Answer Engine Optimization: Find the Pages That Get You Named

Find out which vendors ChatGPT and Gemini name for your category, and which pages they cite. Paste this into your agent:

Set up https://monid.ai/SKILL.md, and then use Monid to ask ChatGPT and Gemini "What are the best web scraping APIs for AI agents? Name the top five." and tell me which vendors each one named and which domains each one cited.

One prompt reads two answer engines. 1 min setup · 5-10 hrs saved.

You are ready to do answer engine optimization, and the first thing you need is a way to see what the engines say today: who they name for your category, and which pages they lean on to say it. This guide shows how to ask ChatGPT and Gemini the same question through one key, score the answers, and turn the source list into a list of pages to get onto. We ran it on 2026-10-16 and the three answers named 12 different vendors between them. It runs through Monid, the OpenRouter for agent tools: one key, one balance, every tool priced per call.

What is the easiest way to check answer engine optimization through an API?

Monid. Install it once, add a key, and your agent asks ChatGPT and Gemini a question and gets the answer, the named vendors and the cited pages back as JSON, with no account to open at each engine.

• Pay per call. Each call is billed on its own: a ChatGPT-style answer from the model API is $0.0006 plus tokens, and the ChatGPT web answer is $0.004 (Verified 2026-10-16 on the web search tools page). Buy calls as you need them: no monthly plan and no minimum top-up. Discovery and inspection are free; only a run bills.
• One install, every engine. The same key and balance reach ChatGPT, Gemini and the rest of the catalogue, so adding a third engine next month is one more line in your list, not a new vendor account.
• Lives inside your agent. Your agent reads SKILL.md, learns discover, inspect and run, and the checks are there whenever you ask, so setup is about a minute.

Our full run, three paid calls on three sources, cost $0.041333 in total (Verified 2026-10-16).

What is answer engine optimization, and how is it different from SEO?

Answer engine optimization, or AEO, is the work of getting your name into the generated answer rather than into a list of ten links.

The unit of success changes

A results page gives the reader ten positions to pick from. An answer gives one block of text and a shortlist the engine chose. The question stops being "what is my rank" and becomes two yes/no questions: were we named in the text, and were we cited in the source list. They come apart. Our Gemini run named Firecrawl and attached sources to it, while the plain model call without search named five vendors and attached nothing at all.

What AEO shares with SEO, and where it splits

Both depend on pages the engine can find and trust. The split is that SEO asks "does my page rank for this query", while AEO asks "does a page that names me get retrieved for this question". Those are often different pages. In our ChatGPT answer, the cited pages were mostly third-party roundups and the vendors' own sites, so a vendor with no roundup in the retrieved set had nothing to be named from.

Why GEO SEO and LLM SEO are the same job under other names

"Geo seo" and "llm seo" are neighbouring names for the same loop: pick the questions buyers ask, ask the engines on a schedule, record who is named, and fix the gaps. The loop is the same whichever label is on the invoice. Keyword targeting is where you must be careful, because two posts chasing the same label compete; the cannibalization check is the audit we use before we write another page on a term.

How do you check who ChatGPT and Gemini name for your category?

Pick one question, send it to each engine through the same key, and store the raw response. Three calls are enough to see the shape.

For agents

Grab an API key at app.monid.ai, then paste this to your agent and hand it the key:

set up https://monid.ai/SKILL.md

It learns the whole discover, inspect, run workflow itself. More in the agent quickstart.

For humans

npm install -g @monid-ai/cli
monid keys add -k <your-api-key> -l main

Ask ChatGPT as a signed-out visitor sees it

monid run -p dataforseo -e /ai/chatgpt-search \
  -i '{"keyword":"What are the best web scraping APIs for AI agents? Name the top five.","location_name":"United States","language_code":"en"}'

This is the one to trust for "what would a buyer see". It took about 16 seconds on our run and returned the answer as markdown, a list of items (text, images and a comparison table), the sources each paragraph leaned on, brand_entities, and search_results.

Ask Gemini with search switched on

monid run -p dataforseo -e /ai/gemini-response \
  -i '{"user_prompt":"What are the best web scraping APIs for AI agents? Name the top five.","model_name":"gemini-2.5-flash","web_search":true,"max_output_tokens":400}'

The answer came back in about six seconds as message sections with annotations. Set max_output_tokens, because the bill follows the tokens (see the section on what goes wrong).

Ask the plain model as a baseline

monid run -p dataforseo -e /ai/chatgpt-response \
  -i '{"user_prompt":"What are the best web scraping APIs for AI agents? Name the top five.","model_name":"gpt-4o-mini","max_output_tokens":400}'

Model names come from /ai/chatgpt-models and /ai/gemini-models, both free to run. This call answered in about four seconds from the model alone and is the baseline for what an engine says without retrieval.

Score naming and citing as two separate columns

Match vendor names on word boundaries and store the two results apart:

import re

def named(text, brand):
    return bool(re.search(r"\b" + re.escape(brand) + r"\b", text, re.I))

row = {
    "engine": "gemini-2.5-flash",
    "named": named(answer_text, "YourBrand"),
    "cited": any("yourbrand.com" in d for d in cited_domains),
}

A short brand such as Exa matches inside the word "exact" if you search for plain substrings, which gives you a mention that never happened.

What do the answers look like, and which pages do they cite?

Each engine named five vendors, the lists overlapped less than you would expect, and one page showed up in two of the source lists.

