Updated Jun 27, 2026
Answer engine optimization (AEO) is priced one of two ways. Hourly or flat retainers bill for effort, commonly a few thousand dollars a month regardless of whether your brand gets cited. Outcome-leaning engagements start with a fixed-scope audit, then a retainer tied to citation work. FORKOFF's entry point is a sandbox audit that maps where ChatGPT, Perplexity, and Google AI Overviews currently cite your category and where you are missing, followed by a retainer by application with a 90-day minimum. The reason scope-first beats pure hourly in AEO is incentive alignment: citation-engineering is judged by whether answer engines actually quote you, so the work should be priced against that outcome, not against hours logged.
Per our own AI Citation Index, a 50-prompt buyer-intent cluster run against 5 engines, forkoff.xyz moved its average cite rate from 22 percent to 34 percent in four weeks. FORKOFF AI Citation Index 2026
Traditional SEO is often priced on a deliverables checklist: so many pages, so many links, so many hours. AEO is judged differently, because the unit of success is a citation inside an AI-generated answer, not a ranking position on a page of ten blue links. That makes effort-based billing a poor fit: you can log many hours and still not get quoted. A scope-first model, a fixed audit followed by an outcome-tied retainer, prices the work against the thing the buyer actually wants, which is to be the source an answer engine repeats when their customer asks.
AEO pricing models
| Model | How it is billed | Price | What you get |
|---|---|---|---|
| Hourly or flat retainer | Effort, monthly | Commonly a few thousand dollars a month | Work logged regardless of whether citations are won |
| Sandbox audit (FORKOFF) | Fixed scope, one-time | $1,500 | A map of current AI-citation coverage and the gaps to close |
| Retainer by application (FORKOFF) | Monthly, scoped to your query set | By application, 90-day minimum | Ongoing citation-engineering with a cap on concurrent engagements |
AEO success is a citation inside an AI answer, not hours logged, so scope-and-outcome pricing aligns the work with the result. Cost scales with query-set breadth, citation competitiveness, and the structured-content and schema work required.
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