I run six content sites. QAJourney, EngineeredAI, RemoteWorkHaven, MomentumPath, HealthyForge, HobbyEngineered. I have been optimizing all of them for LLM visibility for the better part of a year, which means I have seen what bad schema looks like at scale, what happens when citation readiness is ignored, and what Cloudflare AI Crawl Control shows when the infrastructure is wrong. I built tools to make that audit faster. Then I started offering the same work as a service.
This post is what that service actually looks like. Not a pitch. Not a framework someone invented on a whiteboard. The stack I am running, the pricing I am charging, and the early reality of selling it.
What the First Audits Showed Me
When I audited my own six sites I found the same problems in different combinations across all of them. Plugin-generated schema with inconsistent author and publisher fields across post types. Heading structures that buried the answer three paragraphs deep. Canonical chains that broke at the syndication layer because Dev.to or Medium was getting crawled before the source URL was established. These are not edge cases. They are the default state of most content sites that have never been looked at from a retrieval perspective.
The fix on one property took forty minutes. Clean JSON-LD injected manually, heading hierarchy corrected on the top ten posts by crawler activity, canonical links verified across every syndication surface. Cloudflare AI Crawl Control showed the change in allowed requests within the next crawl cycle. That is the work. It is not complicated but it has to be done deliberately and in the right order.
The Service Stack
Schema audit and implementation. Most sites have schema generated by plugins that introduce inconsistencies invisible until you read the raw output. The author entity has different formatting across post types. The publisher field is missing on some templates. The mainEntityOfPage points to the wrong URL. I audit the raw JSON-LD on every content type and deliver clean markup ready to inject. The schema generator I built on EngineeredAI handles Article, FAQ, and HowTo types. That is part of the toolkit I bring to every engagement.
schema-generator — free tool by EngineeredAI. Source on GitHub ↗
Citation readiness audit. Schema is the structural signal. Citation readiness is whether the content itself is formatted in a way that a retrieval system can extract a clean, attributable answer from. The AI citation readiness tool audits heading hierarchy, answer-first structure, entity clarity, and semantic density. On one of my own properties the tool flagged twelve posts with buried answers. Fixing the heading structure on the top five by traffic took two hours. That is a deliverable a client can see and act on immediately.
ai-citation-readiness — free tool by EngineeredAI. Source on GitHub ↗
Crawler visibility check. I pull Cloudflare AI Crawl Control data for clients on Cloudflare, AWStats for cPanel properties, raw server logs for cloud or VPS. This tells me which AI crawlers are hitting the site, at what frequency, and whether requests are being served successfully. A client with zero crawler activity has a fundamentally different problem than one with high crawler activity but low citation rates. The data determines the diagnosis, not the other way around.
Query data analysis. I run Google Search Console and Bing Webmaster Tools AI search report side by side for every client. The Bing WMT AI search report is a separate retrieval surface from GSC and consistently surfaces different query angles, different phrasing, different intent signals. Most clients have never opened Bing WMT. The delta between what GSC shows and what Bing WMT shows is usually where the most useful insights are. That report also covers Copilot, Yahoo, and DuckDuckGo from a single view.
What I Deliver
The starter engagement is a written audit report covering all four areas with a prioritized fix list and implementation notes for each item. I include a baseline Cloudflare AI Crawl Control snapshot so there is a before state to measure against. The report is structured for an independent operator or a small team to execute without hand-holding.
The ongoing retainer is monthly monitoring, schema updates on new content, and quarterly query data analysis across GSC and Bing WMT. One to two hours per site per month once the baseline audit is done and the critical fixes are implemented.
What I Charge
Starter audit for a single site: $300 to $500 depending on content volume and CMS complexity. That is a two to three hour engagement at current pace. Monthly retainer per site: $150 to $300. Three retainer clients is meaningful side income. Five to six is a full remote income stream.
I do not underprice the audit to win the engagement. The audit is the proof of work and it is priced accordingly. Clients who push back on the audit price are usually not the clients who follow through on implementation anyway.
Where the First Clients Come From
Content creators and bloggers already invested in SEO are the fastest to convert. They understand search visibility, they have sites with real content to audit, and they are already paying for tools. The pitch is a direct extension of work they are already thinking about.
Local service businesses that have recently published content are the second target. They have no idea what AI crawlers see when they visit and they are increasingly hearing about AI search from their own customers. That curiosity is the opening.
Small e-commerce operations with a content strategy are the third. Product discovery through AI assistants is a real channel for this audience and the schema requirements are specific enough that an audit delivers immediate, concrete output.
The Technical Foundation
Everything described in this post is built on top of the LLM optimization infrastructure I have been running across six sites for the better part of a year. The full implementation depth, including how Cloudflare AI Crawl Control data reads against GSC and Bing WMT, what the geographic targeting layer looks like, and why schema gets fixed before content structure in the audit sequence, is documented on EngineeredAI. The LLM optimization infrastructure post is the technical reference. This post is the service layer built on top of it.
