Why GEO exists
For twenty years, SEO assumed a crawler. You optimized titles, schema, page speed, backlinks — and Google's spider rewarded you with a ranked list of ten blue links. The buyer clicked one. You won, or you didn't.
Generative engines don't render a list. They synthesize an answer, and they cite a small set of sources to ground it. ChatGPT cites 3–8 URLs per answer; Perplexity 5–15; Gemini AI Overviews surfaces 4–6; Claude with browsing names 2–6 entities and links a few. If your brand isn't in that small set, the buyer never sees it. The funnel collapsed from "ranked list" to "named or not."
The mechanics are different too. SEO rewards crawlable depth — long pages, structured data, inbound links. Generative-engine citation rewards retrieval-friendly authority: clear entity claims, schema that matches the model's training distribution, llms.txt-style indices that the retrieval layer can ingest cheaply, and consistent corroboration across third-party sources the model already trusts. Most B2B brands sit at under 5% citation-share on the queries that matter to them today. GEO is the lane that fixes that.
The four-model panel
Every brand is tracked across four frontier model surfaces: ChatGPT (GPT-4 family + the new search tool), Claude (Sonnet/Opus with web browsing), Perplexity (Sonar + cited search), and Gemini (Pro + AI overviews in Google search). These four cover >95% of agentic and AI-search traffic that intersects buying intent today.
Each model has a different retrieval temperament. ChatGPT favors authoritative entities with rich schema. Perplexity rewards llms.txt and dense citation graphs. Gemini overweights Google's own knowledge graph plus EEAT signals. Claude's browsing tool tends to reach for .edu, .gov, and well-structured Wikipedia adjacent pages. The gaps differ by model — and so do the fixes. We don't ship one playbook; we ship four model-aware patches.
Daily — citation polling
A scheduled walker hits each model with the brand's tracked-query set (typically 30–80 queries per brand: feature comparisons, "best X for Y" lookups, vendor evaluation, integration questions, "how do I" workflows). The query set is authored in week 1 of onboarding and tuned monthly as buyer-intent shifts.
For each (model × query × day) triple we capture:
- Whether the brand was cited — URL, source attribution, or named entity
- Position in the citation list — first, top-3, top-5, mentioned
- Citation context — flattering, neutral, comparative, or negative framing
- Adjacent brands — the competitive surface (who else got cited and where)
- Source pages cited — exactly which URLs the model surfaced (your domain vs. third-party coverage vs. competitor pages)
Raw walker output lands in your tenant's R2 nightly. We don't store the model responses verbatim long-term — cost and privacy concerns both apply — we store structured citation events: hashed query ID, model, ISO timestamp, citation slot, source URL, context vector. Storage runs ~50–200 KB/brand/day.
If the brand falls out of an answer set on a tracked query — citation-share drops below your
configured threshold for that query family — Sentinel-aaS fires a Discord alert in
#geo-drift within 15 minutes of detection. (See the
Sentinel-aaS methodology for how that bus is wired.)
Weekly — signal engineering
Citation gaps are diagnostic. They tell us what authority signals the model couldn't find — or found and discounted. Once a week, the engineer (the same one all month) reviews the gap report and ships structured fixes:
Schema PR pack
A small set of JSON-LD additions (Product, FAQ, Article, Service, Organization with
knowsAbout, HowTo when warranted) targeted at the gaps. Delivered
as a GitHub pull request if you grant repo access — fully reviewed, ready
for your tech lead to merge. Otherwise as a markdown patch your CMS team can drop in. Each
schema block carries a comment linking back to the gap report row that justified it, so
future engineers can see why the schema is there.
llms.txt + AI sitemap
A curated index of canonical sources at yourbrand.com/llms.txt, structured per
the emerging Anthropic + community spec. We list the canonical URLs you want models to
consult, the topics they cover, and the priority. Reduces hallucinated competitor mentions
because models that read llms.txt prefer your stated facts to inferred ones.
Citation-bait briefs
Gap-driven content briefs (typically 1–3 pages/month) with the structure that model crawlers reward: a clear entity-relation graph, comparison tables, FAQ JSON-LD, and citation- ready statistics. We don't write the pages — your team or your agency does — but we tell you what's worth writing, what schema to wrap it in, and which third-party sources to seed if the topic isn't yet adequately corroborated externally.
Third-party signal seeding
For Scale and Enterprise tiers: when the gap report shows a model is consistently citing a competitor's third-party coverage (a Reddit thread, a Stack Overflow answer, a comparison post on a publication) but no equivalent of yours exists, we identify the gap and hand it to your PR/community team with a brief on what the equivalent should look like.
Monthly — executive PDF
On the 1st of each month, a Cloudflare Workflow renders an executive PDF. Always the same shape so you can trend across quarters without re-orienting:
- Citation-share trend — share of voice across the 4-model panel, 30/60/90-day windows, broken down by query category
- Position distribution — % of citations in top-1, top-3, top-5, mentioned-only
- Competitive matrix — which brands gained or lost share against you, by query cluster
- Top 10 query gaps with cause-analysis: missing signal type (no schema / wrong schema / no llms.txt entry) vs. authority deficit (low third-party corroboration)
- Schema PRs shipped — repo, PR number, 1-line summary, merged Y/N
- Quarter-trend forecast based on signal velocity (citations earned per schema PR shipped, lagged 21–35 days)
- Recommendations — 3–5 prioritized next-cycle items, with effort/impact framing
Delivered to your inbox + posted to #geo-monthly in your Discord. The
Workflow that renders it is part of Cluster Ops
— open-source for Garnet customers, auditable end-to-end.
