What AI search optimization involves.
Entity and knowledge-graph clarity
Resolve one entity through Organization schema, consistent naming, sameAs profiles, and an earned Wikidata entry—the strongest entity move available. You cannot buy Wikipedia notability; independent coverage must support it, and consolidation takes three to nine months.
Content a model can lift
Because LLMs score passages independently, write self-contained, entity-named, answer-first sections of roughly 40 to 75 words under question headings. Extractability is a citation-readiness floor, not a guarantee or paid lever.
Digital PR that earns citations
Muck Rack's 2026 study of AI answers found about 84% of citations come from earned media and 0.3% from paid placements. Original data, expert commentary, and credible coverage earn citations; your real story, expert, and data carry the work.
Presence on high-citation surfaces
Only about 11% of cited URLs overlap across engines: Perplexity leans on community, while AI Overviews blend Wikipedia, YouTube, and Reddit. Build genuine presence only—never planted posts or fake reviews under platform and FTC rules.
Crawlability hygiene for AI
AI crawlers execute no JavaScript—500M-plus GPTBot fetches ran zero JS. A client-rendered SPA may rank in Google yet remain invisible to ChatGPT, Claude, and Perplexity, so we specify SSR or pre-rendered raw HTML and separate training bots from retrieval bots in robots.txt.
Citation monitoring across engines
Run a frozen set of roughly 40 to 200 buyer-intent prompts two to three times weekly for four to six weeks per engine. Report ranges because run-to-run drift is real; no tool exposes ground truth or guarantees improvement.
How you get cited when someone asks AI
01People talk about you
- Forums
- Review sites
- Your site
02AI reads the sources
03You get named
AI ANSWER
For AI code review that holds up, developers keep pointing to:
Your toolCITED
Illustrative example for an AI code review tool. People discuss you across the web; the model reads those sources and names you in its answer. The work is making you the thing that gets read, and cited.
How we work with you.
What we handle
- Audit and prompt setAudit citations, uncited mentions, and absences by engine; freeze the buyer-intent prompt set used to measure share of model, meaning how often AI answers name you against competitors.
- Extractability rewritesRewrite answer-first, entity-named, self-contained passages under question headings for reliable extraction.
- Entity and Wikidata workBuild Organization and sameAs schema, verified profile relationships, Wikidata, and an eligible Knowledge Panel claim.
- Digital PR to earn citationsCreate original-data assets, target publications, and outreach that earn credible third-party mentions—never bought placement.
- Monitoring and reportingReport per-engine citation rate, competitor share of voice, and prompt stability in ranges against the month-one baseline.
What we need from you
- AccessCMS or staging, GA4, and Search Console access on accounts you own, control, and can revoke.
- Developer time for SSRDeveloper time to ship our precise SSR or pre-render specification when crawlers cannot access client-rendered content; every delayed fix delays visibility.
- Subject-matter inputAn expert interview or draft review that makes the product story accurate, differentiated, and citable.
- Willingness to earn coverageA willingness to earn genuine press and let real customers participate; we never plant Reddit posts or buy reviews.
- Month 1Audit, gap, and prompt setAudit visibility, freeze prompts, classify competitor gaps, uncited mentions, and existing wins, then ship robots.txt, SSR, and entity-schema fixes. New content can appear in one to two weeks.
- Months 2–3ExecutionShip extractability, entity, Wikidata, and digital-PR work; track citations as per-engine presence begins moving unevenly rather than as one blended score.
- OngoingMonitoringTrack per-engine share of model in ranges, clear the month-to-month swings before claiming improvement, and never promise a citation by a fixed date.
An audit first, then a report every month.
| Deliverable | What it is |
|---|---|
| AI-visibility audit | Per-engine citations, uncited mentions, absences, and the prioritized technical or entity gap behind each. |
| Frozen prompt library | A fixed, categorized buyer-intent prompt set that makes every measurement run comparable. |
| Citation-gap analysis | Prompts classified and prioritized as competitor gaps, uncited mentions, or existing wins to protect. |
| Citation-target list | Publications, original-data assets, and community surfaces mapped to the prompts each should move. |
| Monthly share-of-model report | Citation rate, competitor share of voice, and prompt stability by engine, stated as ranges against baseline. |
Four rules for earning AI citations.
