Every model reads differently.
ChatGPT, Claude, Perplexity, Google AI Overviews, and Microsoft Copilot do not return the same sources to the same question. They are trained on different data, retrieve in different ways, and cite by different rules. We tune for each — in concert, not in isolation — so your business shows up across the conversational layer rather than only in one corner of it.
Six citation surfaces.
One coordinated strategy.
Per-LLM optimisation is the work that begins after the foundational pillars — visibility audit, citation build, schema — are in place. It is the model-by-model tuning that decides whether your business is the one cited across all of them or only in one or two.
Each row below is a real platform with its own retrieval mechanics, its own training cadence, and its own quirks. We treat them individually, then coordinate the tactics so the work compounds rather than fragments.
ChatGPT (OpenAI)
Hybrid retrievalChatGPT cites from two distinct sources: its training corpus (knowledge baked in at training time) and live web browsing through SearchGPT and Bing. Older brands lean on training presence; younger or fast-moving ones lean on browsable authority. We optimise for both — establishing your entity in the kinds of sources that survive into training data while keeping the live-web profile crawlable, well-structured, and fresh enough that ChatGPT’s browsing layer pulls you in. We track what GPTBot crawls, where SearchGPT is sending traffic, and how ChatGPT’s citation surface treats your domain across personas and prompt variations.
Claude (Anthropic)
Knowledge-firstClaude has historically leaned on a deep knowledge cutoff with measured live-web retrieval, and its citation behaviour rewards genuine authority over freshness. The pages Claude surfaces tend to be the considered, well-cited, well-structured ones — Wikipedia, well-regarded industry publications, official documentation, professional bodies. We work to establish your entity in those exact source classes, and we tune ClaudeBot access so the public side of your site is fully readable while sensitive pages are appropriately gated. The reward for this work is a quieter but more reputational kind of citation — one that buyers tend to trust on first sight.
Perplexity
Real-time citationPerplexity is a search-citation hybrid: every answer comes with sources visible at the top, and the citation layer is the product. That makes it the most measurable AI surface in the stack — and the most rewarding for sites with strong, current, source-grade content. We optimise for PerplexityBot’s crawl, write the kind of clean, sourced, recent answer pages that Perplexity prefers, and structure your content so it can be quoted directly into Perplexity’s answer cards. We then track the share of citations you capture for the queries that matter to your business, query by query, week by week.
Google AI Overviews (Gemini)
Search-groundedAI Overviews — the Gemini-powered answer block at the top of Google search — pull from the same index that already ranks your pages. That makes traditional SEO a direct input to AI Overview visibility, but the citation rules differ: Overviews favour pages with answer-formatted content, structured data, declared expertise, and clear topical authority. We implement the schema, content patterns, and on-page structures that AI Overviews specifically reward, and we monitor your overview presence on the queries that matter — which terms trigger an Overview, who is cited, and what content shape gets pulled into the answer.
Microsoft Copilot (Bing)
Bing-poweredCopilot — across Windows, Microsoft 365, Edge, and the consumer chat — is grounded in Bing’s index. Bing’s signals overlap with Google’s but diverge on weighting: Bing tends to value direct keyword relevance, IndexNow submissions, and Bing Webmaster Tools verification more than Google does. The audience also matters; Copilot is increasingly the default AI surface for enterprise users in Microsoft 365 environments. We treat Bing as a first-class indexation target — submit cleanly, verify in Webmaster Tools, monitor coverage — and we tune content so that Copilot’s enterprise queries surface your business when relevant.
Emerging models & bilingual surfaces
WatchingGrok, Mistral, DeepSeek, Qwen, and the new wave of agentic browsers are not hypothetical — they are routing real buyer questions today. We add new models to your tracking set as they cross a meaningful traffic threshold, and we extend the monitoring to bilingual surfaces where it matters. For our EN · ZH clients, that means parallel coverage of how a brand is cited in English-language and Chinese-language prompts, including by the Chinese-trained models that an Australia-or-Canada-only setup typically misses entirely.
The other three pillars, in concert
Linked sub-disciplinesPer-LLM tuning sits on top of the foundational pillars — visibility audit, citation build, and schema. We coordinate the four so that what you publish for one model accrues to the others, rather than being thrown away. Click through for the full AI Search Optimisation service.
Track per model.
Coordinate the tactics.
The temptation is to optimise for the one model that happens to be in the news that month. The discipline is to monitor all of them, weigh them by your buyers’ actual usage, and choose the work that lifts more than one at a time. Our approach is built around that discipline.
