SEO Gets You Ranked.
GEO Gets You Chosen.
Here's how Generative Engine Optimization complements your SEO, rather than rival it.
Classic SEO
- Gets you ranked on the results page
- Optimizes pages and keywords
- Builds authority and content signals
- Success measured via clicks to your website
AI Search Optimization
- Gets you featured in the answers on LLMs
- Extends optimization to 3rd party sources cited on LLMs
- Compounds the authority SEO already built
- Success measured via citations, mentions, and accurate sentiment
One discipline, two surfaces: keep ranking on the results page — and get chosen in the answer above it.
Our Unique Methodology
The Authority Intelligence System is our proprietary five-pillar GEO methodology. It is how we go beyond generic AI optimization to build the commercial intelligence, signal architecture, forensic measurement, and competitive accountability that most GEO engagements lack entirely.
01
Decision-Node Prompt Architecture
We map the commercial landscape before anything else: your product lines, customer segments, and competitor set, modeled into the exact prompt universe your buyers are using.
02
Customer Signal Grounding
Every prompt is anchored in real behavior. We use your Google Search Console data and live query signals to reflect how your actual buyers ask questions.
03
Consensus Resonance Engineering
LLMs cite brands they find consistently across the web. We engineer that consistency, distributing your message across press, communities, directories, and third-party sources so AI finds the same credible signals wherever it looks.
04
AI Sentiment Forensics
Being mentioned isn't enough if you're being framed poorly. We audit how AI describes your brand across every decision node, detect cross-platform bias, and trace negative perceptions to their root cause. Each client receives a Sentiment Authority Index (SAI) score and a remediation strategy.
05
Competitive Share-of-Model Framework
We define what winning looks like before we start. Your target share of model — how often AI recommends you versus named competitors — becomes the OKR every pillar works toward.
Three Findings From a Recent Audit
Delivered to a mid-market software brand before a single placement was made — the level of
depth every engagement starts with.
0%
Share Of Voice
Across 360 answers (120 prompts × 3 runs — 70% long conversational asks, 30% short queries), the brand appeared mostly on branded prompts. On asks like "which platform should we move to?", it surfaced twice.
Why it matters
These are bottom-funnel prompts — the AI era's page one. Every answer that names a competitor is a shortlist the client was never on.
The fix
Prioritize the 12 decision nodes with partial presence, engineer consistent signals across the sources those answers draw from, and re-test the full prompt set weekly.
0/11
Mention Rate
Answers recommending competitors trace back to a small, repeating set of third-party sources. The client appears in two — one with a three-year-old description.
top-10 listicle
review platform
community thread
industry publication
comparison table
directory listing
buyer's guide
4 more…
Why it matters
LLMs don't invent recommendations — they synthesize from sources they trust. A brand absent from the citation layer is invisible at answer time.
The fix
Consensus Resonance Engineering: earn placement or updates on the nine missing sources through manual outreach, then reinforce the two that already cite the client.
0/100
Sentiment Accuracy
AI Sentiment Forensics found no hostile framing, but descriptions lag reality: retired pricing quoted on two engines, and a discontinued product named as the flagship.
stale
Pricing model quoted from 14 months ago
stale
Discontinued product named as flagship
ok
No negative or hostile framing detected
Why it matters
Sentiment decides how you're recommended, not just whether. "Legacy" framing quietly demotes a brand in comparison answers — the buyer never sees why.
The fix
Trace each stale claim to its source document, correct it at origin, and refresh the entity signals models ground on. Re-score SAI at every 30-day re-test.
One Prompt, Tracked to Position #1
Every prompt in your map gets this treatment: weekly re-runs across all engines,
logged and charted. Sample trajectory — one decision-node prompt over eight weeks.
Average slot inside the recommendation list — #3 → #1 across the eight-week sample.
4 → 8 engines citing within eight weeks — coverage compounds as consensus signals spread.
From Audit to Answer Ownership
The audit ends in a plan with a defined win condition — your target share of model becomes
the OKR every deliverable works toward. Sample below, from the same anonymized audit.
