{"slug": "generative-engine-optimization-agency-what-should-you-actually-be-paying-for", "title": "Generative Engine Optimization Agency: What Should You Actually Be Paying For?", "summary": "A generative engine optimization (GEO) agency should be paid for work that changes which sources an AI engine uses when answering buyers, and for measurement proving the change, according to an article in the AI Visibility Engineering cluster. The piece identifies six kinds of GEO work—monitoring, diagnosis, content and technical remediation, authority and corroboration, measurement, and ongoing experimentation—and states that only diagnosis and remediation directly move the number, with measurement as proof, monitoring as input, authority as a slow multiplier, and experimentation as an advanced tier.", "body_md": "# Generative Engine Optimization Agency: What Should You Actually Be Paying For?\n\nA generative engine optimization agency can sell you monitoring, diagnosis, remediation, authority work, measurement or experimentation. Only some of those move the number. Here is how to tell which you are buying.\n\n## Why this matters\n\nA generative engine optimization agency can sell you monitoring, diagnosis, remediation, authority work, measurement or experimentation. Only some of those move the number. Here is how to tell which you are buying.\n\nIn this cluster\n\n## Cluster context\n\nThis article sits inside AI Visibility Engineering.\n\n[Open topic hub](/topics/ai-visibility-engineering)\n\nEntity graphs, schema architecture, and citation mechanics for sub-DR-20 sites competing on AI citations, not SERP rank.\n\nSEO optimizes for rank. Answer engines optimize for citation-worthiness. This cluster is the engineering playbook for the second game, sized for operators, not enterprise SEO teams.\n\nHow ChatGPT and Perplexity Decide Which Sources to Cite\n\nHow answer engines like ChatGPT and Perplexity decide which sources to cite: six measurable factors, 2026 platform data, and the fix for each one.\n\nEntity Optimization for Brands in AI Search\n\nRank is a single-page game. Entity coherence is the compounding game. How sub-DR-20 brands engineer a Person + Organization graph that AI search engines actually cite.\n\nSchema.org for Answer Engines, the 40 Properties That Matter\n\nA tactical guide to the Schema.org properties answer engines actually read. Which fields move citation decisions, which are noise, and how sub-DR-20 operators compress a full JSON-LD graph into the forty that matter.\n\nA generative engine optimization agency should be paid for work that changes which sources an AI engine uses when it answers your buyers, and for measurement that proves whether the change happened. Six kinds of work get sold under the GEO label. Two of them are the reason you are here. Two are supporting work. Two are mostly vanity. This page separates them so you can read a proposal and see what you are paying for.\n\nThe mechanics of how engines select sources are covered in [the answer engine optimization explainer](/blog/aeo-answer-engine-optimization-explained). This page assumes you have budget and are deciding where it goes.\n\n## The six kinds of GEO work\n\n| Kind of work | What it produces | Moves the number? | Fair price shape | \n|---|---|---|---|\n| Monitoring | A recurring read of who engines name, per question | No, but you cannot manage without it | Low monthly, or bundled with measurement | \n| Diagnosis | Why your pages lose on each question, cause labelled | Indirectly. It decides what to fix | Fixed fee, once, with a written output | \n| Content and technical remediation | Pages that answer questions, entity clarity, schema, crawler access | Yes, when aimed by a diagnosis | Project or sprint, scoped to a repair list | \n| Authority and corroboration | Third-party mentions engines treat as evidence | Yes, slowly | Project, honest about timing | \n| Measurement | Before and after, same method, noise floor stated | It is the proof | Included, or it is not an engagement | \n| Ongoing experimentation | Testing which changes engines respond to | Yes, for teams past the basics | Retainer, justified by a measured baseline | \n\nDiagnosis and remediation are the work. Measurement is the proof. Monitoring is the input. Authority is the slow multiplier. Experimentation is the advanced tier that only makes sense once the first four exist.\n\n## What each one looks like when it is done well\n\n**Monitoring** asks a fixed set of buyer questions on each engine, on a schedule, and records every company and source named. Done well, it is reported per engine and keeps the question list stable so reads are comparable. Done badly, it is a single blended score that moves for reasons nobody can explain.