How to Get Cited by ChatGPT (and Other AI Models): The GEO Playbook for B2B Companies
- Patrick Wings
- Sep 2
- 5 min read
Buyers don’t just “search” anymore — they ask. Instead of paging through ten blue links, they type questions into ChatGPT, Gemini, and Perplexity and get one synthesized answer (sometimes with citations).
Your goal isn’t to “rank a page” so much as to become the sentence an assistant trusts and quotes. Your goal is to get cited by ChatGPT.
That’s GEO: Generative Engine Optimization — structuring content and credibility so AI systems can understand it, verify it, and surface it confidently.
How AI assistants decide what to cite (quick reality check)
ChatGPT Search connects to the live web and shows source links; it will search automatically for some queries (news, prices, scores), or you can trigger a search explicitly. Expect it to blend an LLM summary with web citations. OpenAI Help Center
Under the hood, ChatGPT draws on web indices (not just one). Reporting and community analyses indicate Bing’s index is a key source — and recent tests suggest Google results may also be used in some cases. Treat this ecosystem as multi-source and evolving. YoastBacklinko
Perplexity is an “answer engine” by design: it searches the web in real time and always shows citations, which makes crisp, verifiable content even more valuable. Perplexity AI
The GEO principles we use at GROWSaaS to get cited by ChatGPT
Think in two layers:
(1) be the clearest answer and (2) be the most trustworthy source.
That translates into content patterns, distribution tactics, and technical hygiene that AI systems can parse without ambiguity. Much of this overlaps with strong SEO — but you’ll bias more towards precision, recency, and quotability.
1) Build question-first pages (be quotable in 2–3 sentences)
Open with the exact question your buyer asks (“What is RBAC?”, “How do SOC 2 controls map to ISO 27001?”). Then give a 2–3 sentence, neutral answer before you expand. Assistants can lift that micro-summary; humans can keep reading.
Structure blueprint
H1 that mirrors the query
Two-sentence answer
Sections: Key concepts → Practical example → Pitfalls → Optional “How we do it in [Your Product]” (clearly labeled)
3–5 question mini-FAQ
Tone tips: concrete nouns, minimal fluff, consistent naming for features and metrics.
2) Publish “data cards” for canonical facts (give models crisp atoms)
LLMs love compact, machine-friendly facts: limits, SLAs, supported standards, pricing approach, version numbers. Make them easy to parse and easy to update.
Data card contents
SLA: 99.9% monthly; credit policy summary
Auth: SAML 2.0, SCIM; SOC 2 Type II (link to report overview)
Pricing approach: per-seat + usage; typical seat range
“Last updated” + short changelog
Markup: keep tables small, bullets clean; avoid heavy UI widgets.
Process: update the card first; link other pages to it to avoid contradictions.
3) Craft balanced comparisons that read like buyer briefs
When users ask “X vs Y,” assistants prefer criteria-driven, neutral pages.
Criteria chips: integrations, security posture, pricing model, buyer fit, deployment effort
“Choose A if… / Choose B if…” scenarios (no dunking)
Link to both vendors’ primary docs for verification
4) Earn trust signals beyond your site (E-E-A-T, but for AI)
Assistants weigh authority and credibility: named authors, clear expertise, real outcomes, third-party validation.
Author identity: bio + credentials + LinkedIn link
Customer proof: metric-backed quotes and dated case studies
Security & architecture: public pages that summarize audits, certs, data flows
Digital PR: guest posts on reputable industry sites, inclusion in relevant “best” lists, awards, and visible reviews on marketplaces (G2, Capterra). These are classic reputation signals that also help models decide who’s safe to cite.
5) Increase engagement with interactive experiences (and keep them light)
Engagement patterns (time on page, pogo-sticking reduction) still correlate with perceived value. Use lightweight interactivity that adds clarity without burying answers. Examples: a calculator, a short self-assessment, or a collapsible FAQ — plus internal links that guide readers to the next useful page.
