Answer engine optimization and AI SEO that gets you cited by the engines your buyers use

AEO, AI SEO and GEO are the same work: getting your pages quoted inside ChatGPT, Claude, Perplexity and Google AI Overviews answers. What gets quoted is specific, evidence-backed claims, served as clean markdown. Ramp's research found markdown was the only format that reliably surfaced in LLM responses.

A cynical reader might suspect we are keyword stuffing in this section... But whether you're looking for an AI SEO agency, AI SEO services, generative engine optimization agency, generative engine optimization services, AI SEO company, answer engine optimization. They're all the same god-damn thing and they almost all hinge around the same core principles.

Create well marked-up content, iterate on it faster, and make it easier for AI models to read and understand.

AI SEO is the plain-language term. GEO (generative engine optimization) and AEO (answer engine optimization) are the technical ones, and we use them interchangeably. Whichever acronym you arrived with, the deliverable is identical: find where ChatGPT, Claude, Perplexity, and Google's AI Overviews pull their answers, ship the changes that put you inside those answers, and measure whether your citation share moved. If you want to check your own site first, our practical introduction to AEO, GEO and AI SEO walks through the technical checks you can run on any website. The rest of this page is how we run that loop and more importantly how fast.

Per Ramp's first-party research, markdown was the only format that reliably surfaced in LLM responses. Bots ask for markdown. Browsers get HTML. The same URL can serve both. That is content negotiation, and instrumenting it per page is the single change with the widest citation coverage.

Roboto's own site is the worked example. The content is MDX on disk, so the markdown is the source rather than a format derived from a CMS export. When an LLM crawler asks for markdown at a given URL, it gets a clean document with structured headings, inline links, and no CMS wrapper noise. We wrote up the approach in our Next.js AEO/GEO guide. On a CMS-driven stack it's a transform step rather than a route handler, but the goal is the same clean markdown at the canonical URL.

Quarterly advisory cycles break at the feedback step. You get a report in month three describing what happened in month one, and by the time an edit clears approval and publish, the competitive set has shifted again. The diagnostic is stale before the fix ships.

A background agent watches citation share across platforms on a weekly cadence. When share drops on a target prompt cluster, it flags the change, identifies what moved in the competitive set, and drafts the fix: a content edit, a schema patch, an llms.txt update, or a content negotiation rule. One of our engineers reviews it and ships it, then the agent re-monitors that cluster and measures whether share recovered. We run this on our own site and documented the build in why we built our own background agent. For an engagement we set the same loop up on your stack: your data sources, your content repo, your approval flow.

Bot classification is where most implementations break. Single-signal classification on User-Agent alone misses the bulk of AI crawler traffic. Correct classification combines UA, IP range, ASN, and bot score. The Cloudflare gotcha compounds it: ChatGPT, Perplexity, and Claude bots are categorised as "AI Assistants" in Cloudflare's taxonomy, not "AI Search", so firewall and analytics rules targeting only "AI Search" miss all three major platforms.

The rest is table stakes we verify before touching anything higher up. Schema goes in where it helps LLMs parse structure (ServiceSchema, FAQPage, BreadcrumbList, Article). The llms.txt file gets prioritisation logic rather than a flat list of 400 pages. Crawlability is genuinely non-negotiable: every list item rendered into the DOM, navigation in static HTML, correct status codes, and clean submitted sitemaps.

Volume compounds citation share only when the content clears a quality bar that LLMs trust. The editorial infrastructure is what makes volume safe to ship, so we build three gates into the publishing pipeline.

An automated check runs before merge, flagging em-dashes, negative parallelism, and the vocabulary we've caught models overusing; content that fails goes back to be rewritten. A fact-checker agent verifies specific claims against their cited primary sources and flags any that don't hold. A final editing pass strips the sentence structures that read as generated. The cms-auto-updater pipeline is the worked example: it pulls evidence from primary sources on a weekly cadence, drafts updates, runs all three gates, and queues each change for human sign-off before it publishes. The result is far more content per quarter without the citation-eroding slop that comes from raw AI publish pipelines.

Roboto Studio builds Next.js and Sanity websites and does the AEO work in the same build, so the site launches ready for ChatGPT and Google AI Overviews instead of getting fixed afterwards. Every Sanity build we ship follows our AEO and SEO defaults for Sanity: JSON-LD, sitemaps, content negotiation that serves Markdown to AI crawlers, and an llms.txt.

Slingshot Bio is a recent example. We merged their WordPress and Shopify sites into one headless build on Next.js and Sanity, and it launched with JSON-LD on every product, collection and resource, Markdown for AI crawlers, an llms.txt, and a Markdown sitemap. Marketing publishes in Sanity, and all of it stays current without anyone touching it.

If your site already runs on Next.js and Sanity, the setup engagement below adds the same layer to the build you have.

Roboto Studio does AEO for B2B SaaS companies, on Next.js and Sanity sites and on whatever stack your site already runs. SaaS buyers ask assistants shortlist questions: the best tool for a job, the alternatives to a competitor, whether two products integrate, what a plan costs. Assistants answer those from pages that state the facts plainly, so most of the work is on pages you already have, like pricing, integrations, comparisons and docs. We make sure they're crawlable, served as Markdown, marked up with schema, and current.

Keeping them current is the hard part. A comparison page that was right at launch is wrong the first time a competitor changes its pricing. A weekly agent checks our own CMS comparison pages against each vendor's docs and opens a pull request when something changed, and we set up the same page updater bot for clients.

Part of this retainer is a weekly agent that audits the analytics platforms you already use, condenses them into one place, and hands your team the next steps to take. It does the reading and the triage so the monthly call is about decisions, not dashboards.

When a community thread outranks your own page, that thread is an opportunity. This bot finds those threads for your keywords and drafts a response built from your own content, so you get a second touch point on a page that already wins the search.