\n\n**Diagnosis** reads each absence and labels its cause. In practice there are four: the engine cannot read the page, the page has no quotable passage answering the question, no third party corroborates the claim, or a competitor page simply answers better. Each cause needs a different repair, and mislabelling wastes the whole budget. This is the deliverable you should own in writing before any retainer.\n\n**Remediation** is what most agencies actually sell, and it is legitimate when aimed. A page that directly answers a buyer question, in plain text, with the company named as a vendor, is the single highest-yield repair. Schema, crawler access and entity clarity support it. Ten new blog posts with no diagnosis behind them are not remediation. They are volume.\n\n**Authority and corroboration** means getting the claim on your page repeated somewhere an engine trusts: a comparison article, a directory, a community answer. In [a seven-site audit](/blog/ai-citability-audit-what-predicts-citations), domain authority did not predict citations, so this is about the right mention, not more links.\n\n**Measurement** repeats the baseline with the same method and reports the change against a stated noise floor. Presence moves between pulls. An agency that has not measured its own variance cannot tell you whether a change is real.\n\n**Experimentation** tests which changes an engine responds to in your category and drops the ones that do not. It only pays once the basics are in place, and it should be sold as a retainer with a measured reason to exist.\n\n## Deliverables that look like work but are not\n\nNone of this is an accusation against any vendor. These are patterns, and every one of them can be sold in good faith by someone who has not measured.\n\n- **An AI visibility score with no source list.** You cannot act on it. Ask for the sources behind the score.\n- **A monthly content volume commitment before a diagnosis.** Pages aimed at nothing.\n- **“AI-ready” schema packages.** Schema helps a page that already answers the question. It does not rescue one that does not.\n- **Prompt lists with no engine reads.** Which questions to ask is the easy half.\n- **Guarantees.** Any promise of citation counts or “AI rankings” is a promise the vendor has not tested against its own variance.\n- **Blended engine reporting.** ChatGPT, Perplexity and Google AI Overviews disagree sharply. In[my own 47-answer study](/blog/why-chatgpt-is-not-citing-your-website) , my product was named 3 times, all on one engine and none on the other two. One number would have hidden that.\n\n## What a fair engagement is shaped like\n\n1. **Free or cheap first read.** A sample of buyer questions run before any money changes hands, so both sides see real answers.\n2. **Fixed-fee diagnosis** with a written repair list you own.\n3. **Scoped remediation** against that list, smallest repairs first, priced as a sprint.\n4. **Repeat measurement** with the same method, and a plain statement of whether the change is inside the noise floor.\n5. **Retainer only if step 4 justified it.**\n\nThat ladder is how I structure it, and the [AI Discovery Review](/services/ai-discovery) is step one. Step two is the [$1,500 Citation Gap Diagnostic](/services/generative-engine-optimization). If the first read shows no gap worth closing, the honest outcome is that you spend the budget elsewhere.\n\n## When you should not hire a GEO agency at all\n\n- **Your pages do not answer your buyers’ questions yet.** Write the pages. Measure afterwards.\n- **Your site has crawl or performance problems.** Engines read the same pages Google does. A traditional SEO agency fixes that faster and cheaper.\n- **Your category has almost no assistant-driven research.** Some B2B categories still buy from a procurement list. Check your sales calls before buying visibility in a place buyers are not.\n- **You want a score for a board slide.** Monitoring alone is cheap. Buy that and skip the retainer.\n\n[The decision page on B2B SEO agencies versus AI search specialists](/blog/b2b-seo-agency-vs-ai-search-specialist) walks through which of these you are in.\n\n## Frequently asked questions\n\n**Is GEO just SEO with a new name?** Partly. The page-quality foundations transfer. What is new is that the outcome is a generated answer that names companies rather than a ranked list of URLs, and rank reporting cannot see it.\n\n**How much should a diagnosis cost?** Enough to cover fifteen to twenty questions across three engines read by a person, and no more. If it is bundled into a retainer so you cannot buy it alone, that is a sign.