Get found where search is going next.
A growing share of your buyers now ask ChatGPT, Perplexity, or Google's AI Overviews before they ever see a results page - and those systems name a handful of businesses, not ten blue links. We make sure your brand is one of the names: cited, recommended, and described accurately when AI answers the question.
From ranking in results to being the answer
Classic SEO competes for position on a page. AI search changed the contest: when someone asks an assistant "who should I hire for X," the answer is a short, confident recommendation assembled from whatever the model can find and verify about you. Either you're in that answer or you're invisible - there is no position seven.
Most agencies are handling this one of two ways: ignoring it, or slapping "GEO" on the same deliverables they sold last year. Neither works, because AI visibility has its own mechanics - entity consistency across the web, structured data machines can parse, answer-shaped content worth quoting, and third-party mentions in the sources LLMs actually retrieve from. It rewards businesses that are verifiably real and specific, and it quietly filters out everyone whose web presence is vague or contradictory.
This is Amped's forward-edge service, and we practice it on ourselves first - we compete for AI citations in our own market before we recommend a tactic to yours. The honest pitch: nobody can guarantee an LLM says your name. What we can do is systematically build every input those systems draw from, measure how often you appear, and compound your share of the answers month over month.
Why Amped
- ⚡ 20+ years as MSP owners
- ⚡ Reporting tied to leads & revenue
- ⚡ No long-term lock-in
- ⚡ One connected growth system
What you get with Amped
Every input AI systems use to decide who to recommend - built, aligned, and tracked.
AI visibility baseline
We test the questions your buyers actually ask across ChatGPT, Perplexity, Gemini, and Google's AI Overviews, and document who gets named, who gets cited, and what the models currently believe about your business. You can't grow a share of answers you've never measured.
Entity optimization
AI systems reason about entities, not keywords - so your name, services, locations, and credentials must tell one consistent story across your site, Google Business Profile, LinkedIn, directories, and every third-party mention. We find the contradictions that make models hedge and eliminate them.
Structured data for machines
Organization, Service, FAQ, and review schema implemented so your key facts are machine-readable, not buried in paragraphs. Structured data is the difference between a model inferring what you do and a model knowing it - and confident systems cite; uncertain ones omit.
Answer-shaped content
Pages engineered to be quoted: direct answers up front, clear question-matched headings, comparison tables, and specifics a model can lift verbatim. Retrieval-based AI quotes the clearest credible source it finds - we make sure that source is you.
Citation-source presence
LLMs lean on a recognizable set of sources - review platforms, industry directories, publications, forums like Reddit where real users vouch for businesses. We build your legitimate presence in the sources that feed answers for your category, no astroturfing.
AI visibility tracking
Monthly re-testing of your buyer-question set across the major AI surfaces: how often you're named, how you're described, who you're recommended alongside, and which citations drove it. Reported next to your classic rankings, so you see both battlefields on one page.
How AI decides who to recommend
When an AI assistant recommends a business, it isn't consulting a secret ranking. It's synthesizing an answer from two things: what the model absorbed in training, and what it retrieves from live search at the moment of the question. Both are influenceable - if you understand what they reward.
Retrieval still runs through search. ChatGPT, Perplexity, and AI Overviews all pull candidate sources from web indexes before composing an answer - which means crawlability, rankings, and authority remain the price of admission. That's why AI SEO extends classic SEO rather than replacing it: a site invisible to Google is invisible to the systems built on top of Google.
Models reward corroboration. A claim that appears once on your website is an assertion; the same claim echoed by your Google Business Profile, industry directories, reviews, and press coverage is a fact the model can safely repeat. This is why entity consistency does so much work - AI systems are, at their core, machines for repeating what multiple credible sources agree on.
Extraction favors structure. When a model composes an answer, clearly structured content wins: the direct answer in the first sentence, one question per section, tables for comparisons, schema confirming the facts. Pages written to sound impressive lose to pages written to be understood - a shift that genuinely favors plain-English businesses over jargon-heavy ones.
The winner-take-most math is the reason to move early. An AI answer names two or three businesses where a results page listed ten. Referral traffic from AI surfaces is still smaller than classic search, but it converts hard - the assistant already did the persuading. Categories are being settled right now, one answer at a time, and models keep repeating the entities they've already learned to trust.
Our process
AI visibility audit - weeks 1-2
We build your buyer-question set, run it across the major AI surfaces, and record every mention, citation, and misstatement. You get a plain answer to the question most businesses have never asked: what does AI say when your customers ask about you?
Entity & gap analysis - weeks 2-3
We map every place your business is described online, flag the inconsistencies and dead profiles eroding machine confidence, and identify which citation sources feed answers in your category - then rank the fixes by impact.
Structure & content - weeks 3-8
Schema goes in, key pages are restructured into answer shape, and new content is built for the specific questions where you deserve to be the answer and currently aren't. Everything stays human-quality - extractable is not the same as robotic.
Corroboration building - ongoing
Reviews, directory presence, and mentions in the publications and communities LLMs retrieve from - built steadily and legitimately. This is the slowest lever and the strongest one: corroboration is what turns your claims into the model's facts.
Re-test & compound - monthly
The full question set runs again every month: mentions won, mentions lost, how descriptions of you are changing, and which work drove it. AI surfaces shift fast, so the strategy adapts quarterly instead of running on autopilot.
Built by operators, accountable to results.
AI search is full of vendors selling certainty they don't have. We treat it like operators treat any new channel: measure the baseline, work the inputs you can control, re-test relentlessly, and report what actually changed - including when a tactic didn't pay, so the budget moves to one that does.
Book a Strategy Call →Real outcomes
- ⚡ Qualified leads, not vanity clicks
- ⚡ Transparent monthly reporting
- ⚡ You own your accounts & data
Frequently asked
Some inputs move fast: schema, entity cleanup, and answer-shaped restructuring can start shifting AI Overview and Perplexity citations within 60-90 days, since those systems retrieve live. Becoming a name models volunteer consistently across surfaces is a 4-6 month build, because it depends on corroboration accumulating across third-party sources. Your baseline audit tells us which end of that range your market sits on.
Classic SEO earns you a position on a results page; AI SEO earns you a place inside the answer itself. The disciplines overlap - strong rankings feed the retrieval layer - but AI visibility adds work classic SEO never scoped: entity consistency across the whole web, machine-readable structured data, quotable content formats, and presence in the specific sources LLMs cite. Done together, each makes the other stronger.
Yes - by systematically re-testing a fixed set of your buyers' real questions across ChatGPT, Perplexity, Gemini, and AI Overviews every month, and logging mentions, citations, and how you're described. It's share-of-answers tracking rather than a neat position number, and we're upfront that the measurement is younger than rank tracking. It's still vastly better than not looking.
Fair question - the volume is smaller than classic search today, but it's concentrated at the highest-value moment: a buyer asking for a recommendation and receiving two or three names. Those visitors arrive pre-sold and convert accordingly. The strategic reason to start now is that models keep recommending entities they already trust; waiting until the channel is "big enough" means arriving after your category's default answers are set.
Scope depends on the state of your entity footprint and how contested your category's answers already are - we price it after the baseline audit, and if you're already running SEO with us it layers in rather than duplicating spend. Month-to-month, no long-term lock-in, and every asset - schema, content, profiles, tracking data - is yours. We keep clients by winning answers, not by holding contracts.
Related services
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Tell us where you want to grow. We'll ask ChatGPT and Perplexity your buyers' questions live on the call and show you exactly who's getting recommended in your market - and what it would take for that to be you. No obligation, real insight on the call.
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