AEO and GEO Services

Layers of the work: structured content, entity authority, and measurement

We rebuild your pages so answer engines can extract them: clear answer blocks, FAQ and Service schema, an llms.txt and llms-full.txt feed, and a crawler allowlist that invites GPTBot, PerplexityBot, and Google-Extended in.

Answer engines cite what the wider web vouches for. We get you into the directories and listicles the models read and train on, so you become one of the names they pick, not one they skip.

For geographic queries we scope per-city LocalBusiness data, GeoCoordinates, and answer content, so you surface when a buyer asks an AI for a firm in their city or state.

We test the real engines on your buyer queries and track where you are cited and where you are absent, so the work is measured against answers, not just blue links.

Buyers now ask ChatGPT and Perplexity for recommendations before they ever open a search page. If the model does not name you, your ranking never gets a chance to matter.

The firms an AI recommends are the ones sitting in the sources it reads. When you are not in those sources, the model recommends the competitor who is, on every answer.

Client-rendered content, missing schema, no answer blocks, and no llms feed mean the AI crawlers cannot read what your visitors see, so your best content never enters the answer.

AEO is the work of getting your brand cited inside AI answers, the ones ChatGPT, Perplexity, Google AI Overviews, and Claude give when someone asks for a recommendation. It combines structured, extractable content on your site with the third-party authority that makes an engine choose you over the alternatives. The goal is to be the name the AI names.

SEO gets you a position on a page of blue links a person then scans. AEO gets you named inside a single synthesized answer, often before any link is clicked. The technical foundations overlap, but AEO adds answer-extractable content, an llms feed, an AI-crawler allowlist, and a heavier weight on entity authority, because an answer engine cites a few sources, not ten.

Two layers. First, your pages have to be readable and extractable by the AI crawlers: server-rendered content, FAQ and Service schema, answer blocks, and an llms.txt feed, with the crawlers explicitly allowed in. Second, the wider web has to vouch for you, because engines cite sources they trust. We do the on-site layer and give you the exact directory and listicle plan for the authority layer.

People use them for close ideas. AEO, answer engine optimization, is about being cited in AI answers generally. GEO, generative engine optimization, is the same discipline named for generative engines specifically. We also use GEO for the local and geographic layer, making sure you surface when the query names a city or state. In practice we do all of it as one program.

The live-retrieval engines like Perplexity can pick up well-structured pages in weeks once their crawlers revisit. The memory-based answers and the broad, competitive queries take longer, because those ride on authority that builds over months. We set the on-site foundation first so you are eligible the moment an engine looks, then work the authority that carries the rest.

Three layers. On your site: server-rendered content the AI crawlers can read, answer blocks, FAQ and Service schema, an llms.txt feed, and a crawler allowlist that lets GPTBot, PerplexityBot and Google-Extended in. Off your site: the directories, listicles and third-party pages the models read, because an engine cites what the wider web vouches for. Then measurement, testing the real engines on your buyer queries and recording where you are named and where you are not. A firm selling only the first layer is selling schema markup and calling it AEO.

If your SEO team already ships server-rendered pages, clean schema and real authority work, most of the foundation is already theirs and the gap is narrow: an llms feed, answer-shaped content, crawler access, and a measurement loop that reads answers rather than rankings. Bring in a specialist when nobody owns the measurement, when the site renders its content client-side so crawlers see an empty page, or when the category is competitive enough that the authority work needs its own plan.

By asking the engines. We run your buyer queries against ChatGPT, Perplexity, Google AI Overviews and the rest on a schedule, and record which sources each answer names. That produces a citation count per engine per query, plus the list of competitors being cited instead of you, which is the part worth acting on. Rankings still matter more than most AEO vendors admit: across the queries we have measured, citations cluster in the topics where a site already ranks, so the two programs are not separate.

A dated example rather than a promise. On 21 August 2026 the Google AI Overview for the query tableau vs power bi, roughly 1,900 US searches a month, cited our own comparison page as one of its sources, alongside Domo and Velosio. That page is built the way we build them for clients: answer blocks, FAQ schema mirrored exactly in the visible body, and a server-rendered body a non-JavaScript crawler reads in full. We run the same test on your category before you sign anything, so you can see where you stand today.

No, and anyone who guarantees a spot in an AI answer is guessing. The engines are non-deterministic and change constantly. What we can do is remove every technical reason not to cite you and build the authority signals that move the odds, then measure the result across engines so you see the movement rather than take it on faith.

Yes, and it is the clearest proof we can offer. We ran the same program on our own site: schema, an llms feed, answer content, and the crawler allowlist, then tested the engines on our category. We went from not appearing at all to the first firm named in Perplexity for our space. We will show you the before and after.

Answer Engine Optimization: get cited by ChatGPT, Perplexity, and Google AI. Schema, llms.txt, answer content, and the authority to be the name they name.

Get cited by ChatGPT, Perplexity, and Google AI. The name the answer engines name.

There is no flat rate. These are the factors that move the effort on an answer-engine program.

Server-rendered pages with clean schema are quick to make extractable. Client-only rendering and missing markup add rebuild work before an engine can read you.

If you already have directory and editorial presence, selection comes faster. Starting from zero third-party signal means more of the work is entity building.

How many buyer questions and geographies you want to be cited for sets the content and measurement scope.

Answer engines shift constantly. A one-time setup differs from a program that monitors citations and defends your position across engines.

Buyers in your market ask ChatGPT or Perplexity for recommendations.

Your site is not indexed at all yet: start with technical SEO first.

You need the underlying content built: see Data Analytics Consulting.

Your goal is classic local ranking only: start with the local pack basics.

Most agencies still optimize for a position on a page of links. Here is where an answer-first program is different.

The directories and listicles the models actually read and cite.

Citations across engines, tested on your real buyer queries.

Pick the entry that matches where you are, and we scope from there.

A fixed-scope check of how you appear across the answer engines today, what is blocking you, and a prioritized plan before any build.

A defined engagement: the schema, answer content, llms feed, and crawler setup, plus the authority plan. You see the number before you commit.

We monitor your citations across engines and defend your position as the models change, on an ongoing basis.

We hand it off. Documentation and training so your team keeps the content extractable and the authority growing.

Answer engines cite sources that are clean, consistent, and well structured, which is a data problem before it is a marketing one. Thinklytics is a data and analytics firm first, so we structure the content, schema, and metrics an engine reads the same way we structure a warehouse. That is what makes a citation hold instead of flicker.

Before a buyer opens a search page, they ask ChatGPT or Perplexity who to hire. If the model does not name you, your ranking never gets read. We make your brand the one answer engines cite, with structured content on your site and the authority that makes them choose you. We ran this on our own firm and went from invisible to first in Perplexity for our category.

Answer engine optimization gets your brand cited inside AI answers from ChatGPT, Perplexity, Google AI, and Claude. Thinklytics builds the extractable content and schema those engines read, sets up your llms feed and crawler access, and works the entity authority that makes an engine choose you, so you are the firm the AI recommends when a buyer asks.

Strategy, agent and LLM builds, and the governance to run AI in production, from pilot to live.

The content and proof underneath your answers, one trusted source of truth an engine can cite with confidence.

Score your data and architecture for AI before you build, with a roadmap and a 90-day plan.

Start with an audit. We test the answer engines on your buyer queries, show you where you are cited and where you are absent, then give you a prioritized plan before any work begins.