Generative Engine Optimization (GEO) — Draft

Updated Aug 16, 2025

A practical playbook for monitoring, explaining, and improving how assistants answer questions about your brand. Treat the answer as the product and the assistant as the channel.

Why GEO

Assistants collapsed the funnel. People ask, read, and decide in the answer. If your brand isn’t included—or is described incorrectly—you lose the moment of choice.

GEO treats the answer as the product and the assistant as the channel. The job is to make your facts present, citeable, and recent—then measure how often you’re included and how you’re described.

From links to a multi‑axis answer
User
What is the best CRM for SMBs?
Assistant
For most SMBs, start with AcmeCRM or RiverSuite. AcmeCRM wins on price and onboarding (from $29/mo), and RiverSuite on native integrations (150+). If you need built‑in quoting or field kits, consider Apex.

Ranks collapse into dimensions: inclusion, position, authority, sentiment, recency.

Keywords → Concepts

Models compose concepts, not keywords. Make entities (brand, products, categories) unambiguous, attributes crisp, and claims easy to cite. When concepts are clear and corroborated, assistants reuse them—again and again.

Entity
Your brand, products, competitors
Attributes
Pricing, SLAs, integrations, limits
Use‑cases
Who it’s for; outcomes
Evidence
Case studies, reviews, docs
Comparisons
Neutral, criteria‑based tables
Recency
Changelog, release notes

Ranking was one‑dimensional. GEO is multi‑dimensional: inclusion probability across engines and personas, position within the answer, citation authority, sentiment, and Time‑to‑Knowledge as models update. Win on concepts, and the rest compounds.

The new scorecard

  • AI Share‑of‑Voice (SOV_AI) — inclusion probability weighted by engine mix and intent.
  • Coverage — % of tracked prompts where you’re mentioned/cited.
  • Citation Authority — quality/recency of sources used when you’re mentioned.
  • Time‑to‑Knowledge (TTK) — median time from site change → updated answers.
  • AI‑Attributed Pipeline — qualified pipeline influenced by AI answers/sessions.
For the curious: SOV_AI calculator (toy)

SOV_AI (toy)

29%

Adjust engine weights; inclusion % are fixed for demo (transparent below). Intent modifies impact.

Engine weights and intent
Engine weights
Intent
Inclusion (assumed for demo): ChatGPT 42% · Perplexity 33% · AI Overviews 27% · Claude 24% · Copilot 18%
Estimated SOV_AI
Demo only

Site hygiene checklist

  • Brand facts JSON‑LD (canonical, machine‑readable).
  • llms.txt policy aligned with robots.txt.
  • Comparison tables (neutral, criteria‑based).
  • Changelog/release notes (dated, linkable) for TTK.
  • Pricing and plans as structured data; single source of truth.
  • How‑it‑works pages with deep, linkable content and diagrams.
  • Third‑party citations (docs, reviews, case studies).

FAQ

Is this just SEO?
No. SERP rank was one dimension. Assistant answers are multi‑dimensional: inclusion, position, citation authority, sentiment, and recency.
Do we need a vector DB to start?
No. Start with facts, structure, citations, and a prompt pack. Add retrieval later if your app requires it.
How do engines differ?
Inclusion and citation behavior vary. Weight engines by your audience mix in SOV_AI and compare deltas over time.
Can we attribute pipeline to answers?
Treat assistant sessions like a channel: log assisted touchpoints and model influence (e.g., position‑based or time‑decay) across sessions.