Somewhere in the last two years, the question changed. Clients used to ask where they ranked. Now they ask why ChatGPT described their company that way, or why Perplexity cited a competitor’s blog post and a four-year-old Reddit thread instead of their own documentation. Ranking position still matters, but it is no longer the whole scoreboard. In AI search, the thing that counts is whether you get cited inside the answer.
That shift is why I built the ARC Method: a simple, repeatable framework for earning AI citations rather than hoping for them. It is the backbone of how I approach Generative Engine Optimization (GEO) with clients, and it is the spine of my book, Reddit, AI Overviews & GEO: The SaaS Founder’s Playbook for Winning AI Search Visibility.
Why a framework was necessary
Let’s define terms, because the vocabulary is still settling. Generative Engine Optimization (GEO) is the practice of making your brand and content retrievable, quotable and trusted by generative systems — ChatGPT, Perplexity, Google AI Overviews, Claude, and whatever ships next quarter. AI Overviews are the synthesized answers Google places above traditional results. AI citations are the linked or named sources those systems attach to their answers.
The problem with most GEO advice is that it is a pile of tactics with no ordering logic. Add schema. Post on Reddit. Write FAQs. Get on listicles. Each of those can work. None of them tells you what to do first, or why the tactic failed when it failed.
Over a decade in reputation and search, I’ve learned that tactics without a diagnostic model produce busywork. A framework does something different: it tells you which layer is broken. If a model won’t mention you at all, that is a different problem than a model mentioning you inaccurately, which is different again from a model mentioning you but citing someone else’s page. ARC separates those three failure modes so you can fix the right one.
The ARC Method, in plain terms
ARC stands for Authority, Relevance, Citability. Think of it as three gates. A language model has to pass you through all three before your name shows up in an answer.
A — Authority: does the wider web corroborate you?
Generative systems are, at their core, consensus machines. They don’t evaluate your claim about yourself; they weigh what independent sources say about you. If your brand exists only on your own domain, you are a single unverified assertion. If your brand appears in industry press, community discussion, review platforms, directories and third-party comparisons — consistently described the same way — you become a fact the model is comfortable repeating.
This is where reputation work and AI search collapse into the same discipline. Authority is earned, not bought. It accumulates through real coverage, real customer discussion and real expert bylines. That is also why AI reputation management is now inseparable from GEO: the same corpus that shapes whether a model cites you also shapes how it characterizes you.
R — Relevance: are you present where the answer is formed?
Authority gets you considered. Relevance gets you retrieved for a specific question. Models assemble answers from sources that closely match the intent of the prompt, and prompts are messier and longer than keywords. People ask AI systems things like what’s the best contract analysis tool for a two-person law firm that doesn’t need enterprise pricing. That is not a keyword. It is a scenario.
Relevance means mapping the actual question space of your buyer and making sure you exist inside it — in your own content, yes, but also in the places these systems lean on heavily for practical opinions. Community platforms punch far above their weight here, which is why Reddit authority and AI search ended up in the title of the book. Show up where the answers are formed, not just where you own the domain.
C — Citability: is your content easy to lift?
This is the gate most teams fail, and it is the most fixable. A model generating an answer needs a discrete, self-contained, unambiguous passage it can extract and attribute. Long meandering paragraphs, vague hedging, marketing language, claims buried under three subheadings of throat-clearing — all of it reduces the odds that any single chunk of your page survives retrieval.
Citable content tends to share the same traits: a direct answer stated early, specific numbers and definitions, clean heading structure that matches how people phrase questions, named authorship, and dates. Clarity and structure are not stylistic preferences in AI search. They are retrieval mechanics.
How the three gates behave together
Here’s a concrete pattern I see constantly with SaaS teams. A company publishes a genuinely excellent comparison page. It is thorough, honest, well-designed. It gets some traffic. But when you prompt ChatGPT or Perplexity with the exact question that page answers, the model cites a Reddit thread, a review aggregator and a competitor’s help doc.
Run it through ARC and the diagnosis is usually quick. Authority: the brand is barely discussed off-domain, so its self-published comparison reads as a sales asset. Relevance: the page targets a keyword, while buyers are asking a situational question the page never states in plain language. Citability: the actual answer sits in paragraph nine, wrapped in qualifiers, with no clear extractable statement anywhere.
None of those are content quality problems in the traditional sense. They are structural problems, and structural problems respond well to systems. What I’m seeing across AI search is that teams who build a repeatable process — publish, structure, corroborate, monitor, revise — compound their visibility, while teams chasing individual mentions stay flat. Systems and automation are how you scale quality, a theme I dig into further on AI visibility and fulfillment automation for SaaS.
What to do this month
If you want to apply the ARC Method without a six-week strategy phase, start here:
- Build a prompt set, not a keyword list. Write 20–30 questions a real buyer would type into ChatGPT or Perplexity. Include the awkward, specific, budget-and-constraint ones.
- Run the baseline. Ask each prompt across two or three assistants. Record whether you’re mentioned, how you’re described, and which sources get cited. That citation list is your competitive map.
- Audit Authority. Count independent sources that describe your category position. If the honest number is close to zero, that is your first constraint, and no amount of on-page tuning solves it.
- Audit Relevance. For every prompt where a community thread or third-party page won the citation, ask whether you have any presence in that venue at all.
- Fix Citability first. It’s the cheapest gate to open. Lead each page with a two-sentence direct answer. Use question-shaped headings. Add a definition, a specific example and a named author.
- Re-measure on a schedule. Monthly, same prompts, same assistants. Citations move; you want to see the direction.
One thing that consistently works: rewriting a strong page’s opening 150 words so the answer comes first. It costs an hour and changes retrieval outcomes more often than any technical addition.
The durable principle
Models will change. Interfaces will change. What will not change is the underlying logic: generative systems reward sources that are corroborated by others, present in the conversation, and structured so a machine can lift a clean answer without guessing. That is all ARC encodes.
I’m Cory Maki — an AI search strategist working on GEO and online reputation management from Taichung, Taiwan, and Head of Fulfillment at Reputation Pros. I write about frameworks here because frameworks survive algorithm updates in a way tactics don’t; you can read why I started publishing these notes if you want the longer version. Citations beat rankings in AI search, and citations are earned the slow, structural way. ARC is just the order of operations.