One question, six answers
For every priority query, we record what each of the major models returns — the cited domains, the answer phrasing, the rank order in their respective citation surfaces. Five identical questions can produce five almost entirely different source lists. The diff between those lists is where the per-model work lives, and it is what we report on every month.
Crawler access tuned per bot
GPTBot, ClaudeBot, PerplexityBot, Google-Extended, OAI-SearchBot, Bingbot, Applebot — each has its own user-agent, its own behaviour, and its own set of opt-out signals. We configure robots.txt and meta directives so the bots you want citing you have full access, the ones you’d rather opt out of are politely declined, and the public side of your site is consistently readable across the lot.
Content shaped for each model’s preferences
Perplexity rewards source-grade clarity; Claude rewards considered authority; AI Overviews reward concise, well-structured answers; ChatGPT rewards entity presence in canonical sources. We do not write four versions of the same page — we write one that hits the overlap, and we add the specific structural patterns each model rewards. The result is content that earns citations from more than one surface for the same investment.
Bilingual coverage where the buyer is
For clients selling into both English- and Chinese-language audiences, we extend the monitoring to both linguistic surfaces and the model sets that serve each. A brand that is cited well by ChatGPT in English but invisible to the Chinese-trained models is leaving half the conversation on the table. We close that gap, deliberately, and report on it as a single coordinated picture.
Businesses being
asked about by name.
Per-LLM optimisation is most valuable for businesses whose buyers are already past the awareness stage and are actively researching providers, products, or services in the conversational layer. If your competitors are showing up in answers and you are not, this is the work that rebalances that picture.
Businesses where the model’s answer carries weight with the buyer.
- i High-consideration service firmsLaw, accounting, advisory, healthcare, migration. Buyers ask conversational questions like “best immigration lawyer in Vancouver” or “which firm handles small-business tax in Toronto” — the cited business wins the click before any ad does.
- ii Brands already cited by one model and absent from anotherThe most common audit finding is uneven coverage — strong in Perplexity, invisible in Claude, partial in AI Overviews. Per-model tuning is exactly the work that closes those visible gaps without disrupting the wins you already have.
- iii SaaS and technology companies in evaluation cyclesTechnical buyers ask AI models for vendor comparisons, integration questions, and feature-fit checks. Being cited as a recommended option in the model’s answer is a higher-intent signal than any banner placement, because the buyer is already in the decision.
- iv E-commerce and product brands during research moments“What’s the best standing desk under five hundred” or “which Canadian brand makes organic dog food” are questions models answer with a finite list. Being on the list — across multiple models, not just one — is the difference between consideration and silence.
- v Bilingual businesses serving EN and ZH audiencesIf half your buyers ask in English and half in Mandarin, you are already operating across two model ecosystems. Per-LLM optimisation across both languages is one of the few places where bilingual operators have a structural advantage worth pressing.
If your business is being asked about by name in any of these conversational surfaces, the question is no longer whether to invest in per-model optimisation but how soon to begin. Citation patterns compound — early authority is sticky and difficult for late entrants to dislodge once it has been established.
Curious how Google sees your site?
Send us your URL. We’ll send back a Premium SEO Report, prepared by hand, within 48 hours — domain authority, keyword rankings, backlinks, competitor gap, and the quick wins worth chasing first. The same report doubles as the starting point for AI search visibility, because the model layer reads many of the same signals search does.
No sales call required.
Each model has its own citation sensibility — its own taste in what counts as a good source. The work is to be a good source on each one’s terms.— The Aureole Practice —
Questions about
per-model tuning.
Per-LLM optimisation is the youngest sub-discipline in our practice and the questions that come back are pointed and specific. If a question is missing here, the contact link at the foot of the page goes to the person who would answer it.
i Why optimise per model rather than once for all of them?
ii Which models do you actively track and optimise for?
iii How do you actually measure citations across so many models?
iv Will work that lifts one model help with the others?
v How long before per-model tuning shows up in citations?
vi Do you also handle Chinese-language model coverage for bilingual brands?
Where per-LLM tuning
fits in the whole.
Model-level tuning compounds when the foundational pillars are in place. The link below returns to the parent service; the pills extend laterally to the sister sub-disciplines that share the same engagement.
Parent service
Sister sub-disciplines
Adjacent services
Find out where each
model sees you today.
We’ll run an AI visibility audit across ChatGPT, Claude, Perplexity, AI Overviews, and Copilot — and show you, model by model, where you stand and where the gaps are. No obligation, no sales department, no funnel.