Multi-Engine Optimization Strategy

Seven surfaces are tracked here, and they are not seven versions of the same thing. Six are chat models reached through an AI gateway. One — Google AI Overviews — is not a chat model at all: it is answered by a search-results provider, and it behaves differently in ways that matter for both your budget and your interpretation.

A multi-engine strategy is mostly about not building seven strategies. This tutorial covers what genuinely differs between surfaces, what does not, and how to avoid the most expensive mistake — a prompt set multiplied across engines you were never going to act on.

Step 1: Take stock of the seven surfaces

Surface	How it is queried
ChatGPT	Chat model, web-search capable when grounding is on
Perplexity	Search model — grounded on every call, returns sources by default
Gemini	Chat model, Google Search grounding when enabled
Claude	Chat model; its web search is provider-executed and needs a direct provider key
Grok	Chat model, live search when enabled
Meta AI	Chat model with no web-search capability at all
Google AI Overviews	Not a model — read from a SERP provider, carrying Google’s own cited sources

Two entries in that table drive most of the confusion downstream. Meta AI can never return a real cited URL, because it never searches; anything that looks like a citation from it is a domain the analyser pulled out of prose. And Google AI Overviews requires a SERP provider to be configured for the deployment — without one it is skipped by scans entirely, silently and without charge.

Microsoft Copilot is visible in onboarding as a disabled, clearly-labelled “coming soon” toggle. It is not scanned, and it is shown rather than hidden so nobody assumes it is being covered.

Step 2: Choose the engines this project actually needs

Engine selection is per project, in Settings → General, under AI Engines. Toggle the surfaces you want and save; the same list is set during onboarding and can be changed at any time.

Engines whose backing integration is unavailable stay selectable and are flagged honestly on the row — a missing SERP provider, an unconfigured gateway, or a model id the gateway no longer resolves. Selecting an unavailable engine cannot break or bill a scan, because the pipeline skips it server-side.

Resist the urge to select everything. The right test for each engine is: if this surface reported a problem, would we do something about it? An engine nobody will act on costs query budget every scan and adds a row you scroll past.

Nobody can tell you the correct split of effort across engines, and you should distrust any article that quotes one — reliable public figures for AI assistant usage by market and segment do not exist in a form worth planning against, and a made-up percentage is worse than no percentage. Choose from what you can observe instead: where your referral traffic comes from, what your customers say they use, and which surfaces already mention you.

Step 3: Budget for the multiplication

Every scan runs engines × prompts queries. Seven engines and ten active prompts is seventy queries per scan, on whatever cadence your plan runs. That product is what counts against your monthly allowance, so adding an engine is not a free coverage improvement — it is a proportional increase in spend on every future scan.

Two more cost facts worth knowing before you widen coverage:

• Errored answers are free. A prompt whose engine call failed is excluded from the charge and from free-tier quota, because the recorded count is delivered answers rather than attempted ones.
• Search grounding is not free. When grounding is enabled, engines that opt into a provider web-search tool bill at a multiple of the base rate. Perplexity and Google AI Overviews are not surcharged, because they search unconditionally and that is already inside their base rate.

Step 4: Read per-engine differences without over-explaining them

Open the per-engine breakdown after a scan. Expect real spread — the same question routinely produces four different shortlists across seven surfaces.

Before attributing that spread to engine “personality”, check the three structural explanations first:

1. Grounded or not. A grounded engine answered from a live search; an ungrounded one answered from what the model already carries. That single difference explains more variance than any stylistic account of an engine’s preferences.
2. A SERP surface is not a chat surface. Google AI Overviews reflects what Google’s own systems assembled from search results. Google’s guidance on AI features describes those surfaces directly, and it is a different mechanism from a chat model answering from its weights.
3. Recency. An ungrounded model’s knowledge has a cut-off; a grounded one does not. Sudden disagreement about something that recently changed is usually this.

What remains after those three is genuine engine difference — and it is a smaller residue than most strategy writing implies.

Step 5: Decide what is genuinely engine-specific work

Most of what improves visibility improves it everywhere, because every surface is drawing on overlapping public material. Do the shared work first:

• One clear, current statement of what you do and who it is for, on a page that answers a question rather than describing a product.
• Corroboration outside your own domain — listings, comparisons, reviews. Ungrounded engines lean on what was widely published; grounded ones lean on what is retrievable right now. Both reward the same thing.
• Retrievability. Crawler access is the one place where per-engine action is genuinely required, because the agents are distinct and so are the consequences. OpenAI documents its crawlers separately, and the agent that feeds search results is not the one that trains models — blocking indiscriminately can remove you from the surface you are trying to appear on.

Genuinely engine-specific work is a short list: crawler and robots.txt policy per agent, and presence on the sources a specific grounded engine keeps citing for your prompts, which the Citations page shows you per engine.

Step 6: Watch for an engine that goes quiet for the wrong reason

A surface reporting zero looks the same whether nobody mentioned you or nothing was measured. Three causes to rule out before you write a brief:

• Every call to that engine failed. Its row is dropped from your scan rather than averaged in as a zero, precisely so an outage does not read as invisibility. Check Activity for the scan job.
• The engine’s configured model no longer exists. Providers retire model ids, and a scan against a dead id returns nothing — indistinguishable from poor visibility on the dashboard. The engine row flags this state explicitly; treat it as an ops fix, not a content problem.
• Google AI Overviews never appears. No SERP provider is configured. Nothing is wrong and nothing was billed.

Step 7: Where to go next

• Multi-engine monitoring — the feature page behind this.
• Which AI engine matters most? — how to prioritise without inventing market-share numbers.
• Engine comparison guide — surface-by-surface detail.
• Optimizing Your Content for ChatGPT — the single-engine companion to this strategy.
• Setting Up Tracked Prompts — the other half of the engines × prompts equation.