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Search & AI Discovery

Get retrieved and cited when a buyer asks an AI engine, in one focused sprint

For US small and mid-size businesses, med-spas, home services, dental and solo-legal practices, that rank fine on Google but never get named when a prospect asks ChatGPT, Perplexity or a Google AI Overview the same question.

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Every engagement is directed by a technical specialist and reviewed before delivery.

What this is

The GEO Sprint is a focused, time-boxed engagement that moves one number: how often an AI engine names and cites you when a buyer asks about what you sell. It coordinates three disciplines that only work together, citation-readiness on your own pages, content reshaped into the passages engines actually quote, and earned authority placement on the third-party sources those engines already trust, run as one sequenced build rather than three separate tasks. It is scoped against a real reading first: we fix a panel of your buyer questions, run each one across each engine many times, and report the appearance rate as a Share-of-Answer figure with a confidence band. The sprint then closes the gaps in a fixed order and re-reads the number so the movement is measured, not asserted. The outcome is a business that shows up inside the AI answer a prospect reads, evidenced against a before figure, scoped in writing before any work begins, and reviewed by a technical specialist before delivery.

The problem

Why this matters now

You might rank for your name and a few core terms, so on the classic results page your visibility looks healthy. Then a prospect skips that page entirely and asks ChatGPT, Perplexity or reads a Google AI Overview for the exact service you sell, and a competitor gets named in the reply while you are absent. Winning a keyword ranking and being cited in an AI answer are two different outcomes. The evidence that they are separate is now hard to ignore: Ahrefs found that 28.3 percent of ChatGPT's most-cited pages have zero organic visibility in Google, and fewer than 10 percent of sources cited across ChatGPT, Gemini and Copilot rank in the top 10 for the same query. Ranking does not buy the answer.

A one-off tactic fails here because AI citation is a three-part problem. An engine has to be able to parse a page cleanly, it has to find a self-contained passage worth quoting, and it has to already trust you from mentions on sources it reads elsewhere. Fix only the pages and you are eligible but never corroborated. Buy only PR and the engine still cannot cleanly retrieve the page it lands on. The three pieces reinforce each other or none of them moves the answer, which is exactly why a coordinated sprint beats a stack of disconnected fixes.

It is also genuinely hard to know where you stand, because answer engines are not deterministic. Ask the same question twice and the cited businesses can change. A single check reveals almost nothing, so most firms either guess or avoid measuring the AI surface at all and keep reporting the classic rankings they can see. Without a repeatable way to sample the answer surface, there is no way to tell whether you are invisible, occasionally named, or genuinely established.

The GEO Sprint closes that gap on a fixed clock. We read your Share-of-Answer, sequence the three disciplines that move it, and re-read the number at the end so you can see the shift with its uncertainty shown, rather than paying for activity that cannot be verified.

How it works

The mechanism, made checkable

  1. 01

    Phase 1, Read your answer surface and freeze the baseline

    Before anything is scoped, we build a panel of your real buyer questions and run each one across each engine, ChatGPT, Perplexity, Google AI Overviews, Gemini and Copilot, many times over, then report the appearance rate as a Share-of-Answer rate with a confidence band. We also record who does get cited for those questions and which third-party sources the engines are pulling from. Every reading is stamped with the engine, locale and date, because all three change the result. This frozen baseline is what the whole sprint is measured against at the end.

  2. 02

    Phase 2, Make your pages retrievable and citation-ready

    An engine can only cite a page it can cleanly parse. This phase repairs the citation-readiness layer: crawl and render access for AI crawlers, clean semantic structure, one canonical entity for your brand and your people, and hand-verified JSON-LD applied for its genuine parsing value. Google has confirmed no special markup is required for AI Overviews, so this is not a promised citation lift, it is what makes you eligible to be quoted. On its own it rarely wins the answer, which is why it is a phase and not the whole sprint.

  3. 03

    Phase 3, Reshape priority content into the passage engines quote

    Engines retrieve passages, not whole pages, so we reshape your priority pages into the form they lift: a real buyer question as the heading, the direct answer in the first sentence or two, then a self-contained, statistic-backed elaboration. The peer-reviewed GEO study (Aggarwal et al., KDD 2024) found that adding citations, quotations and concrete statistics were the strongest levers for being cited, and that keyword stuffing performed at or below baseline. Our writing follows that finding with human craft, not filler, and the same shape also serves classic featured snippets.

  4. 04

    Phase 4, Earn the third-party corroboration engines trust

    AI engines corroborate: a business named positively across several independent domains is treated as more authoritative than one that only praises itself. Muck Rack's May 2026 Generative Pulse analysis of more than 25 million links found that 84 percent of AI citations come from earned media, real editorial coverage, not brand-owned pages. We earn genuinely citable mentions for you on the sources those engines already read, through white-hat digital PR, original data assets and expert commentary. No bought or exchanged links, no private blog networks, real customers and real publications only.