Micro-quizzes/polls that confirm use case fit
Clean internal-link “trails” (Definition → Comparison → Implementation → Case study)
Don’t over-script: heavyweight components can hide headings and confuse parsers
6) Keep pages fast and parseable
Great answers still get skipped if the DOM is bloated or mobile janky. Aim for simple, accessible HTML with clear headings — and compress what you can. Keep a quarterly pass on Core Web Vitals and trim unnecessary JS.
Compress images (WebP), lazy-load below the fold, minimize CSS/JS
Stabilize canonical URLs; avoid interstitials over your best answers
Show publish and last-updated dates prominently
7) Stay current — freshness is a tiebreaker
Models favor recent, reliable sources when facts change quickly. Institute a content recency cadence: refresh definitions, add dated examples, and expose change notes. This is simple to operationalize and disproportionately increases your chance of being cited.
Quarterly update pass on top pages
Add “What changed” notes with dates
Version long-form guides and pricing explainers
8) Don’t ignore Microsoft Bing (and Webmaster Tools)
A lot of ChatGPT’s web lookups intersect with Microsoft’s ecosystem. Submitting to and optimizing for Bing broadens your visibility in sources assistants may pull from. At minimum, set up Bing Webmaster Tools, verify the site (DNS or XML), and use URL Submission on key pages.
Bing essentials (fast lane)
Add/verify site in Bing Webmaster Tools (import from GSC or verify via DNS/XML)
Ensure sitemaps are discoverable; keep them fresh
Monitor indexation and fix crawl blockers on your answer pages
Writing that LLMs (and humans) love to get cited by ChatGPT
Front-load the answer. Two-sentence definition first.
One idea per paragraph. Short sentences, descriptive headings.
Concrete over fluffy. “SAML SSO with SCIM” beats “enterprise-grade identity.”
Consistent naming. Use the same feature/metric labels everywhere.
Give quotable lines. A crisp 25–40-word insight the model can lift verbatim.
Technical schema & markup (use sparingly, where it fits)
Add JSON-LD for FAQPage, HowTo, Product/SoftwareApplication, and Organization on the right pages.
Keep the DOM clean so headings/bullets are obvious.
Ensure your robots, canonicals, and sitemaps don’t fight each other.
Measurement: treat AI visibility like a product KPI
Set up a repeatable test of 25–50 buyer questions. Quarterly, ask major assistants and log:
Were you mentioned/cited?
Which page did they pull from?
What wording did they lift?
What change would have earned the mention?
Complement this with web analytics (assistant referral patterns), branded-query lift after distribution pushes, and new backlinks from third-party roundups you seeded.
A pragmatic 30-60-90-day rollout
Days 1–30 — Foundation
Map the top 50 buyer questions and cluster them
Ship 5 pages: definition, comparison, implementation guide, benchmark, data card
Set up Bing Webmaster Tools; submit priority URLs; add visible dates and author cards
Days 31–60 — Distribution & credibility
Pitch 2–3 guest posts on reputable industry sites
Secure inclusion in 2+ relevant listicles/roundups; pursue 10 high-fit reviews on marketplaces (G2/Capterra)
Add a basic security/architecture page and at least one diagram
Days 61–90 — Optimization & measurement
Run your prompt test across assistants; triage gaps
Improve CWV, simplify heavy components, expand FAQs where models struggled
Publish a short study/benchmark with method + date (prime citation material)
What about limitations?
No assistant is perfect. Expect occasional inaccuracies, over-literal readings, and gaps (especially on fast-moving topics). That’s another reason to publish dated facts and changelogs — they help both humans and models assess recency and reliability.
The takeaway
GEO isn’t a bag of hacks; it’s disciplined content and credibility ops. Lead with a clean, quotable answer, prove you’re reliable, keep facts fresh, and distribute those facts across the web so assistants see them often and in context. Do this consistently and you’ll show up more often in AI answers — with the right message and the right proof behind it.
Want this done for you?
GROWSaaS helps B2B SaaS and tech teams operationalize GEO: question-first content, canonical data cards, distribution/PR, and quarterly AI-visibility testing. We’ll map your top 50 questions, ship five citation-ready pages in the first month, wire up Bing Webmaster Tools, and set you up with a simple measurement loop. Then we keep the flywheel turning.
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