Studio, not platform
Why the GEO layer should ship into the customer's repository, not run on someone else's domain — and why a retainer can stay on the right side of that line.
The SaaS GEO platforms sell you a layer that runs on their infrastructure
and renders into your domain at request time. Cancel the subscription and
the layer disappears — the JSON-LD they injected, the AI-sitemap they
maintained, the manifests they served from /.well-known/*. The work
survives only as long as the contract.
A studio engagement is the opposite trade: the work ships as commits into the customer’s repository, the customer owns the code after the engagement, the studio is not in the request path.
¶What Setup is
The studio’s one-time engagement is Setup. It is a fixed-price, scope-locked piece of work that ends with pull requests merged into the customer’s main branch. There is no runtime dependency on the studio’s infrastructure. There is no API key the customer holds against the studio. The deliverables are reviewable as diffs and survive the studio going quiet, raising prices, or pivoting to something else.
Setup’s job is to translate the Checker’s findings into changes the customer’s stack accepts, and to build out the signal coverage the site is missing. Next, Astro, Nuxt, Hugo, WordPress, custom — the studio adapts to the codebase, not the other way round. It ends with a before/after grade that proves the result.
What a SaaS GEO platform is
A SaaS GEO platform sells you a recurring subscription that runs structured data, manifests, and crawl-monitoring from its infrastructure into your domain. The customer’s domain becomes a thin client of the platform. The platform owns the methodology, the dashboard, and the rendering layer; the customer owns the subscription invoice.
The trade is convenience for lock-in. The platform handles the work; the customer pays $129–$299 per month indefinitely; the moment the customer cancels, the layer disappears and the AI assistants that were citing the site stop citing it again.
The math: $299 a month for 36 months is $10,764, and at the end of those 36 months the customer owns nothing. A $5,000 Setup leaves the customer owning the code on day one — and a retainer on top of it is still cheaper per month than the platform, while the code keeps accruing in the customer’s own repository rather than evaporating at cancellation.
¶Recurring revenue without becoming a platform
There is a real objection to a one-time-only studio: the work decays. AI standards move — ACP, AP2, x402, the Apps SDK and WebMCP are all young and shifting. The customer’s own site keeps changing. A site graded 92 in March drifts by autumn. So the studio offers a Retainer.
That sounds like the platform model returning through the back door. It is not, and the distinction is worth being precise about.
The line that separates a studio from a platform is not recurring versus one-time. It is where the work lives. A platform’s layer runs on the vendor’s infrastructure and renders into your domain; cancel and it vanishes. A studio retainer maintains code that already lives in your repository; cancel and every line of it stays.
¶The retainer is a monitoring-and-judgement engagement, not a dashboard subscription. Each month it ships a report you can read in 90 seconds — the Checker score delta, AI-crawler server-log analysis, a citation prompt-panel, AI-source referral traffic — and the maintenance work goes into the customer’s repo as PRs, exactly like Setup. There is no login habit to form, no layer in the request path, nothing that disappears at cancellation. It is sold after a Setup, on the proven result, with a three-month minimum because AEO results take 60–90 days to land.
So the ladder has three rungs:
- Free Checker — diagnose for free. Letter grade in seconds.
- Setup — from $5,000 one-time, scope-based, fixed-price. Adapts an existing codebase for AI-agent readiness; PRs into the customer’s repository; before/after grade.
- Retainer — $1,500–$7,500/mo, sold after a Setup. Keeps the score from decaying and tracks whether AI citations are moving. A monthly report, not a dashboard.
The retainer is recurring revenue. It is also, strictly, still the studio model: it maintains and extends code in the customer’s repository — it does not move that code onto the studio’s infrastructure.
What this means for AI-agent readiness
Two things follow from the studio model, both load-bearing.
First, standards-only testing. We grade against published, vendor-neutral standards: schema.org, llms.txt, ai-plugin.json, OpenAPI 3.1, MCP, IndexNow, ACP, AP2, x402, OpenAI Apps SDK, Google WebMCP, robots.txt. We do not test against proprietary protocols, invented well-known files, or trademarked methodologies. The standards-disclosure line on every report is the permanent rebuttal to platforms that do.
Second, deterministic recommendations. The Quick Wins panel is
generated from a static failure → fix lookup table, not from an LLM. A
recommendation never contradicts the evidence panel. If the evidence panel
shows /.well-known/ai-plugin.json returning 200 OK with valid content,
the Quick Wins panel does not say “deploy ai-plugin.json.” This sounds
trivial. It is the single biggest accuracy bug in the category, and we
position against it directly.
Both properties — standards-only and deterministic — are easy to maintain in a studio funnel where the report is the product and the report is true.
Talk to the studio
The Free Checker is the entry point. If you want the failing signals fixed in your repository, Setup starts at $5,000 and scopes to the stack. If you want the result to keep holding as the standards move, the Retainer keeps it ahead. There is no portal, no log-in, no layer running on your domain that disappears if we go quiet. The code is yours — on day one, and after every month of the retainer.