  5. 05

    Phase 5, Re-read, evidence the movement, and hand off

    At the close, we re-run the exact frozen panel from Phase 1 against the same engines and locale, and report the new Share-of-Answer with its variance beside the before figure, so you see the shift with the working, never as a naked number or a guarantee. You receive a specialist-reviewed report, the prompt panel, and a prioritized list of what to hold and what to compound next. If your surface warrants ongoing defense, we scope a standing cadence separately; the sprint is complete and self-contained on its own.

What is included

What is delivered

  • A frozen Share-of-Answer baseline across ChatGPT, Perplexity, Google AI Overviews, Gemini and Copilot, built from your real buyer questions and stamped with engine, locale and date.
  • A competitive citation read: which businesses currently win your target questions and which third-party sources the engines are drawing from.
  • Citation-readiness engineering: AI-crawler access, clean semantic structure, canonical entity resolution and hand-verified JSON-LD applied for genuine parsing value.
  • Priority content reshaped into the answer-passage form, with question headings, direct opening answers and self-contained, source-backed elaboration.
  • White-hat authority placement: earned editorial mentions, original data assets and expert commentary on the sources your target engines already read, never bought or exchanged links.
  • Real-customer review and reputation signals corroborated across the web where they support your entity, with responses, since engines weigh independent sentiment.
  • A close-of-sprint re-read of the identical prompt panel, reporting new Share-of-Answer with variance beside the baseline figure.
  • A specialist-reviewed report plus a prioritized hold-and-compound list, and the full prompt panel handed over so the measurement is repeatable.
  • Written scope and sequence confirmed before any work begins, and a technical specialist directing and reviewing every deliverable.

The outcome

What it moves

  • A real read of how often you appear inside AI answers, sampled across each engine many times and reported as a Share-of-Answer rate with a confidence band, surfacing the half of visibility most firms never measure.
  • Pages that AI crawlers can access, parse and resolve to one canonical entity, making you eligible to be quoted rather than skipped.
  • Priority content reshaped into the question-and-answer passage form that engines lift, built to the citation levers the research actually supports.
  • Earned mentions on trusted third-party sources that give engines the independent corroboration they weigh most heavily, with the comparison group disclosed whenever a benchmark is run.
  • A before-and-after Share-of-Answer figure with variance shown, so you see the movement over the sprint evidenced rather than asserted, and never dressed up as a guarantee.
  • A specialist-reviewed handoff pack: the frozen prompt panel, the findings, and a ranked list of what to hold and compound after the sprint closes.

What you get

What you get, and how it is priced

The figure on the GEO Sprint tracks your actual Share-of-Answer reading, not a template. A business missing from every engine with thin third-party mentions needs a very different sequence from one that is occasionally cited but has unparseable pages. Every sprint starts with a diagnosis, scope is confirmed in writing before work begins, and what follows is exactly what it assembles, how it is phased, and the outcome it produces.

Focused Sprint. The core engagement: one baseline read, the three coordinated disciplines run in sequence against a defined question panel, and a close-of-sprint re-read with the movement evidenced. Best when the AI-answer surface is the specific gap and you want it moved on a fixed clock. Panel size, page count and placement scope are set after your diagnosis and confirmed in writing before work begins, priced to that scope.Quoted
Extended Sprint. The same method run across a wider question panel and more priority pages, with a deeper authority-placement push where the surface is competitive and the corroboration gap is large. Best when several service lines or locations need to be moved at once. Deliverables and sequence are set from your diagnosis and confirmed in writing. Scope before numbers, always.Quoted

You see the full deliverables and cadence first, then a price built for your business, confirmed in writing.

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Straight answers

Questions about GEO Sprint

How is this different from your Search Surface Optimization program?

Search Surface Optimization is the full coordinated program that moves your whole Machine-Readiness Score across four pillars: classic search, AI answers, reputation and the technical foundation. The GEO Sprint is the focused engagement for when the AI-answer surface is your specific gap and it needs to move on a fixed clock. It coordinates three disciplines, citation-readiness, content-for-AI and authority placement, against one number, Share-of-Answer, rather than working all four pillars at once. When your classic rankings and technical floor are already sound and the problem is that engines simply never name you, the sprint is the right size of work.

Do you guarantee we will get cited by ChatGPT or appear in AI Overviews?

No. Answer engines are not deterministic, AI Overview selection is undocumented and volatile, and results personalize by user and locale, so a citation cannot be guaranteed. Our commitment is to method and measurement: we read your present Share-of-Answer, close the gaps that most influence it, and re-read the identical panel at the end so you can see the movement with its variance shown, with the working behind every claim.

