Direct answer
Imagine a procurement manager asking a web-enabled assistant: “Can a software provider process scanned quotations, and who reviews the result?” This is hypothetical. A common path is: decide whether to search, turn the question into searches, find candidate pages, select text, compose an answer, and display source links. Information can be lost at each step.
The assistant does not read the entire web at the moment of the question, and not every question triggers search. OpenAI says ChatGPT Search may search automatically when current information would help, or manually when a user selects Search. This article describes a web-search response, not every text model. OpenAI: Searching the web with ChatGPT
One question can become several searches
The question has two requirements: scanned files and a human review boundary. A search for “quotation processing” may find the first while missing the second. A system can rewrite the request or expand it into subquestions.
OpenAI says ChatGPT Search can rewrite a question into targeted queries when working with search providers. Google says its AI features may use query fan-out across related subtopics. This establishes several retrieval attempts, not the exact queries, ranking scores, or source weights of one answer. OpenAI: Searching the web with ChatGPT Google: AI features and your website
A page must first be eligible to become a candidate
A search product need not fetch every page afresh. Google describes pages as discovered, crawled, and indexed before relevant results; its AI-search guidance requires supporting links to be indexed and eligible for a normal Search snippet. How Google Search works Google: AI features and your website
For ChatGPT Search, OpenAI asks publishers to allow OAI-SearchBot if they want their content included in summaries and snippets. Its separate search guidance says result placement is not guaranteed. Browser reachability, search eligibility, and answer selection are different states. OpenAI: Publishers and Developers FAQ OpenAI: Searching the web with ChatGPT
The first gate creates only a possible candidate. The system must still choose material for this question; public documentation does not provide channel quotas.
The model summarizes selected passages, not whole websites
Retrieval-augmented generation, or RAG, is a useful model for the next step. A retriever finds documents or passages, supplies them with the question, and a generative model writes from that material. The original paper combines a language model with a non-parametric document store. It illustrates an architecture, not commercial systems’ passage lengths, ranking, or orchestration. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
One company page might say “we process quotations,” while another says scans require text recognition and human review. If only the first is selected, the answer can overstate capability. Current service conditions still need checking at the official source.
A found page, a selected passage, a claim in the answer, and a displayed citation are separate observations. Do not collapse them into “the AI used the website.”
A visible citation still needs verification
Links beside an answer are visible citations, not a complete log of candidate sources. A citation can fail to support the nearby sentence, while other material claims lack support. ALCE separates citation correctness from citation completeness for this reason. Enabling Large Language Models to Generate Text with Citations
Open the source and test the wording. Does “supports scanned quotations” include text recognition first? Does “automated” omit human review? Finding a source, writing a fact, and displaying a link are different events.
The GEO role of the official site
For current company facts, the official site is the accountable reference for identity, scope, and conditions. Reporting can corroborate an event; repetition does not make a repost independent evidence. See our source-weight article for those roles.
GEO asks whether a buyer question has a public page, whether it can enter the candidate set, whether capability and conditions remain together, and whether observed answers represent it accurately. The services page states our own delivery boundaries; it is not an external assessment.
Scope and boundaries
This flow joins Google and OpenAI guidance with a RAG example. Products differ in search, indexes, passage selection, and citations. It cannot reveal internal weights or predict a citation rate.
For a material decision, inspect the original record, date, and authority. Fluent prose is not proof that a condition remains current.
Practical steps
Start with one real buyer question. Preserve its wording, time, product mode, answer, and cited URLs. Read each passage against its claim. Then inspect whether the company page gives capability and qualification without an old announcement or sales conversation. Retest under comparable conditions after a reviewed change.
Verification checklist
- Was web search enabled for the recorded answer?
- Is the target page eligible to enter the relevant search candidate set?
- Does the selected or cited material contain the capability and its condition together?
- Does each displayed citation support the claim beside it?
- Are current first-party facts and independent evidence recorded separately?
- Has a person reviewed claims that affect a public or commercial decision?
Next step
Open GEO Console can turn this into a website diagnosis: it examines public pages and buyer questions, then organizes answer coverage, citation evidence, and page gaps into priorities. It does not disclose selection scores or guarantee citation. See the Open GEO project, public projects, or contact us with a public URL and buyer question.