1. Background and context
Traditional SEO obsessively chases keyword rankings. GEO (Generative Entity Optimization) flips that script: optimize for discoverability and quotability inside machine-generated content — the snippets, answers, and synthesized outputs that power chatbots, search assistants, and enterprise RAG systems. This case study documents a nine-month project where GEO tactics were applied to Atlas Health (a mid-market healthcare SaaS) to force organic inclusion in LLM-driven answers and syndicated AI content.
Context matters: search behavior is shifting. More queries are answered inside generative interfaces, zero-click experiences are standard, and ranking by keyword is an incomplete proxy for visibility. Atlas Health had solid organic traffic but minimal presence in conversational answers, no reliable Knowledge Panel, and negligible citation in industry summaries generated by large models. The brief: make Atlas not just found — but quoted and cited by machines.
2. The challenge faced
Atlas faced three interlocking problems:
- Low entity salience: Atlas’s brand and core claims weren’t recognized as authoritative entities across web-scale knowledge graphs and corpora used by LLMs. Poor quotability: Brand messaging was long-form and diffuse, not structured into concise, reusable “quote units” that models prefer to reproduce. Data invisibility to RAG: Proprietary docs, white papers, and core product facts were not accessible to the retrieval sources used in RAG flows and third-party agents, so generative answers omitted Atlas despite high relevance.
Result: Atlas produced search traffic but rarely appeared in assistant answers, industry summaries, or featured snippets. The business missed lower-funnel trust signals — AI-driven discovery and direct citations that drive enterprise leads.
3. Approach taken
We used GEO as a strategic overlay on top of existing SEO and content efforts. The approach had four pillars:
Entity Foundation: Establish canonical representations across the web and knowledge graphs (Wikidata, schema.org markup, organizational profiles). Quotable Units (QUs): Create and distribute microcontent — tightly worded, evidence-backed 1–2 sentence claims designed to be copied verbatim in machine outputs. RAG Integration: Make corporate research, FAQs, and playbooks retrievable by external agents via public hosting, sitemaps, and API-friendly endpoints. Signal Amplification: Seed high-authority sources and machine-visible datasets (press wires, GitHub, research archives, public datasets) and use structured data to expose provenance.
We treated the project as content engineering rather than pure marketing. The objective was measurable: increase the rate at which machine answers referenced Atlas (the “AI citation rate”), increase branded inclusion share in assistant responses, and secure presence in Knowledge Panels and Featured Snippets where possible.
4. Implementation process
We executed across five simultaneous workstreams over nine months, with two-week sprints and quarterly business reviews. Each step is prescriptive — implement these in order.
4.1 Entity groundwork (Weeks 1–6)
- Create/clean up Wikidata entry and ensure cross-links to Wikipedia drafts, with neutral, referenced statements. Publish a canonical “brand dossier” page with machine-first schema: Organization, Person, FAQPage, QAPage, and Article schemas using JSON-LD (ensure fields: sameAs, identifier, citations with URL, datePublished). Register with authoritative directories and industry data feeds: Crossref for white papers, company registries, and patent offices where applicable.
4.2 Designing Quotable Units (Weeks 3–10)
- Break down core claims into 200–350 character QUs. Each QU includes a one-line claim, a 1–2 sentence evidence note, and a permalink. Embed QUs in HTML as bolded lead sentences and in JSON-LD as Repeatable Answer objects with identifiers and citation links. Publish a “QUs API” — a human-readable index plus machine-consumable JSON endpoints (robots-allowed, no paywall).
4.3 RAG enablement and dataset seeding (Weeks 6–16)
- Expose whitepapers, PDFs, and fact sheets via a crawlable public bucket and generate an XML sitemap that highlights canonical assets. Publish data tables and CSVs with clear meta rows and schema annotations so crawlers and scrapers extract structured facts. Seed GitHub public repo with sanitized code samples, policy docs, and a README that points to the QUs API.
4.4 High-authority amplification (Weeks 8–26)
- Target syndicated platforms: PR wires that are indexed in knowledge graph pipelines, industry portals, and research aggregator feeds. Coordinate outreach to journalists with quote-ready statements (the QUs) and ask for byline-friendly use of the canonical lines. Contribute to community-curated resources (Wikidata, Hugging Face datasets) with clearly attributed entries.
4.5 Monitoring and model probing (Weeks 12–36)
- Build an LLM-monitoring rig: scripted probes against GPT-family, Bard, and Bing Chat with 150 targeted prompts across intent clusters to measure citation and verbatim QU usage. Use embedding-based fuzzy matching to detect paraphrase vs. direct quote inclusion and compute a “Quotability Score.” Set up alerts for Knowledge Panel creation and Featured Snippet capture.
5. Results and metrics
The outcomes were concrete and measurable. Below is a summary of primary KPIs achieved within nine months.
Metric Baseline 9-month Outcome Delta AI citation rate (percentage of sampled LLM answers that reference Atlas) 3% 20% +17 pp (566% relative) Quotability Score (percentage of answers using verbatim QUs) 2% 26% +24 pp Featured Snippet / Answer Box share on target queries 0% 60% of prioritized answer intents +60 pp Knowledge Panel presence None Verified panel created (Google Knowledge Panel) Panel achieved Direct lead conversions attributed to AI-driven discovery 4% of monthly MQLs 12% of monthly MQLs +8 pp Organic sessions (site-level) Baseline +18% +18%Interpretation: the biggest wins were in machine citation and quotability. The team converted Atlas into a high-signal entity and supplied the machine ecosystem with exactly the form of content machines prefer: short, attributable, and machine-accessible. That translated into more assistant-driven discovery and measurable lead growth.
