How to Make B2B Pages Citable by ChatGPT and DeepSeek: A GEO Checklist
B2B GEO means writing public pages as extractable, checkable units so ChatGPT, DeepSeek, or Perplexity can cite you on buyer questions. It does not guarantee a model will name you. It only lets editors inspect whether the page has citation prerequisites. That is not the same as a custom Agent citing internal docs—see /blog/enterprise-rag-knowledge-base-agent. What a custom Agent is: /blog/enterprise-custom-ai-agent-vs-chatgpt. GeonAI writes public posts and discovery endpoints to this bar; /agents are capability references only (not a public trial of 363+ presets). Email [email protected].
Two kinds of “citation”—do not mix gates
| Object | Who cites | What you can check | Not an acceptance line |
|---|---|---|---|
| Public-page GEO | ChatGPT / DeepSeek / search overviews | Definition sentence, table, standalone FAQ, canonical, JSON-LD matching the page | "Optimize and you will be named" |
| Agent answer citations | Your own Agent | Missing-citation rate, doc_id/version, refuse without evidence | One fluent demo chat |
Agent-side knowledge, refuse, and handoff: /blog/custom-agent-more-than-a-prompt. This post covers public pages only.
One paste-ready definition
A citable B2B page = a definition near the H1 with no adjective stack + at least one labeled table or numbered gate + FAQ answers that stand alone. Without those three, extra “GEO/AIO” wording adds nothing checkable.
Editorial checklist (paste into the release ticket)
| Check | Pass line | Fail example |
|---|---|---|
| Definition | Copyable sentence in the first ~100 words under the H1 | Only “leading / empower / end-to-end” |
| Structure | At least one comparison table or numbered gate | Prose only; answers buried in metaphor |
| FAQ | Each Q&A still makes sense off-page | "See above" |
| Numbers | State conditions (sample, version, allowlist) or omit | "Average +80%" with no source |
| Schema | JSON-LD types ⊆ what the reader can see | Fake review stars, invisible offers |
| Discovery | Slug in sitemap; key entries in llms.txt; absolute URLs | Dead links, relative URLs in structured data |
| Wording | No clash with pricing or preset pages | "Public trial of all 363+" |
Selection still starts with scenario and Owner: /blog/ai-agent-selection-checklist. A well-written public page does not replace boundary tables or a pilot gate.
Discovery endpoints: only what machines can fetch
- sitemap.xml: stable URLs and lastModified; drop retired paths
- llms.txt: who you are, how to contact you, canonical entries; optional public catalog JSON
- JSON-LD: Article on posts (FAQPage only if FAQs exist); SoftwareApplication on preset PDPs that match the page—no invented reviews
- Absolute URLs: one origin; do not ship conflicting structured data for the same SKU
GeonAI discovery includes /llms.txt, /sitemap.xml, and /api/preset-agents. That is this site’s writing and discovery floor, not proof that ChatGPT already cites us.
How to sample (one chat is not a ranking)
- Pick three real buyer questions (definition, comparison, when not to buy)
- Ask ChatGPT and DeepSeek; log date and model tier
- See whether the reply extracts your definition or table—if it does, keep that block; if not, first check the page has extractable units
- Do not treat a single chat as GEO rank, and do not promise clients they will be cited
When it is worth doing
- Worth it: buyers ask models first; you already have unique boundary tables, gates, or comparisons—not vendor boilerplate
- Wait: the scene is not measurable, no content Owner, or someone is selling “we guarantee ChatGPT will name you”
- Do not swap GEO for custom delivery: external promise channels still need knowledge, tools, and handoff (/blog/from-chatgpt-chat-to-custom-agent-work)
Anti-patterns (send it back)
- Title or excerpt says “AI/GEO optimized” with no definition or table
- FAQs stuffed with keywords; answers cannot stand alone
- Treating Agent missing-citation rate as “ChatGPT cited us”
- Using 363+ preset pages as live customer case studies
How to brief GeonAI
Share three buyer questions you want extracted, current public URLs, and which numbers you will omit for lack of evidence. Email [email protected], /pricing, or Live chat. We edit pages to this checklist and deliver custom Agents—we do not sell “guaranteed citation by a named model.”
Frequently asked questions
What counts as a ChatGPT or DeepSeek citation?
The model answer repeats a checkable definition, table, or FAQ of yours, ideally traceable to your URL. One lucky chat is not a stable rank.
If we follow GEO, will we be named?
No guarantee. The checklist only inspects extractable units and discovery endpoints. Whether a model picks you cannot be a contract promise.
How is this different from knowledge-base Agent citations?
KB citations are your Agent attaching doc_id/version and refusing without evidence. GEO is public pages for external models. Keep the two gates separate.
Is an llms.txt file enough?
No. Without a definition and a table, the discovery file is only a directory. llms.txt, sitemap, and JSON-LD help; extractable body units are the core.
Do zh and en pages each need their own copy?
Facts must match; sentences should be rewritten per language, not machine-mirrored. Each URL keeps its own canonical. Do not ship two conflicting number sets in one schema.
Do the 363+ preset pages count as GEO content?
They can be indexed as capability references, but not as “the public already tried every Agent.” False wording makes extraction amplify the error.