Become the source AI engines cite.
GEO — generative engine optimization: earning citation and accurate representation inside AI-generated answers. Glossary
When buyers ask ChatGPT, Gemini, or Perplexity to explain your category or shortlist vendors, we make your company easier to understand, harder to confuse, and more useful to cite.
We build the entity structure, reference content, and measurement loop inside your existing stack — then hand it to your team. Operator-led by Evgeny Popov, in the category we actually know: AdTech, MarTech, and data.
Three outcomes. One system.
For AdTech, MarTech, data, and AI companies whose category is too technical for a generalist GEO agency: the entity, evidence, and measurement infrastructure that makes the company accurate, citable, and shortlist-ready across AI answers.
- 01
Accurate representation
Correct the stale positioning, hallucinated capabilities, and brand collisions in how engines describe you — so the AI version of your company is the current one.
- 02
Citation authority
Build the reference material engines can retrieve, quote, and attribute — self-contained claims, dated records, and category pages written to be the source.
- 03
Shortlist readiness
Improve how you appear in the questions that create pipeline: category explainers, vendor comparisons, and "who should we look at" answers.
Who this is for.
Companies whose buyers now start with an AI answer instead of a search results page. Strongest fit follows the site's ICP: Series B scaleups as primary fit, Series A PMF as secondary, selective Series C / Growth on board-grade decisions.
- AdTech and MarTech platforms whose category — curation, agentic, CTV, retail media — is being redefined inside AI answers right now.
- Data and measurement companies whose value proposition has to survive being summarized by a model — including post-M&A and post-pivot vendors whose AI description still sells the old company.
- B2B technology companies with an entity problem: ranking on Google but absent from generated answers, or a name that AI blends with someone else’s.
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"ChatGPT describes us using our competitor’s product line."
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"We rank on page one, but AI answers never mention us."
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"Perplexity cites our competitors for a category we helped invent."
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"Honestly? Nobody here has checked what AI says about us."
Not sure the profile fits? Start with who the practice is built for or the problems it takes on.
Three packages. Start with the baseline.
Each package runs through one of the practice's standing engagement shapes — same method, scoped to this work. Typical scope below; exact scope is set per engagement.
- 01
AI Answer Baseline
2–3 weeks
The dated record of how engines answer your buyers’ questions today — and what would move it.
- Typically 50–100 prompts across category, comparison, and vendor-selection questions
- 3–5 competitors, four engine families (ChatGPT, Gemini, Perplexity, AI Overviews)
- Accuracy audit, entity and schema review, prioritized gap map
- Dated and re-runnable — the before-record every later claim is measured against
- 02
Answer Authority Build
6–8 weeks
The build: entity, evidence, and measurement infrastructure shipped inside your existing stack.
- Entity graph and disambiguation shipped to your site — schema, anchors, machine surfaces, llms.txt
- Citable layer on the first tranche of pages (typically 10–25) plus one or two category reference pages
- Delivered as pull requests to your repo or through your team — your stack, your review gates
- Measurement wiring: probe cadence, Search Console reference queries, AI-referral tracking
- 03
Visibility Operations
3 / 6 / 12 months
The maintenance rhythm that keeps the answers current as engines and your category change.
- Baseline re-runs on cadence; movement — or its absence — goes on the record
- Freshness drills, new category questions, content and entity updates
- Collision monitoring where a name overlap exists
- Team enablement: templates, checklists, and agents so your team runs it without us
Diagnose. Engineer. Publish. Measure.
Four stages, in order — the mechanics live in the workstream detail below for technical evaluators.
- 01
Diagnose
The AI answer baseline: how engines describe you, where they are wrong, and which gaps rank.
- 02
Engineer
Entity graph, disambiguation, citable structure, machine surfaces — built in your stack.
- 03
Publish
Category reference pages and converted key pages, written to be retrieved and attributed.
- 04
Measure
The same probe re-run on cadence, plus Search Console and AI-referral signal — honestly reported.
A public reference implementation.
Most AI-visibility vendors report their own metrics and show you other people's case studies. We show you our production system and its public history — it proves the machinery on our own brand; client engagements apply it to yours. Every claim below checks out in this site's page source, on a linked live page, or as a dated observation.
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The entity resolves
This site’s Organization graph — disambiguation, founding data, verifiable anchors — is in the page source right now. On July 23, 2026, Google’s AI Overview described No Fluff Advisory in language closely matching that schema and cited the independent standards listing used as an authority anchor.
See the entity page → -
The citable layer runs at scale
66 essays on this site carry a fact-grounded citable block — a self-contained claim plus quotable facts, each verified against the essay text — wired into structured summaries and abstract markup. Open nearly any essay and scroll to the end.
See one live → -
The answer engine is public
Ask Evgeny answers questions from this corpus only — retrieval-grounded, every answer cited, refusing when coverage is missing — over site chat and MCP. We published the entire build as a tutorial.
Read the build guide → -
The dated instruments compound
The AdCP Ecosystem Tracker, Living Maps, and On the Record are maintained, dated reference surfaces — the format engines cite because it shows its work and its revision history.
See the tracker → -
The measurement loop is closed
A weekly Search Console pulse and a monthly editorial agent that grades its own prior recommendations against outcomes — the anti-dashboard. Also published as a tutorial.