\n\n**Can I do the monitoring myself?** Yes. The method is four steps and needs no tooling. I use the [citability.dev](https://citability.dev) panel for the pulls on my own products, but a spreadsheet works for a first read.\n\n· Sources & further reading\n\n## Sources & Further Reading\n\n### Sources\n\n- [Why ChatGPT Is Not Citing Your Website chudi.dev](https://chudi.dev/blog/why-chatgpt-is-not-citing-your-website) Shows what a generative engine optimization diagnosis measures, separating monitoring from remediation.\n- [AI Citability Audit: What Predicts Citations chudi.dev](https://chudi.dev/blog/ai-citability-audit-what-predicts-citations) Seven-site audit showing domain authority did not predict AI citations, the evidence behind the buyer questions here.\n\n### Further reading\n\n- [How to Choose the Best AI SEO Agency for B2B SaaS /blog/how-to-choose-best-ai-seo-agency-b2b-saas](/blog/how-to-choose-best-ai-seo-agency-b2b-saas) There is no best AI SEO agency in the abstract. Here is the evidence to require, the measurements an engagement must report, the claims to distrust, and the cases where a traditional SEO agency is the better choice.\n- [SEO for SaaS in the AI Search Era: What Traditional SEO Does Not Measure /blog/seo-for-saas-ai-search-era](/blog/seo-for-saas-ai-search-era) Traditional SEO for SaaS still works. What changes in the AI search era is that buyers also get answers from ChatGPT, Perplexity and Google AI Overviews, and rank and traffic reports cannot see who those answers name.\n- [AI Visibility Audit for B2B SaaS: Find Where Buyers See Competitors Instead /blog/ai-visibility-audit-b2b-saas](/blog/ai-visibility-audit-b2b-saas) An AI visibility audit tells a B2B SaaS company which buyer questions ChatGPT, Perplexity and Google answer with a competitor, and whether the gap is worth fixing. Here is what a real one measures.\n- [B2B SEO Agency vs AI Search Specialist: Which Problem Do You Actually Need Solved? /blog/b2b-seo-agency-vs-ai-search-specialist](/blog/b2b-seo-agency-vs-ai-search-specialist) A B2B SEO agency and an AI search specialist solve different problems. This decision page tells you which one you have, when you need both, and when you should spend the money somewhere else first.\n- [Schema.org for Answer Engines, the 40 Properties That Matter /blog/schema-org-answer-engines-guide](/blog/schema-org-answer-engines-guide) A tactical guide to the Schema.org properties answer engines actually read. Which fields move citation decisions, which are noise, and how sub-DR-20 operators compress a full JSON-LD graph into the forty that matter.\n\nReading Path\n\n## Continue the AI Visibility Engineering track\n\n[Go to hub](/topics/ai-visibility-engineering)\n\nContextual next reads\n\nHow ChatGPT and Perplexity Decide Which Sources to Cite\n\nHow answer engines like ChatGPT and Perplexity decide which sources to cite: six measurable factors, 2026 platform data, and the fix for each one.\n\nEntity Optimization for Brands in AI Search\n\nRank is a single-page game. Entity coherence is the compounding game. How sub-DR-20 brands engineer a Person + Organization graph that AI search engines actually cite.\n\nSchema.org for Answer Engines, the 40 Properties That Matter\n\nA tactical guide to the Schema.org properties answer engines actually read. Which fields move citation decisions, which are noise, and how sub-DR-20 operators compress a full JSON-LD graph into the forty that matter.\n\nWant more of this in your Google results?\n\n## What do you think?\n\nI post about this stuff on LinkedIn every day and the conversations there are great. If this post sparked a thought, I'd love to hear it.\n\n[Discuss on LinkedIn](https://www.linkedin.com/in/chudi-nnorukam)", "url": "https://wpnews.pro/news/generative-engine-optimization-agency-what-should-you-actually-be-paying-for", "canonical_source": "https://chudi.dev/blog/generative-engine-optimization-agency-what-you-pay-for", "published_at": "2026-09-08 00:00:00+00:00", "updated_at": "2026-09-08 09:01:27.113508+00:00", "lang": "en", "topics": ["ai-products", "ai-tools"], "entities": ["ChatGPT", "Perplexity", "Schema.org"], "alternates": {"html": "https://wpnews.pro/news/generative-engine-optimization-agency-what-should-you-actually-be-paying-for", "markdown": "https://wpnews.pro/news/generative-engine-optimization-agency-what-should-you-actually-be-paying-for.md", "text": "https://wpnews.pro/news/generative-engine-optimization-agency-what-should-you-actually-be-paying-for.txt", "jsonld": "https://wpnews.pro/news/generative-engine-optimization-agency-what-should-you-actually-be-paying-for.jsonld"}}