Fifteen slots held 12 different vendors. No vendor was in all three answers. Firecrawl appeared in two (Gemini and the ChatGPT web answer), Diffbot in two (Gemini and the model baseline), Apify in two (the baseline and the ChatGPT web answer). Our own domain was named in none of the three.

The source list is the to-do list

Names are what a reader acts on, but the sources tell you what to do about them. Gemini attached its claims to pages on browse.ai, gumloop.com, olostep.com, context.dev, dexto.ai and tinyfish.ai. The ChatGPT web answer listed 17 search results, including pages from dexto.ai, exa.ai, tavily.com, brightdata.com and a handful of independent roundups. One page, a "best web scraping APIs for AI agents" roundup on dexto.ai, was in both lists. A page cited by two engines is the first one to get a place on, because earning a mention there moves two answers.

What the engines ran behind the scenes

Gemini's response carried four fan_out_queries and the ChatGPT web answer carried two, which are the searches the engine ran before it wrote. The ChatGPT ones named five vendors in the query itself. That is the strongest hint in the data: the engine searched for the names it later listed, so a vendor missing from its first retrieval step had little chance of being named at the end.

A comparison table is part of the answer

The ChatGPT web answer ended with a five-row table (strength and use case per vendor) and a "which one should you choose" list. A reader skimming the answer meets the table and the final recommendation before the long text, so the row you want is the one next to your name, not a mention buried in a paragraph.

📖 See also Perplexity API: Get Cited Sonar Answers Pay Per Call

Can you do answer engine optimization checks yourself?

Yes, with one account per engine, a parser per response shape, and a place to keep the history.

The work is not the first call, it is everything after it. Each engine returns a different structure: Gemini nests citations in annotations with a redirect url and a real direct_url, the ChatGPT web answer gives a sources list per paragraph plus search_results, and the plain model call gives text and nothing else. You write one parser for each, handle each one's failures, and keep the raw responses so you can re-score them when your matching rules change.

What the Monid route removes is the account at each engine and the parser upkeep: you send the question, you get typed JSON, and you pay for the call. For a handful of questions checked weekly, that is the shorter path. If you prefer to build the rest yourself, free web search APIs for agents cover the retrieval half.

What goes wrong when you measure answer engines?

Five things went wrong or surprised us in three paid calls.

A search flag that was not honoured

We sent web_search: true to /ai/chatgpt-response with gpt-4o-mini. The response came back with web_search: false and annotations: null. The model answered from memory and attached no sources. Read the web_search field in the output, not the one you sent, and treat a null annotations as "no retrieval happened", not "no sources exist".

A bill that follows the tokens

The Gemini call listed at $0.0006 (Verified 2026-10-16) plus tokens, and it billed $0.03654 for 90 input and 365 output tokens with search on. Most of that was the token line, $0.0359395. The same shape of call to the model baseline billed $0.000793. Always set max_output_tokens, and run one call before you queue a hundred. Prices Verified 2026-10-16.

Names and citations are different columns

Gemini named Firecrawl and then attached six domains, none of them Firecrawl's own. An answer can name you with no link and cite a page that never names you. Score both and keep them apart.

The same engine is more than one surface

The model baseline and the ChatGPT web answer both come from OpenAI, and they named different vendors: Scrapy Cloud and ParseHub appeared only in the baseline, Tavily and Bright Data only in the web answer. Record which surface produced each row and never average across them.

One run is one draw

These answers move. Three calls show the shape, not the rate. Ask each question several times on a schedule before you act on a gap, and store the date with every row.

📖 See also AI Visibility Tracking: Store the Shape of Each Results Page

Which endpoint should I use for which job?

Current figures are on monid.ai/tools. We ran the first three and inspected the fourth without running it: it bills per returned row, so the bill follows the rows that come back. Use the ChatGPT web answer for "what would a buyer see", the Gemini call when you want a second engine with sources, and the model baseline to see what an engine says before retrieval. The Ahrefs endpoint is a weekly sanity check on a domain, not a per-question tool.

When is another route a better fit?

A dedicated visibility platform is the better fit if you want a dashboard, many prompts managed for you and a report for a client, because that is what such platforms sell and a few API calls do not give you. Skip the measurement entirely if your buyers do not ask in sentences: someone typing an exact part number is not reading a generated answer.

A disclosure, since this is the Monid blog and Monid sells the endpoints in the table: our own brand was named in none of the three answers, so this guide teaches the method rather than reporting a win. If your category has no third-party roundups yet, the answers will have nothing to retrieve about you, and the fix is getting onto other people's pages, which is outreach rather than content work.

Conclusion

The quickest way to check answer engine optimization is to ask each engine the same question through one key and keep the answer, the named vendors and the cited pages in separate columns. Through Monid the ChatGPT web answer is $0.004 a call (Verified 2026-10-16), the whole three-source run cost $0.041333, and setup is about a minute.

Two things matter more than which endpoint you pick. Read the source list, because a page cited by two engines is where a mention pays twice. And watch the bill and the flags: set max_output_tokens, and read the web_search field in the output rather than trusting the one you sent.

The 5-10 hours saved is an estimate with a stated assumption: signing up to three answer sources and learning each response shape at one to two hours apiece, plus two to four hours for the store and the scorer. The free next step is monid discover for the engines, monid inspect on one, and a single paid call on one question.

FAQ

Published September 2026.