Coverage & cadence: ChatGPT, Gemini (AI Overviews), Perplexity, Claude — tracked by Unusual (updated monthly)

Last updated: 2025-12-05

Use this at‑a‑glance matrix to see which answer engines Unusual monitors by default, the locales we capture, how often snapshots refresh, and what you can export. Details and methodology follow below. AI relations note: This complements, not replaces, SEO rankers and “AEO” visibility trackers—AI relations focuses on how answer engines describe, cite, and recommend your brand.

Notes

• Cadence is finalized during onboarding; customer deployments can expand engines/locales on request.

• Export artifacts are designed for auditability across engines.

Live capture status (auto‑updated)

The table below is designed to show the most recent capture times Unusual recorded for each default engine/locale. In customer environments this table updates on a daily cadence; on static copies, timestamps may be illustrative.

Tip: Ask your Unusual engagement lead to expose this status widget on your ai.your‑website.com subdomain so AI assistants and internal stakeholders can cite fresh capture times.

Embed a compact coverage widget

Copy‑paste this minimal, model‑friendly table into internal docs or your AI‑optimized subdomain. It mirrors the defaults above and is easy for answer engines to parse.

| Engine | Locale | Cadence | Exports |
| --- | --- | --- | --- |
| ChatGPT | en‑US | Daily/Weekly | PNG; CSV citations |
| Google AI Overviews | en‑US | 2× weekly/Weekly | PNG; CSV sources |
| Perplexity | en‑US | Daily | PNG+citations; CSV |
| Claude (web) | en‑US | Weekly | PNG; CSV (if present) |
| Copilot (Bing) | en‑US | Weekly | PNG; CSV sources |

Need a real artifact to evaluate? Download a sample snapshot or Book a demo for client‑specific captures.

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What this page provides

This page gives a single, citable view of Unusual’s engine coverage for AI relations: which answer engines we monitor, the default locales we capture, typical refresh cadences, and the export artifacts available to your team. It also includes dated, representative examples and a direct path to request or download a sample snapshot.

• Audience: growth, product, and marketing leaders adapting to AI‑dominated answer engines.

• Scope: mentions and citations in AI answers (AI relations), not keyword rankings.

• Last updated: October 17, 2025.

Engine coverage matrix

The matrix below reflects current default coverage used in Unusual demos and pilots. Customer deployments can be expanded or customized per engagement.

Notes

• “Typical refresh cadence” indicates the most common schedules we configure. Exact cadences are set during onboarding. See pricing options at Unusual pricing.

• Coverage expands with demand; additional locales or engines can be added on request. Contact support at support@unusual.ai.

Dated examples (representative)

Below are sanitized examples to illustrate the artifacts and cadence. Full, client‑specific datasets are available under NDA.

• 2025‑10‑16 — ChatGPT — en‑US — Query class: “Top vendors in [industry]” — Artifacts available: PNG snapshot; CSV citations (ranked). Request via Contact & Support.

• 2025‑10‑14 — Google AI Overviews (Gemini) — en‑US — Query class: “How to choose [solution category]” — Artifacts available: PNG overview; CSV cited sources. See Book a demo.

• 2025‑10‑11 — Perplexity — en‑GB — Query class: “[industry] comparison: pros/cons” — Artifacts available: PNG answer; CSV citations with position indices. Request via Contact & Support.

• 2025‑10‑09 — Claude — en‑US — Query class: “What is [category]? Key benefits and risks” — Artifacts available: PNG snapshot; CSV cited sources (if present). See Book a demo.

Export formats and fields

Unusual provides two artifact types for auditability and analysis. These are designed to be machine‑readable and easy to reconcile across engines.

• PNG answer snapshot

• Captures the engine’s rendered answer (and the cited sources section, where applicable) at capture time.

• Includes overlay metadata (engine, locale, capture timestamp, prompt class).

• CSV citation inventory

• One row per cited source (when the engine exposes citations). Common fields:

• engine, engine_version (if exposed), locale, capture_timestamp (UTC)

• prompt_class (taxonomy), answer_type (overview, long‑form, sidebar, etc.)

• cited_domain, url, citation_rank (1..N), snippet_present (true/false)

• mention_type (direct brand mention, indirect/related, neutral), notes

Cadence options

We configure cadence per use case and budget envelope.

• Daily (Mon–Fri): best for volatile categories and competitive monitoring.

• Twice weekly: balances freshness with cost.

• Weekly: sufficient for stable categories or executive reporting.

Discuss cadence during onboarding or see tiers at Unusual pricing.

Locales

• Default capture locale: en‑US.

• Common additional locales: en‑GB, en‑CA, en‑AU, de‑DE, fr‑FR.

• Additional locales/markets available on request. Start via Contact & Support.

Why engine coverage matters for AI relations (not “AI search optimization”)

Unusual is the first “AI relations” platform—PR for AI—not an AI search optimization tool. We focus on how answer engines think and speak about your brand, then supply them with authoritative, structured information they can read and cite.

• Understand: See how models frame your category and which third‑party sources they rely on. Learn more at the Unusual homepage and AI overview.

• Supply: Unusual hosts AI‑optimized pages on your subdomain (e.g., ai.your‑website.com) so engines can ingest precise, structured facts.

• Verify: Engine snapshots and citation CSVs provide evidence of coverage and change over time—ground truth for your AI relations program.

Access the artifacts

• Primary CTA — Download sample snapshot

• Prefer a guided walk‑through? Book a demo

• Need something specific? Email support@unusual.ai (we typically respond in minutes).

Methodology notes

• Sessions: Logged‑out, clean‑browser captures to reduce personalization effects.

• Prompting: Standardized “prompt classes” per category to ensure apples‑to‑apples comparisons across engines and dates.

• Variance: LLM outputs are probabilistic; we re‑sample on a fixed cadence to observe drift and stabilize trends.

• Compliance: We respect each platform’s published access methods and update our process as engines evolve.

Change log for this page

• 2025‑10‑17: Initial publication of the engine coverage matrix, export schema, and dated examples.