I have Claude or Codex open for much of the day now. They have made building websites, exploring an idea, and working through implementation much faster. Then I hit a familiar problem: I wanted to measure how different AI models described a brand across the prompts buyers might use. I wanted to see whether the brand appeared, which competitors were named, and which sources were cited. The answer was another subscription.
I have spent my career as a web engineer in large technology companies. A lot of that work has sat where SEO, user experience, Core Web Vitals, and experimentation meet. I have watched the questions change from how a page ranks in a search engine to what an AI answer says about a product, which competitors it names, and whether it cites the right source.
That is why I built Cite42. I wanted the data I needed inside the tools where I was already thinking, without turning brand measurement into another $100-per-month dashboard.
Brand monitoring subscriptions kept stacking up
A dedicated AEO or GEO platform can make sense for an agency or a larger team that tracks many brands and prompts every day. For me, plans that started around $100 a month were hard to justify before adding a keyword tool, the Claude or Codex subscriptions I already rely on, and everything else I use to do good work on the web.
My needs change from week to week. I might run the same buyer prompts across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews, then compare whether they mention the brand, where it ranks, which competitors appear, and what sources they cite. The next week, I might only need a keyword volume check before starting a page.
Paying a few hundred dollars each month for several tools, just to keep the option open, felt backwards. I wanted to pay when I had prompts to measure or a question to answer, then leave the tools alone until I needed them again.
AI search made the old workflow feel incomplete
The work I have done around SEO has always been partly about measurement. You make a change, learn what happened, and use that to make the next decision. AI answers make that more complicated. A result can mention your brand, skip it for a competitor, cite a source you did not expect, or describe the same company differently depending on the prompt.
A single snapshot is useful, but it is not enough. I wanted to save the prompts that matter and see their rankings, citations, competitor mentions, and sentiment over time. That makes a prompt something I can monitor weekly or monthly, instead of a result that disappears as soon as I close a tab.
This is the part of AEO and GEO that I find most practical. It is not a new label for old SEO work. It is a way to understand what people see when they ask AI assistants the questions that sit near a buying decision, then improve the pages, sources, and information behind those answers.
Claude or Codex should be able to get the data itself
MCP changed how I thought about the problem. Model Context Protocol lets Claude, Codex, ChatGPT, or another connected AI assistant call a tool while I am already working with it. Instead of opening a dashboard, exporting a report, and pasting it into a conversation, I can ask for the data where the question came up.
If I am planning a page, I want the assistant to check keyword demand and use it as part of the brief. If I am reviewing AI visibility, I want it to check rankings or citations and help me read the result. The assistant does not make the decision for me. It saves me from moving data between tools before I can start making one.
That is a more useful role for an AI assistant than giving it a vague instruction to research. It has real inputs to work with, and I still have the context to judge whether the answer is worth acting on.
What Cite42 gives an AI assistant
Cite42 brings together AI search rankings, citations, competitor visibility, SEO keyword data, and trend data through MCP and REST. These are different tools, but they often belong in the same piece of work. You might check which brands an AI model names, inspect the cited sources, compare the competitors that appear alongside them, and then research the terms that belong on a page.
The goal is a bundle of small tools, workflows, and trackers that make the AI you already pay for more useful. You can make one call through the REST API, connect the MCP server to an AI assistant, or set up a tracker for the checks you want to repeat. The dashboard is there to manage keys, usage, and tracker reports. It is not the place you have to start.
Today, trackers can save AI visibility, competitor, citation, and sentiment checks, run them weekly or monthly, and keep the completed results in history. That is important to me, because it turns an occasional audit into something I can compare as the prompts, content, and models change.
I wanted the price to follow the work
Pay-as-you-go is the model I wanted for myself. AI checks such as rankings and citations have a cost because the selected models do work. Data tools such as keywords and trends have a flat per-call price. You choose the calls or trackers you need and see the cost before you run them.
I think more software will work this way. When an API or MCP tool is useful inside the workflow you already have, paying for what you use is often more honest than a large plan built around fear of missing out. It lets a creator, maker, or agency try a real workflow, keep it if it helps, and stop paying when it does not.
Who I am building it for
I am building Cite42 for people who work in that same way. Creators who want to know what their topic needs before they write. Makers who want to put real search data into an AI workflow. Agencies that need to check a set of client prompts and keep a record of what changes. Founders who want to see how their company appears in AI answers without adding a stack of fixed subscriptions.
I will keep building it from the perspective of someone who uses these tools too. I want Cite42 to make the research and tracking more practical, give your AI assistant better data, and stay out of the way until there is a question worth asking.
If that sounds useful, you can create an API key and try the calls you need. You can use REST directly, connect the MCP server to the AI assistant you already use, or create a tracker for the prompts you want to follow.