# How to Get Cited by AI Answer Engines

> You get cited by AI answer engines by maximizing eight measurable citation signals — FAQ schema, answer-first structure, statistical density, heading structure, freshness, crawler access, schema coverage and author attribution — which are the same signals that make your site agent-ready for the agentic web.

> Sourcing note: the signal weights are a synthesized GEO/AEO field model, and the per-engine figures and time-to-citation windows are reported (as of 2026-06-15), not primary-confirmed. Verify every percentage and window against a primary source at build.

## GEO, defined: how AI answer engines cite the agentic web

GEO (Generative Engine Optimization) is the practice of structuring a page so AI answer engines cite it as a source in their generated answers — and on the agentic web it is the same work as making your site agent-ready. This pillar models the eight weighted citation signals, the per-engine citation behavior, and the time-to-citation windows, and ties each back to a concrete readiness step.

### GEO vs AEO vs LLMO vs SEO

- **GEO** — optimizes for being cited inside a generated answer.
- **AEO** — optimizes for direct answer engines and answer boxes.
- **LLMO** — optimizes how a brand sits in a model’s parametric memory.
- **SEO** — optimizes for ranked blue links in classic search.

See /glossary/geo and /glossary/aeo for the standalone definitions.

### AI answer engine vs AI agent

An AI answer engine cites your page in an answer; an AI agent acts using your page. Both are served by the same structured page. The actual software (OAI-SearchBot, ClaudeBot, PerplexityBot) is in /crawlers/search.

## Citation signals: the eight weighted inputs that get you cited

Eight signals, in descending weight, drive AI citation. The weighting is a synthesized field model (reported, verify each percentage against a primary source at build):

| Rank | Signal | Weight (reported) | How to implement | Maps to readiness |
|---|---|---|---|---|
| 1 | FAQ schema | ~20% | Add FAQPage JSON-LD to Q&A blocks | /agent-readiness/content |
| 2 | Answer-first structure | ~19% | Open every section with the liftable answer | /agent-readiness/content |
| 3 | Statistical density | ~16% | Name numbers, dates and figures with sources | /agent-readiness/content |
| 4 | Heading structure | ~16% | Lead every heading with its key noun | /agent-readiness/discoverability |
| 5 | Freshness | ~8% | Show dated, last-verified content | /agent-readiness/quality |
| 6 | Crawler access | ~8% | Allow AI crawlers in robots and edge rules | /agent-readiness/access-control |
| 7 | Schema coverage | ~7% | Add structured data beyond FAQ | /agent-readiness/discoverability |
| 8 | Author attribution | ~6% | Name credentialed authors with Person markup | /agent-readiness/quality |

The full machine-readable record of all eight signals lives at /geo/citation-signals.

## Per-engine citation behavior: how ChatGPT, Perplexity and Claude choose sources

Each AI answer engine cites from a different retrieval source (all figures reported for 2026, verify against primary at build):

| Engine | Primary retrieval source (reported) | Practical implication | Guide |
|---|---|---|---|
| Perplexity | Community content; Reddit reported at ~47% of citations | Community presence + answer-first pages compound fast | /geo/perplexity |
| Claude | Brave Search retrieval backbone | Brave-index inclusion gates Claude citations | /geo/claude |
| ChatGPT | Bing top-10 organic results | Classic technical SEO still feeds ChatGPT citations | /geo/chatgpt |

The ~47% Reddit figure in particular must not be published without its primary source. These engines run on the frontier models in /models.

## Time-to-citation: how fast a new page earns an AI citation

Time-to-citation varies by engine (reported for 2026, verify every window against primary at build):

| Engine | Typical time-to-citation (reported) | Why |
|---|---|---|
| Perplexity | ~2–7 days | Real-time retrieval over fresh content |
| ChatGPT | ~7–21 days | Depends on a Bing index pass and ranking |
| Claude / AI Overviews | ~14–45 days | Slower propagation, higher domain-trust bar |

Compress the window with answer-first structure, statistical density, crawler access and community co-citation.

## GEO is agent-readiness: why the same investment pays off in both channels

GEO is not a separate discipline from agent-readiness — every citation signal is also an agent-readiness signal. FAQ schema, answer-first and schema coverage are the content/discoverability checks; crawler access is the access-control check; freshness and author attribution are the quality/E-E-A-T checks. This page implements all eight signals on itself — view its .md twin and FAQPage JSON-LD. Build them at /agent-readiness/content and /agent-readiness.

## GEO measurement vs GEO engineering: read the data, then build the signals

Each citation signal maps to one concrete change on your site — and the Agent-Readiness Audit checks whether you made it. GEO measurement (Profound, Ahrefs Brand Radar, Semrush) tells you whether you are cited; GEO engineering is how you get cited. Run the Audit at /services, watch the data at /state-of-the-agentic-web, declare citable content with /protocols/discovery/llms-txt, and see the index at /.