How do you measure whether we appear in AI answers, given the results keep changing?

That volatility is exactly why a single check is worthless. We fix a panel of your real buyer questions and run each one across each engine many times, then report the appearance rate with a confidence band rather than a single snapshot. Every reading is stamped with the engine, the locale and the date, because all three move the result. The AI surface is the most uncertain thing we measure on this engagement, so we show it with the widest band and never publish an invented average.

Why do you insist on doing all three parts together instead of just the piece I think I need?

Because AI citation is a three-part problem and the parts reinforce each other. Fix only your pages and you are parseable but never corroborated by outside sources. Buy only PR and the engine still cannot cleanly retrieve the page it lands on. Reshape only content and neither eligibility nor trust is in place. Running citation-readiness, content-for-AI and authority placement in sequence is what actually moves the answer, which is why the sprint is coordinated rather than sold as one isolated task. When a diagnosis shows one piece is already solid, we set the scope around it.

Is the content you produce just churned out at scale?

No. Every passage is written by a person who read your buyer's intent, then reshaped into the question-and-answer form engines quote and backed with real, sourced statistics. The peer-reviewed research is blunt that thin, stuffed content performs at or below baseline in AI answers, so volume for its own sake would actively work against you. The work is attributed to the method, the Share-of-Answer measurement and the Visibility Corpus that aims it, not to any tooling. Every engagement is directed by a technical specialist and reviewed before delivery.

You are based overseas. Who does the work, and does that matter for a US business?

Raveneye Global, operated by RavenGroup Global Tech Private Limited, bills in USD and serves US businesses. Every engagement is directed by a technical specialist and reviewed before delivery. The sprint is measured against US engines, US buyer questions and US locales, and every Share-of-Answer reading is stamped with the exact engine and locale it was run on. What you are buying is an engineering standard and a measured outcome, not a time zone.

Does schema markup or an llms.txt file get us into AI answers?

We engineer schema for its genuine value, cleaner parsing and rich-result eligibility in classic search, but Google has confirmed no special markup is required for AI Overviews, so it is not a promised citation lift. llms.txt is not a citation lever either, because Google has confirmed its Search systems do not use it; it is AI-crawler readiness, nothing more. The levers the evidence actually supports are self-contained passages, cited statistics and third-party corroboration, and that is where the sprint puts the work.

Why is this scoped instead of a fixed price?

Because the work is set against your actual Share-of-Answer reading, not a template. A business absent from every engine with almost no third-party mentions needs a very different sequence from one that is occasionally cited but has unparseable pages. Publishing one price would either overcharge the simple case or under-deliver the hard one. We publish the full deliverables and the sequence, diagnose your surface, then quote the exact figure directly with you.

Related

Where this connects

Search Surface Optimization

The full coordinated program when the gap is more than AI answers. It moves your whole Machine-Readiness Score across classic search, AI answers, reputation and the technical foundation as one sequenced build. The GEO Sprint is the focused subset of this method.

Explore

The Machine-Readiness Score

The single number behind every engagement, and how Share-of-Answer is read as one of its four pillars. See exactly how appearance inside AI answers is sampled, banded and scored before you scope the sprint.

Explore

The Raveneye Methodology

The published method behind the sprint: how we measure the answer surface, why the three disciplines are run together, and the Visibility Corpus behind the method. Read how the work is built before you buy it.

Explore

Provenance

Sources

  • GEO: Generative Engine Optimization, Aggarwal et al., KDD 2024, arXiv:2311.09735 (finding that adding citations, quotations and concrete statistics were the strongest levers for generative-engine visibility, and that keyword stuffing performed at or below baseline)
  • Muck Rack, Generative Pulse, May 2026 (analysis of 25+ million links finding that 84 percent of AI citations come from earned media rather than brand-owned or paid placements)
  • Ahrefs, cited 2026 (28.3 percent of ChatGPT's most-cited pages have zero organic visibility in Google; fewer than 10 percent of sources cited in ChatGPT, Gemini and Copilot rank in the top 10 organic results for the same query)
  • Otterly.AI, The AI Citations Report 2026 (analysis of 1+ million AI citations across ChatGPT, Perplexity and Google AI Overviews, January to February 2026, on citation source distribution and multi-source corroboration)
  • Google Search Central, Guide to Optimizing for Generative AI Features on Google Search (AI features run on the core index and ranking systems; no special markup required for AI Overviews; Search does not use llms.txt)

Begin with where the business stands.

Submit a query See the method

No obligation. The deliverable is a measured starting position and the corrections that move it most.