Be a corroborated entity, not a claim.
Build one consistently named entity that models can resolve and corroborate across first- and third-party sources.
Write passages a model can lift.
Make each answer-first, entity-named passage understandable and citable without surrounding page context.
Earn the mentions — never fake them.
Use original data, expert commentary, and genuine disclosed participation—never planted posts, paid editorial citations, or brokered reviews.
Measure directionally — promise process, not placement.
Report volatile third-party outputs in ranges against the month-one baseline; promise process and rigor, never a specific citation.
Why the answer names you — or does not
01The AI answer in your category
“best AI code review tool for a small team”
Your tool1 reviews each pull request; developers say it catches real bugs2, fewest false positives3 in one benchmark, and its docs explain self-hosted setup4.
02Stitched from these sources
- 1yoursite.com/ai-code-reviewGUIDE
- 2reddit.com/r/ExperiencedDevsTHREAD
- 3github.comBENCHMARK
- 4yoursite.com/docsDOCS
Two of four phrases came from your pages. That is why the answer names you.
Illustrative answer for an AI code review tool. An AI answer is stitched together sentence by sentence, each phrase drawn from a source. The phrases it pulls from your pages are what put your name in the answer. Our job is making your pages the ones worth stitching from.
Where the target prompts come from.
Each week the trend engine pulls the prompts buyers have started typing into ChatGPT, Perplexity, and AI Overviews, and the sources those answers cite. We target the new questions before competitors answer them, in current buyer wording instead of a static keyword list.
Questions buyers ask
- Is GEO just SEO with a new name?
- Roughly 80% is good SEO: technical eligibility, useful content, and authority. The GEO-specific 20% covers extraction, entities, per-engine surfaces, monitoring, and AI crawlability. AI Overviews pull about 76% of citations from the top ten, while roughly 80% of ChatGPT citations sit outside Google's top 100.
- Can you guarantee we get cited?
- No. Citations are volatile output from someone else's model: Profound found 70% to 90% of cited domains change within six months. We guarantee rigorous work and honest reporting, not placement or a fixed date.
- How do you measure this?
- A frozen prompt set run repeatedly by engine. Baselines are roughly 8 to 15% citation rate, optimized programs 20 to 30%, and leaders 40%-plus. Because 40 to 60% of cited domains change from one month to the next, we prioritize citation rate, share of voice, and branded-search lift over AI-referral traffic, which is often click-free or attributed as Direct.
- How long until it works?
- Technical fixes land quickly; new content can appear in one to two weeks. Entity consolidation takes three to nine months, and measurable share of model typically takes four to six months. We never attach a fixed date to a citation.
- Can we just pay to be placed?
- Not for editorial citations; paid placements are a sliver of what engines cite. Engine ads are separate and labeled. Hi Violet earns citations and never buys editorial placement.
- Why do you keep pointing us at Reddit and YouTube?
- AI Overviews cite Reddit and YouTube; Perplexity leans on community answers. We use real answers from disclosed accounts and video that earns attention—never planted posts or fabricated reviews.
- Does this work for healthcare?
- Yes, with a higher bar. In January 2026 Google pulled AI Overviews from some liver-test searches after a Guardian investigation found misleading answers. For clinics, a named clinician writes or reviews every medical page, and we check how engines describe the practice, not only whether they name it.
- Does this work for Web3?
- Yes. Buyers ask engines to compare chains, wallets, and protocols, and most citations come from earned coverage, not your own site. We check that each engine states your chains, audits, and fees correctly, fix the source pages, and earn mentions in the outlets and listings the answers draw on. Products that hold user funds face the same higher trust bar as health.
- Does this replace our SEO?
- No. AI search optimization extends SEO's technical eligibility, useful content, and authority with entity, extraction, per-engine citation, and monitoring work. Google calls AI-feature optimization "still SEO"; we do not bill twice for overlapping work.
- Is an llms.txt file worth adding?
- Not for citations: no evidence shows it moves them, and roughly 97% of llms.txt files receive zero requests. Prioritize crawlability, entity clarity, extractable content, and earned mentions. Coding agents do use it to find their way through docs, which is agent experience work.
Become the source the answers cite.
Bring five questions your buyers ask AI, and we'll show who gets named today and why.