Read how it works →
What we won't sell: guaranteed citation counts, "prompt presence" percentages, or a visibility score nobody outside the vendor can audit.
AI answers are probabilistic and personalized; any specific number promised in advance is a number the vendor generates and grades themselves. What this engagement commits to instead: a dated record of how engines answered before, the shipped system, and the same probe re-run on cadence — so the movement, or its absence, is on the record either way. That standard is not a hedge. It is the same one this site applies to everyone else's self-reported metrics.
The ten workstreams, in detail.
Most engagements need five or six of these, not all ten. The point is that they connect: a citable layer without entity clarity gets quoted without attribution; authority anchors without freshness decay into last year's answer.
01 · AI answer baseline
How ChatGPT, Gemini, Perplexity, and Google’s AI Overviews actually describe you today, across the buyer questions that matter — recorded as a dated, re-runnable probe, not a screenshot deck.
02 · Entity engineering
Organization and Person graphs, disambiguation, sameAs anchors, and independent sources that corroborate your identity, expertise, and category role — so the machine knows exactly who you are.
03 · Citable content layer
Self-contained claims and fact blocks on your key pages, written to be lifted verbatim with attribution — structured summaries engineered for extraction, not hoped for.
04 · Machine surfaces
llms.txt, clean structured data, answer-shaped reference pages — and where it earns its keep, an answer engine on your own corpus, grounded and cited, like the one running on this site.
05 · Authority anchors
The external corroboration that moves entity resolution: standards participation, verifiable registries, real third-party listings. Not backlink farms — anchors a skeptical model can check.
06 · Category language
The vocabulary AI uses for your category, mapped — and dated reference pages built to become the source engines cite when buyers ask how your category works.
07 · Freshness system
Dated records, visible revision histories, and a maintenance drill — because for time-sensitive questions, a source that shows when and why it changed is more useful than an undated page whose accuracy cannot be assessed.
08 · Brand collision defense
When your name is shared or adjacent to another company’s, structured disambiguation plus a monitoring probe that flags contamination before your prospects see it — the same probe this site runs weekly on its own name.
09 · Honest measurement
A dated answer-probe cadence, Search Console reference queries, and AI-referral traffic — what can actually be measured, stated plainly, with no invented visibility scores.
10 · Team handover
The templates, checklists, and scheduled agents so your team runs the system after the engagement ends — including the measurement loop that grades the work.
FAQ.
What is GEO (generative engine optimization)?
GEO is the practice of making a company visible, accurately described, and citable inside AI-generated answers — ChatGPT, Gemini, Perplexity, and Google’s AI Overviews. Where SEO optimizes pages to rank as links, GEO engineers the entity signals, content structure, and authority anchors that determine whether an engine mentions, cites, or recommends you at all. The same discipline also travels under other labels — AEO (answer engine optimization), AI search optimization, LLM optimization; the vocabulary is still settling, and the work is the same.
How is GEO different from SEO?
SEO earns a position in a list of links; GEO earns inclusion in a synthesized answer. The mechanics differ: visibility in generated answers turns on entity clarity, structured data, citation-worthy self-contained claims, and verifiable third-party anchors more than on keyword targeting or raw backlink volume. The two are complementary — this site runs both — but they are not the same work.
Can you guarantee my company appears in ChatGPT answers?
No — and you should distrust anyone who does. AI answers are probabilistic, personalized, and change between model versions; a vendor promising a specific citation count in 90 days is selling a number they generate and grade themselves. What is controllable: the entity signals, citable structure, and authority anchors that raise the odds — and a dated probe record that shows honestly whether the needle moved.
What does AI visibility work actually involve?
Concretely: a dated baseline of how engines answer your buyers’ questions; Organization and Person schema with disambiguation and verifiable anchors; citable claim-and-facts blocks on key pages with structured summaries; llms.txt and clean machine surfaces; category reference pages built to be cited; and a measurement cadence grounded in Search Console and AI-referral data.
Why an AdTech/MarTech specialist instead of a generalist GEO agency?
In this category the hard part is not the schema — it is the substance. Buyers ask AI about curation, clean rooms, AdCP, measurement standards, and agentic workflows. No Fluff Advisory operates in that territory: its principal is a founding AdCP member, and the practice runs a live registered agent and maintains a standards reference atlas built to be cited. A generalist can mark up pages; a specialist can write the category reference an engine treats as the source.
Have you done this for yourselves?
Yes — that is the pitch. The techniques that carry this page run in production on nofluffadvisory.com: the entity graph in every page's source, the citable layer on 66 essays, the Ask Evgeny answer engine, llms.txt, the dated reference instruments, and the self-grading measurement agents. Call it a public reference implementation: it proves the machinery on our own brand, in public — client engagements apply it to yours.
Deeper background: the answer-engine build guide, the measurement-agent tutorial, the operator track record on the proof page, and who runs the practice. Page last reviewed July 23, 2026.
Want to know what
AI says about you?
The first deliverable of any engagement is the baseline: a dated record of how ChatGPT, Gemini, Perplexity, and AI Overviews answer your buyers' questions today — including the ones you'd rather not see. The gap map sets the scope from there.