Unblock & correct
AI crawler access opened, schema and entity signals shipped, stale claims corrected at their source documents.
Seed the citation layer
First placements live on the missing sources; answer-first content published against priority decision nodes.
Share of model moves
Mention-rate and position targets hit on priority nodes; weekly re-tests confirm the trend across engines.
Win the 4 listicles and the comparison table these answers draw from; publish an answer-first comparison hub.
Correct stale pricing at its source; seed current head-to-head data on the review platform models quote.
Structured pricing page with semantic schema; place verified pricing data in the two buyer's guides cited.
Highest-leverage node: earn placement in"alternatives" roundups on all three engines citing them.
Modular, extractable guide built from the audit's prompt phrasing; expert quotes distributed to trade press.
Case-study placements in the industry publication; entity signals linking brand → product → segment.
Sentiment remediation: refresh review-platform listing, current UGC seeding, flagship-product correction.
The single highest-value node. Two placements on the most-cited listicles move it furthest per dollar.
Fix the stale pricing claim — a correction, not a campaign. Cheapest meaningful win in the plan.
One roundup placement per month; compounds into the"best of" node as consensus builds.
6 Success Metrics For LLM Ranking & Citation
How to measure visibility on LLMs in generative search.
Citation Frequency
How often AI engines cite your content as a source inside their answers.
AI Share of Voice
Your presence versus competitors across the full decision-node prompt map.
Mention Rate
The share of relevant buyer prompts where your brand is named at all.
Position in Responses
Where you sit when multiple brands are recommended. #1 gets the click that exists.
Sentiment Accuracy
If models describe you correctly and positively; scored as your SAI.
Engine Coverage
How many of the 8 engines cite you; coverage compounds as consensus spreads.
The Citation Layer Is Our Home Turf
GEO's hardest problem is earning trusted third-party coverage, which is exactly the muscle we've built for over a decade. The same manual outreach behind 400,000+ placements now powers the citation layer AI models learn from.
Manual outreach, never marketplaces — every placement earned one publisher at a time, vetted against eight quality checks.
We don't compete with you — no content services, no ads. We plug into your team, which is why 2,600 agencies send us their clients.
Built to scale, by hand — 5 placements a month or 500, the same vetting runs every single one.
0K+
links & placements built to date
0K+
new placements every month
0
agencies white-label our work
0+
industries served worldwide
Frequently Asked
Questions
GEO is the process of optimizing your brand and content so it gets cited and recommended by AI tools like ChatGPT, Gemini, and Perplexity, not just ranked on Google.
SEO focuses on rankings and clicks. GEO focuses on being mentioned inside AI-generated answers, where users increasingly get information without visiting websites.
We optimize for leading LLMs, including ChatGPT, Google Gemini, Perplexity AI, Claude, and Microsoft Copilot, depending on where your audience is searching.
We improve your content structure, authority signals, entity presence, and external references so LLMs recognize and trust your brand as a source worth citing.
No ethical provider can guarantee exact placements. However, we significantly increase your probability of being cited by aligning with how LLMs retrieve and generate answers.
Most clients start seeing early citation signals within 4–8 weeks, with stronger and more consistent presence building over 2–3 months.
Not necessarily. While SEO helps, GEO can work independently by building AI-friendly authority and structured content, even for newer websites.
We track brand mentions in AI responses, citation frequency, visibility across prompts, and assisted traffic/conversions from AI platforms — using in-house automated tools to ensure you're cited right.
If your customers ask questions online, GEO matters. It works especially well for B2B, SaaS, eCommerce, local services, and personal brands.
No. GEO complements SEO. The best strategy today is hybrid visibility — ranking on Google and being cited by AI. Leaving one for the other is never recommended.
Yes. Schema markup helps AI models understand your entities, services, and context. While it doesn't directly guarantee citations, it increases clarity, trust, and retrievability — improving your chances of being referenced by LLMs.