Answer Engine Optimization: We Asked Four Surfaces the Same Six Questions

Copy this line to your agent to read what the answer engines say about your category.

set up https://monid.ai/SKILL.md and ask google ai mode and perplexity the same ten questions about my category, then tell me which vendors each one named

On 2026-09-28 we asked four AI answer surfaces the same six questions and read all 24 answers. They named 20 distinct vendors between them. On two of the six questions there was not a single vendor that all four surfaces agreed on. Our own domain appeared in zero of the 24. Answer engine optimization is usually discussed as if there were one answer to get into, and the measurement says there are at least four, they disagree, and they disagree most on exactly the questions where a buyer is closest to choosing. This guide runs through Monid, the OpenRouter for agent tools.

What is answer engine optimization?

Getting named in a generated answer rather than ranked in a list of links.

The mechanical difference from SEO

A search result page gives ten positions and the reader picks. A generated answer gives one paragraph and a shortlist the engine picked. There is no position two to settle for, which is why the metric changes from rank to two yes/no questions: were we named, and were we cited.

Those two come apart. Being named means the answer text contains you, which is what a reader acts on. Being cited means a link is attached, which is what a reporting tool counts. An answer can name a vendor with no link and cite a source it never names.

What you are optimizing

Not a page's position, but the odds that retrieval pulls a document saying what you want said, and that generation keeps your name in the summary. In practice that means third-party pages that compare you, forum threads that mention you, and your own pages being specific enough to quote.

Why a single number is the wrong shape

Had we run only one of the four surfaces, the vendor list we came away with would have been different in every case, and on two questions there was no overlap at all. Any report saying "the AI recommends X" without naming the surface is reporting one draw from a distribution.

📖 See also SERP History: Track the Page's Shape, Not Just Your Rank

What did four surfaces actually answer?

Twenty four answers, one day, six questions from our own question registry.

Three things in that table are worth more than the zeros in our own column.

The citation counts differ by a factor that is not noise. Sonar attached 84 distinct source domains, the consumer app 55, Exa 37, and the AI Mode endpoint returned an empty reference list on all six. On that surface we could measure who got named but not who got linked, which is a limit of what came back rather than a statement about what Google shows a person.

The latency differs by more than a factor of twenty. Sonar answered in about three seconds, the consumer app in 36 to 92. That decides whether you poll nightly or hourly, and it is why the two Perplexity surfaces need separate treatment rather than being treated as one engine.

The vendor lists overlap much less than you would guess.

The question where all four disagreed

"What tools are similar to People Data Labs?" All four answered it. Five vendors named across the four answers, and the number common to all four was zero:

Perplexity Sonar API  : people data labs, zoominfo, clay, apollo
Exa answer            : people data labs
Google AI Mode        : people data labs
Perplexity consumer   : zoominfo, apollo, diffbot

The consumer app never named People Data Labs in an answer about tools similar to People Data Labs.

The question where they agreed most

"What are the best alternatives to Apify?" Thirteen vendors named, five present in all four. Commercial comparison questions have enough third-party pages behind them for retrieval to converge, which is the same reason our Apify alternatives guide is the one piece of ours this shape of query reaches.

Who won, across all 24

Apify was named in 18. Firecrawl and Bright Data in 10 each, Octoparse 7, Browse AI 6, ScraperAPI 6. We were named in zero.

An independent read agrees. Ahrefs counts how many AI answers cite a domain across eight platforms, and the same day it gave monid.ai 1, firecrawl.dev 198, apify.com 465 and brightdata.com 541. Different method, different sample, not our code, same direction.

Is GEO SEO the same thing?

Mostly yes, and the disagreement is about scope rather than substance.

The three names in circulation

Answer engine optimization is the oldest and broadest: be the answer, not a link. It predates generated answers and originally covered featured snippets and People Also Ask. Generative engine optimization, shortened to GEO, narrows it to engines that generate prose, and carries the volume attached to "geo seo". LLM SEO is the same idea named after the component rather than the surface, with the loosest definition of the three.

Where the terms genuinely diverge

One place. AEO in its older sense includes the zero-click features on a normal results page, and those still respond to schema markup and a question-shaped heading. GEO in the narrow sense barely responds to markup, because the retrieval step reads text rather than parsing your structured data. If someone sells you schema as a GEO service, that is the distinction they are eliding.

Otherwise the work is identical: find the questions buyers ask, ask them of the engines on a schedule, record who got named, and go earn third-party pages for the questions you lost. Which is why the older SERP tracking routine is the right skeleton to extend rather than replace.

The practical consequence for your content plan

Write one set of pages, measure on more than one surface, and do not build two teams. What you should split is the keyword targeting: we put "answer engine optimization" here and "ai visibility tracking" in the SERP history guide rather than stuffing both into one page, after an audit of our own 131 guides found 51 pairs competing with each other.

