ChatGPT web search, Perplexity, Gemini grounded search, and Google AI Overviews are four separate answer surfaces, each with its own retrieval mechanics, and a brand can look strong on one and invisible on another for the identical prompt. OpenAI's web_search and Perplexity's Sonar run live retrieval on demand or by default, Gemini decides on its own when to ground an answer in Google Search, and Google AI Overviews lives inside the regular search results page — it can render with a citation, render without one, or not appear at all for that search. TAMGIO measures each surface independently: ongoing tracking runs on three engines — OpenAI web_search, Perplexity Sonar, and Gemini grounded search — while Google AI Overviews (via SerpAPI) is added in Audit Mode, for four measured surfaces in total. Every number carries the raw API or SERP response behind it, instead of one blended "AI visibility" score that hides which surface is actually the problem.
Four engines, four different retrieval mechanics
OpenAI web_search (gpt-4.1-mini). The model has a search tool available and calls it when it judges the question needs current information. When it does, the answer comes back with inline citations you can inspect — but the model's decision to search at all is not guaranteed on every prompt.
Perplexity Sonar. Sonar is grounded by default — every query triggers live retrieval, with no model judgment call involved. That makes Perplexity the most consistently "search-triggered" of the four, which is one reason it behaves differently from the others in raw appearance rates.
Gemini grounded search. Gemini can ground its answer in Google Search results, but — like OpenAI's tool call — grounding is a per-response decision, not a guarantee. Two runs of the same prompt can come back with different grounding behavior.
Google AI Overviews (via SerpAPI). This is the odd one out: it is not a chat answer at all, it's a block embedded directly in the Google search results page. It is deterministic per search in the sense that a given query, at a given moment, returns one specific AIO (or none) — but whether an AIO renders is entirely Google's call, and for a large share of queries it simply doesn't show up. That absence is what we log as no_surface, and it is a different fact than "the brand wasn't mentioned." A brand can be completely absent from an AIO that never appeared, or absent from an AIO that appeared and mentioned three competitors instead — those are two different problems with two different fixes.
| Surface | Answer format | Retrieval behavior | Measurement mode in TAMGIO |
|---|---|---|---|
| OpenAI web_search | Conversational answer with citations | The model calls its search tool when it judges current information is needed | Ongoing tracking |
| Perplexity Sonar | Grounded conversational answer | Live retrieval runs by default for every query | Ongoing tracking |
| Gemini grounded search | Answer that can be grounded in Google Search | Grounding is decided separately for each response | Ongoing tracking |
| Google AI Overviews | Block inside the search results page | The surface can render or return no_surface for a specific search | Audit Mode |
Why "AI search" is not one channel
Bundling these four into a single "AI visibility" number hides exactly the information a marketing or SEO team needs. A brand might have a 40% mention rate on Perplexity, near-zero on Gemini for the same set of prompts, and no AI Overview appearing for most of the local or pricing-intent queries where a competitor is cited. Three different situations, three different actions — none of which are visible in a blended score. Measuring per-surface also matches how traffic actually arrives: a citation in ChatGPT's answer, a link surfaced in Perplexity's sidebar, and a brand name inside an AI Overview snippet are three different exposure events with different click behavior.
Non-determinism is the reason single checks are unreliable
None of these four engines return the same answer every time for the same prompt. A single query to OpenAI web_search or Gemini today tells you almost nothing about tomorrow's answer, or about how the same intent is handled by ten reworded variants of that question. That's why TAMGIO's core metrics — Mention Rate, Share of Voice, Citation Rate, and Prompt Coverage — are all N-sample proxy measures: they are computed across a set of prompts and repeated samples, not a single lucky or unlucky call. No tool can honestly promise a specific percentage move, going from 60% to 90%, because the underlying answers are not deterministic; any tool that promises otherwise is smoothing over the noise for you, not measuring it.
How TAMGIO keeps the four surfaces honest
Every metric in TAMGIO carries a dashed underline — open it and you see the exact API or SERP response, the citations returned, and the timestamp behind that number, for whichever engine produced it. Errors and no_surface results are shown openly rather than dropped from the count, because silently excluding them would inflate every score. Prompts are written or AI-generated in sets of 30 to 40, spread across best-of, comparison, how-to, local, and pricing intent — the same prompt set run across all three ongoing engines (OpenAI web_search, Perplexity Sonar, Gemini grounded search) so the per-surface differences are comparable. For a faster first look, Audit Mode runs a one-time batch of roughly 20 to 30 auto-generated prompts across all four engines in a single scan and produces an audit report and PDF, without setting up ongoing tracking.
Frequently asked questions
- Is Google AI Overviews the same thing as ChatGPT search?
- No. ChatGPT web search and Perplexity are conversational answer engines that decide per query whether to retrieve live results. Google AI Overviews is a block embedded in the regular Google search results page — it's tied to a specific search, not a chat session, and it can be entirely absent from a search page (no_surface) while ChatGPT still answers the same question.
- What does 'no_surface' mean for Google AI Overviews?
- It means Google did not render an AI Overview for that specific search at all — there is no answer block to check for a mention. That's different from a brand simply not being mentioned inside an AIO that did appear; TAMGIO logs the two cases separately so a missing AIO doesn't get misread as a missing brand.
- Why can a brand rank well on Perplexity but not appear on Gemini for the same question?
- Perplexity's Sonar grounds every answer by default, so it retrieves live sources on every query. Gemini decides per response whether to ground in Google Search, so the same prompt can trigger grounding one time and not the next. Different trigger logic produces different visibility, even for identical prompts.
- How many prompt samples are enough to trust a mention rate number?
- A single query result tells you almost nothing, because none of the four engines return identical answers on repeat. TAMGIO computes Mention Rate, Share of Voice, Citation Rate, and Prompt Coverage across a full prompt set, typically 30-40 prompts, sampled repeatedly, and every number links back to the raw evidence behind it.
- Can I track all four engines from one dashboard?
- Yes, from one dashboard — with one distinction worth knowing. Ongoing tracking runs on three engines: OpenAI web_search, Perplexity Sonar, and Gemini grounded search. Google AI Overviews (via SerpAPI) is added in Audit Mode, for four measured surfaces in total. TAMGIO runs the same prompt set against each and reports every surface separately, so you can see exactly where a brand is strong, weak, or simply not surfacing.