6. Lessons learned
Be explicit about provenance. Machines are literal and conservative. If you want to be quoted, provide a canonical string plus an authoritative source URL. Do not rely on implicit association.
Make assets machine-consumable. PDFs and gated whitepapers are invisible to many crawlers and RAG connectors. Publish HTML or structured JSON endpoints for high-value facts.
Balance brevity and evidence. QUs must be short enough to be quoted verbatim but must link to substantiating evidence to survive fact-checking layers inside LLM ecosystems.
Coordinate editorial and engineering. This is content engineering. Legal, product, and comms must sign off on canonical claims; developers must expose APIs and structured data.
Measure differently. Traditional SERP rankings are incomplete. Develop metrics that capture machine citations, paraphrase incidence, and RAG retrieval shares.
Expect a race. Once you create clean, quotable assets, competitors will copy or rephrase them. Lock in authority by seeding high-trust datasets and maintaining a persistent, verifiable presence in knowledge graphs.
7. How to apply these lessons
Use the following checklist and tactical playbook. Execute deliberately and measure relentlessly.
7.1 Quick-start checklist (first 30 days)
- Audit entity presence: check Wikidata, Wikipedia, Crunchbase, industry registries. Create a canonical brand dossier page with JSON-LD and an open QUs API. Extract 40–100 QUs from existing assets; publish them as short claim + evidence + permalink. Generate an XML sitemap for your assets and ensure it’s discoverable by crawlers. Set up LLM probes for your top 50 intents to create a baseline measurement.
7.2 Tactical playbook (months 1–6)
Seed high-authority sources: press wires, research aggregators, industry portals, and GitHub. Embed schema.org types: FAQPage, QAPage, Article, ClaimReview when available. Expose machine endpoints: machine-readable JSON for facts, public CSVs, and API endpoints for RAG ingestion. Run outreach to journalists and curators. Provide QUs up front and request verbatim use when appropriate. Integrate with partners’ RAG pipelines: offer a verified data feed or connector to get your content inside partner retrieval layers. Continuously monitor: LLM probes weekly, Knowledge Graph changes monthly, and digital footprint audits quarterly.7.3 Advanced techniques (scale and defend)
- Use embedding spaces to cluster QUs and detect paraphrase leakage or unauthorized reuse. This gives an early signal when your QUs migrate into third-party corpora. Create canonical quote URLs with persistent identifiers (e.g., /quote/atlas-2025-claim-42) and ensure redirects and stability — machine systems prefer stable URIs. Publish small, high-quality datasets (CSV/JSON) with clear license and attribution. Machines ingest datasets; proprietary, closed-source assets do not help your discoverability. Implement a “provenance header” in your responses when API access is possible: surface canonical IDs so downstream RAG systems can attribute correctly. Invest in partnership-level ingestion: data partnerships with major platforms or third-party dataset aggregators yield persistent retrieval placement.
7.4 Guardrails and ethics
- Avoid manipulation: do not use deceptive markup or cloaking to trick crawlers. That’s brittle and risks delisting or legal scrutiny. Respect privacy: do not publish personally identifiable information (PII) in QUs or machine endpoints. Be transparent about claims: provide clear citations and evidence for each QU to prevent amplification of false or misleading statements.
Contrarian viewpoints and trade-offs
Be clear: GEO is not a substitute for good human-facing content. Here are common contrarian objections and our responses.
Objection: “Optimizing for machines will ruin user experience.” Valid concern. Counter: design QUs as an additional layer — not the sole output. Keep full-length, human-first pages intact. QUs are supplements designed to improve discoverability in machine contexts while preserving human UX.
Objection: “You’ll just teach competitors to copy our messaging.” True. But authority is defensible: maintain persistent canonical records, authoritative datasets, and high-trust placements. A single copied line on an untrusted site won’t displace a verified Knowledge Panel or a widely indexed dataset.
Objection: “This is SEO theater — ephemeral and vendor-dependent.” Partly true. Model behavior changes. That’s why GEO emphasizes durable signals (Wikidata, Crossref, public datasets, verified panels) rather than ephemeral tactics. GEO is iterative: if retrieval patterns change, adjust the connectors and evidence surfaces, not the underlying entity work.
Objection: “Isn't this manipulative of RAG?” Only if you weaponize it. The ethical path is to make truthful, citable information more accessible so models produce accurate answers with transparent provenance. The manipulative approach is publishing falsehoods with convincing markup — don’t do that.
Closing
If you still obsess about one position in traditional organic rankings, you’ll miss the new battleground: being discoverable and quotable inside machine-generated content. GEO is not magic — it’s disciplined content engineering, data hygiene, and authoritative publishing. Execute the steps above: build canonical entities, produce short, evidence-backed QUs, seed reputable datasets, and instrument LLM probes. Do that, and machines will start citing you — and those citations will drive measurable discovery, trust, and leads.
Action now: run the 30-day checklist, publish your QUs API, and schedule your first LLM probe report. If you wait, your competitors will become structured data tools for AI the go-to quote in the AI answers your prospects read first.