Which AI engines Ignite covers

Which five AI chat engines does Ignite sweep, and how do they answer?

Ignite directly sweeps five AI chat engines: ChatGPT (OpenAI), Claude (Anthropic), Gemini (Google), Perplexity, and Grok (xAI). Each one is queried and its answers are sampled, so Ignite sees the actual generated response, which brands it names, and which sources it cites when it cites any. The set is fixed and declared rather than open-ended, so a reader always knows exactly which surfaces a reported number came from.

The five engines split into two answering behaviours, and Ignite records which one was in effect on each call. Some answer parametrically, from trained memory with no live web retrieval; others are grounded, retrieving and citing live sources. Each engine is called in the mode its terms allow, and its state is recorded per call rather than assumed from the brand name.

In pilot use Ignite samples on the order of a thousand-plus AI answers across the covered engines over a multi-week window. (Illustrative scale, not a benchmark and not a promise of any reader's volume.)

What is the difference between a grounded and a parametric answer?

A parametric answer comes from the model's internal memory with no live source lookup. A brand mention there reflects what the model absorbed during training and how it generalizes, which can be stale or unattributable. A grounded answer is generated after the engine retrieves live documents, so a mention there reflects what is findable and quotable on the open web right now, and it usually arrives with a traceable citation. The two describe different realities. Ignite tags each answer with the state that was actually in effect and never averages a grounded mention into a parametric one.

The grounded-versus-parametric split is also where on-site work can move the needle, so it is not cosmetic. Published research on generative engine optimization (GEO) found that adding statistics, citations, and quotations to visible content raised answer visibility on retrieving engines. That means a brand's own pages can influence grounded answers in a way they cannot directly influence a purely parametric recall. Keeping the two states separate is what lets a report say honestly that a lift showed up in grounded mentions, where fresh content can act, rather than being smeared across a metric that mixes memory with retrieval.

Aggarwal et al., GEO (KDD 2024): adding statistics, citations, and quotations to visible content raised answer visibility by up to 40%, with best-method gains of 22% in Position-Adjusted Word Count (how much of your text an answer surfaces, not website traffic or rankings) and 37% in Subjective Impression.^[1]

How does Ignite check whether a brand appears in Google's AI Overview?

Ignite checks Google's AI Overview by sampling it, not by sweeping it the way a chat engine is swept. The AI Overview is not queried as a chat engine at all. Instead Ignite samples whether the brand appears in the AI Overview for a given query, and who else is cited there, using third-party search-results data. It reports that presence with a confidence interval rather than as a single certain event. That keeps the AI Overview signal live and comparable while staying inside what a Google Search sample is permitted to do.

Cross-checked sources like the AI Overview are never folded into the five-engine chat sweep totals. Each one sits as its own provenance-tagged row, so a reader sees the signal for what it is, here a presence check from search-results data, instead of a blended score. The Bing and Copilot signals below follow the same rule.

First-party fact: Ignite samples the Google AI Overview on a weekly cadence and reports presence with a confidence interval, not as a single certain event. A figure is withheld until it is backed by repeated samples, so one lucky or unlucky reading never becomes a reported result.

How does Ignite track a brand's presence on Bing?

Ignite tracks Bing from the classic-search side, not through an AI sweep. Bing rank measures whether the brand holds a top organic rank for a query, read through Bing's official webmaster tooling. That makes it a conventional search-visibility signal rather than an AI-answer measurement, so it stays on its own provenance row instead of joining the five-engine sweep.

First-party fact: Bing enters as a classic-search signal, tracking whether the brand holds a top organic rank for a query through Bing's official webmaster tooling, read over a rolling window rather than a single check.

How does Microsoft Copilot data reach Ignite if Copilot is not swept?

Microsoft Copilot is not swept directly, so its citation data reaches Ignite only as a customer-provided export, labelled imported with its export date and never presented as a live sampled number. That is a deliberate honesty boundary: where no queryable interface exists, Ignite corroborates from an uploaded report rather than inventing a live signal.

First-party fact: because Copilot has no queryable interface, its figures enter only as an imported export stamped with its date, and like every metric they must be backed by repeated samples before Ignite reports them.

What AI engines are outside Ignite's covered set today?

Regional and local-language LLMs are outside Ignite's covered set today. Ignite does not claim to measure engines it does not actually query, so the coverage list stays the bounded set defined above. Gaps are named rather than papered over: if a source cannot be sampled programmatically, it either enters as a clearly tagged import or it is left out and said to be left out.

First-party fact: the covered set is a bounded five-engine sweep (ChatGPT, Claude, Gemini, Perplexity, and Grok) plus the Google AI Overview and Bing cross-checks. Any surface Ignite cannot sample is named as a gap rather than estimated into a number that was never backed by repeated samples.