 25% by end of 2026 as AI chatbots absorb query volume. AP Web Solutions reports 56-60% of Google searches now end without a click. The Australian market is roughly 12 to 18 months behind the US and UK on AI search adoption. That window is closing.
Agencies that update their content systems in the next 6 months will be measurably ahead. Agencies that wait will spend 2027 explaining to existing clients why citation rates flatlined while a competitor's took off. The conversation we have most often with prospective clients is some version of: "Our existing agency is fine, but our visibility in ChatGPT is zero. What is the bridge?"
The gap is bridgeable. It is not a tooling problem or a budget problem. It is a structural rebuild, and in our experience the timeline runs 6 to 12 weeks for a single domain, depending on the size of the content backlog and how much of it is worth keeping. The shape of the work is the same regardless of who delivers it: structural audit, content rebuild against the highest-impact gaps, schema and llms.txt deployment, and citation tracking once the rebuilt content is live.
What Should You Look For in an AI Search Agency in Australia?
The five things that separate a real AI search agency from a traditional SEO firm with new branding are measurable, not aspirational. Whether you are evaluating an AI search agency, an SEO agency, a GEO consultancy, or an AEO specialist in Melbourne, Sydney, Brisbane, Perth, or Adelaide, the same five checks apply regardless of what the agency calls itself. Each check is verifiable in under five minutes from public information, no sales call required.
Can the Agency Show Their Own AI Citation Data?
Ask any AI search agency for live citation tracking screenshots from ChatGPT, Perplexity, Gemini, Google AI Overviews, and Microsoft Copilot on their own brand queries. An AI search agency that cannot demonstrate citation in its own funnel has not validated its own playbook. The screenshots should show direct quote citations, not just brand mentions, and should cover commercial-intent queries like "best SEO agency Australia" or "AI search optimisation Melbourne". UC tracks weekly across all five answer engines and shares anonymised snapshots in initial discovery calls. If the agency cannot show this for itself, it is selling a service it has not verified internally.
Does the Agency Publish FAQPage Schema and llms.txt on Their Own Domain?
Open the agency's domain in any browser tab and view source on a recent blog post. Look for FAQPage JSON-LD schema and a llms.txt file at the domain root. Both are minimum viable AI Search Surface signals, the structural markers AI crawlers use to identify citable content. Most Australian SEO agencies do not have either deployed on their own properties despite selling AI search optimisation, GEO, AEO, and LLMO services. The audit takes 60 seconds with browser DevTools and is the single fastest tell for whether an agency practises what it sells.
Does the Agency Source-Cite Like a Journalist or Like a Marketer?
Read three of the agency's recent blog posts. Count outbound hyperlinks to government data sources: the Australian Bureau of Statistics, ACCC, industry.gov.au, Fair Work Commission, ATO, ASIC, peer-reviewed research, or primary organisational documentation from Google, Anthropic, OpenAI, or Schema.org. Compare to outbound links pointing to Semrush, Ahrefs, HubSpot, Wikipedia, or the agency's own older blog posts. The ratio is the Source Discipline tell. Agencies sourcing from primary documentation are writing for AI extraction; agencies sourcing from tool-vendor blogs are writing for keyword density.
Can They Score Their Own Content Against a Public Rubric?
Ask the agency to score one of its own recent blog posts against the Robin Search rubric described in this article, or any other publicly defensible content quality framework. Many will avoid the question, defer to "we do things differently", or score themselves favourably with no methodology behind the number. An agency that cannot grade its own work against a defined rubric is operating on instinct, which is fine for some kinds of work but a poor fit for measurement-driven channels like AI search. Ask for a category-level breakdown across the nine dimensions Robin Search uses; if those categories are unfamiliar to the agency, it is reasonable to ask what framework they use instead.
Do They Cover the Full Searchability Ecosystem, or Just Google?
The searchability ecosystem in 2026 spans Google traditional search, Google AI Overviews, ChatGPT search, Perplexity, Gemini, Microsoft Copilot, and increasingly Anthropic's Claude. A specialist AI search agency should be able to talk fluently about citation behaviour across all of them, including how Perplexity differs from ChatGPT, why Gemini favours different content patterns from AI Overviews, and what schema each engine actually parses. If the conversation is exclusively about "ranking in Google", you have a traditional SEO firm with new vocabulary, not an AI search agency. The five engines have meaningfully different extraction behaviours and any single-engine optimisation strategy is leaving citation share on the table across the other four.
The same evaluation framework holds whether you are a Melbourne tradie, a Sydney professional services firm, a Brisbane healthcare practice, a Perth e-commerce operator, an Adelaide hospitality business, or a national consultancy. The five questions are agency-agnostic by design and should produce the same red flags or green flags regardless of who you are evaluating.
What Does It Actually Take to Rank in ChatGPT, Perplexity, and AI Overviews?
Ranking in AI search engines requires a different content architecture from traditional SEO. The mechanics are not secret. Google Search Central documentation outlines the structural signals AI Overviews use. The gap is execution.
The Robin Search rubric covered earlier names the nine categories that, in