📖 See also Keyword Cannibalization: How to Check Whether Your Pages Fight

How do you measure whether an answer engine names you?

Four steps, and the second one is the one people skip.

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

Step 1, write the questions down once and freeze them

A question set you edit between runs produces a trend line that measures your editing. Ours is a file with 60 questions, a version stamp, and a source note per question saying where the phrasing came from, because "best web scraping API" as typed by a marketer and as typed by a developer on Reddit are different queries with different answers. Give each one a priority: we run all 60 for a baseline and a 6-question subset when we want to read the prose rather than count.

Step 2, ask more than one surface, and never average them

monid run -p mrscraper -e /google/ai-mode \
  -d '{"keyword":"best web scraping api for ai agents","country":"us"}'

Then the same question somewhere else:

monid run -p cloro -e /perplexity/ask \
  -d '{"prompt":"best web scraping api for ai agents","country":"US"}'

Two answers, two surfaces, two rows in your store. The mistake is combining them into one "AI visibility" percentage, because the surfaces disagreed on four of our six questions. A blended number hides the only actionable fact in the data, which is which surface you are losing on.

Step 3, score naming and citing separately

Look for your brand in the answer text and your domain in the source list, and store both. Two warnings from our own run.

Match on word boundaries. Our first pass matched vendor names as substrings and found "exa" inside the word "exact", producing a mention that was never there. A short brand name will do this to you every time.

Do not assume a source list exists. The AI Mode endpoint returned an empty reference array on all six questions, so a pipeline scoring only citations would have recorded six blanks on a surface that was answering perfectly well.

Step 4, take a second read you did not compute yourself

monid run -p ahrefs -e /site-explorer/ai-responses-count \
  -q '{"target":"monid.ai","mode":"subdomains"}'

That counts AI answers citing a domain across eight platforms, a completely different method from asking questions yourself. When your own sampling and a third-party count point the same way, the finding is probably real. When they disagree, your question set is probably the problem.

Which endpoint reads which surface?

Every row was verified with monid inspect on 2026-09-28, and the latency column is what we actually waited. Billing shape is given rather than figures, because shape drives design and current numbers live on monid.ai/tools.

The first three rows are answer surfaces and are not interchangeable, so pick at least two and keep their results in separate columns forever. The fourth is the cheap sanity check and should run on a schedule whether or not you are asking questions, because it moves slowly and a drop in it is a real signal. The last row exists because a question nobody asks is not worth tracking, and 60 keyword rows cost one call.

When is LLM SEO not worth doing?

Four cases, and the first is the common one.

Your category has no third-party comparison pages yet. Retrieval pulls documents. If nothing on the open web compares you to anyone, there is nothing to pull, and writing more of your own pages does not fix it. We were absent from 24 of 24, and the vendors present are the ones with years of listicles and forum threads behind them. The fix is other people's pages, which is outreach rather than content work.

Your buyers do not ask in prose. Someone searching an exact part number or an API endpoint name is not going to get a generated answer, and if they do they will scroll past it.

You would be measuring monthly and acting quarterly. These answers move week to week. If you are not going to run the loop, skip the measurement and spend the time on the third-party pages.

The question volume is not there. "llm seo" has real volume as a topic and almost none behind it, a reminder that the meta-topic and the money topic are different things.

And the disclosure, since this is our blog and we sell the endpoints in that table: we ran this on ourselves and the result is that we lose, on every surface, to three competitors by two orders of magnitude. We are giving you the method rather than a win, because the method is the part that transfers.

Conclusion

Answer engine optimization is a real change in shape, from ten ranked links to one paragraph with a shortlist. It is also routinely reported as if there were one answer to measure. On 2026-09-28 we asked four surfaces the same six questions, read 24 answers, and found 20 distinct vendors named. Two questions had no vendor all four agreed on, and one produced four answers with zero vendors in common.

The zeros in our own column are the second finding. Zero of 24 answers named us, zero cited us, and an independent count of AI citations across eight platforms put us at 1 against 198, 465 and 541 for three competitors. Both reads agree, which is what makes it worth acting on.

If you take one thing from the method, take this: keep every surface in its own column and never blend them into a single visibility percentage. The blend hides which surface you are losing on, which is the only part you can do anything about. Match brand names on word boundaries, because ours matched inside the word "exact" on the first pass. And score naming separately from citing, because one surface returned no source list at all while answering perfectly well.

Free next step: run monid inspect -p mrscraper -e /google/ai-mode and read the response shape before you write a scorer. Then ask one question of two surfaces and compare the vendor lists by hand once, because the disagreement is larger than it sounds until you have seen it. Start at monid.ai.

FAQ

Last updated September 2026.