Day 1, Day 30, Day 90
Day 1 — onboarding kickoff
- 30-min intake call: tracked queries authored, baseline citation-share measured
- R2 tenant + GEO_MONTHLY workflow provisioned in your Cloudflare account
- Discord channel pair (
#geo-drift+#geo-monthly) bootstrapped - First nightly walker run completes within 12 hours of intake
Day 30 — first executive PDF
- 4 weekly drops: schema PR pack #1–#4, content briefs, llms.txt v1
- Baseline → 30-day delta in citation-share (typically 5–12 percentage points by Day 30)
- Mid-month tracked-query review — adjust queries based on real buyer-intent traffic
Day 90 — full panel maturity
- Citation-share lift typically lands in the 30–60% top-3 range on tracked queries (vs. baseline often <5%)
- Schema PR pack patterns reused as templates — week-over-week velocity climbs
- Quarter-end review: full readout, next-cycle plan, decision on tier escalation
What success looks like
Across the first 90 days, GEO Pro typically moves brand citation-share from ~<5% baseline to 30–60% top-3 placement on tracked queries. GEO Scale ($4,999/mo) targets >60% on a wider tracked set (typically 80–150 queries) plus the third-party signal seeding work above. GEO Enterprise ($14,999/mo) adds adversarial-query monitoring (catching deliberately misleading prompts your category may face), competitive-takedown briefs, and per-region tracking for brands operating across EU/US/APAC where retrieval temperaments diverge.
What it isn't
- Not SEO. Different signal stack, different feedback loop. SEO is the long tail of organic search ranked-list traffic; GEO is the citation behavior of generative answers. The two pipelines are complementary — strong SEO often correlates with strong GEO baseline citations — but the optimization work is distinct.
- Not LLM fine-tuning. We don't train models. We engineer the public signals models cite from. Fine-tuning the buyer's model is rarely the right move for citation share — it's expensive, model-specific, and doesn't propagate across the panel.
- Not a content mill. Most engagements ship 1–3 pages/month, all intentional, all gap-driven. We are not interested in volume; we are interested in the smallest set of additions that close the largest gaps.
- Not a "set it and forget it" subscription. The query set changes as your market changes. The retrieval temperament of each model changes when they ship updates (it changed measurably for ChatGPT in September 2025, for Gemini in November 2025). The weekly cadence is the unit of work, not a polite check-in.
FAQ
How fast does citation-share actually move?
The first measurable lift typically lands 3–4 weeks after the first schema PR ships, because that's the lag between schema appearing on your domain and the model's next retrieval index pass picking it up. Pages with strong existing inbound signals move faster (1–2 weeks); cold-start pages take 4–6 weeks. Variance comes mostly from how often each model refreshes its retrieval index against your domain.
Do you need access to our codebase?
For Pro and Scale: helpful but not required. We deliver schema PRs as GitHub pull requests if you grant access, otherwise as markdown patches your team applies. For Enterprise: yes — repo access lets us land schema, llms.txt, and routing fixes without round-tripping through your CMS team, which buys ~5–8 days/month back on the cycle.
What if our content is locked behind login or a paywall?
Generative engines don't cite content they can't retrieve. If your authority lives behind a wall, GEO can't engineer for it directly — we can engineer the public-facing surface (case studies, methodology pages, comparison content) so the model has something to cite that accurately reflects the gated material. Some Enterprise customers run an "AI-public summary" tier of their docs specifically for this.
How is this different from "AI SEO" agencies popping up everywhere?
Most "AI SEO" offerings are SEO agencies that added a chapter on AI overviews. They optimize for the same Google ranked-list signals and assume those propagate. They don't, fully — the citation behavior across the four-model panel diverges from organic ranking by ~30–50% on buyer-intent queries. GEO is built around the actual citation telemetry, not the SEO proxies for it.
Can we cancel mid-month?
Yes — monthly subscription, cancel any time, last full report still ships at month-end. The only thing that doesn't carry over after cancellation is the polling itself; your historical data stays in your R2 tenant. Most cancellations we've seen come from acquisitions, not dissatisfaction — but the door is open both ways.
Adjacent lanes
GEO is one of three production lanes. Customers running a serious citation-engineering program often pair it with:
- Audit Retainer — continuous architecture audit of the systems hosting the schema, llms.txt, and the pages models cite. Drift detection so a CMS migration doesn't quietly break your citation surface.
- Sentinel-aaS — the Discord bus that fires the citation-drift alerts and routes monthly PDF previews. The 15-minute drift alert pipeline is a Sentinel deliverable.
- Cluster Ops — for customers running their own LLM inference, the operations layer that keeps the rack honest. Includes the Workflow runtime that renders the monthly executive PDF.
See GEO pricing → See the 30-day onboarding walkthrough → or talk to engineering