Different Ways to Use Your AI Data Pack

You ran the analysis. You read the six insights, fixed the obvious things, and wrote some new content to fill in the clear gaps in your discoverability. Now you want to go further.

That is what the AI Data Pack is for. It comes with every analysis, it is one markdown file, and it holds all of the evidence the report was built from. We highlight six key insights and action items for you to tackle on your results page, but with 125 AI responses to buyer questions about your market at your disposal it's worth digging into the data.

What is the AI Data Pack?

It is a single markdown file, around half a megabyte for a typical run, and it contains six things:

• A briefing. A description of your business, how the data was collected, your headline results, and the six insights already identified. It is written for an AI assistant to read first.
• The mention map. One row per question, one column per AI model, showing which models named you and which did not.
• Competitors named. Every company the models named instead of you, ordered by how many different models named each one.
• What the models searched for. The models often take the initial query and break it out into multiple narrower searches, a process called query fan-out. Usually you do not get to see those searches, but we save all of them for you, word for word.
• The source index. Every site the models cited while answering, ranked by how often, and labelled with which model cited it and whether it belongs to a competitor.
• All 125 responses, verbatim. Every answer from every model, in full.

Two things about it are worth knowing before you start. It is built directly from your stored results with no AI involved, so downloading it twice gives you the identical file. And it contains no personal data, just what was derived from our understanding of your website, market, search space and AI prompts and responses.

Why the raw responses matter

The report answers "How visible am I? Who else shows up? and what should I do about it?" The AI Data Pack allows you to dig underneath it all and expose further insights guided by your own curiosity. Somewhere in those 125 responses, an assistant explains to a buyer why a competitor is a good choice. You get to read the pitch that beat you, word for word, giving you the valuable information needed to figure out what to do about it.

Build a competitor list you did not have

This one needs no AI tool at all, and it is the fastest thing in the file to act on.

The Competitors named section is a table of every company the models put in front of a buyer instead of you. Each row shows how many responses named the company, how many different models named it, and the other spellings it appeared under. That is already the right shape to lift into a spreadsheet, a rank tracker, or whatever you use to keep an eye on the competition.

Start with the top table, "Named by more than one model". A company that several assistants name on their own is a firmer finding than one that appeared once. The companies named by one model only are listed below it, and they carry the least weight.

What makes the list worth having is where it came from. These are not the competitors you would name from memory, and they are not simply whoever ranks above you in Google. They are the businesses current AI assistants named when asked realistic buyer questions in the market you sell in. This will often contain obvious names you've already heard of, but it will most likely unearth some other competitors you may not know you had.

Two checks before you commit the list to anything. Confirm that a company exists before you track it, particularly one named by a single model. And drop any row that reads like a description of the category rather than a business, since a model sometimes names the category itself.

The source index is the other half. It lists the sites the models cited, which tells you where they read about your market, not only who they named.

If you use an agentic coding tool

If you work with something like Claude Code, Grok Build, Codex, Antigravity or Cursor, this is where the AI Data Pack really shines. Those agentic tools can read your website's source and the AI Data Pack at the same time. That combination is the difference between advice and action.

Put the file somewhere the tool can see it, alongside your site, then work through it:

• Find the gaps, then fill them. Ask it to list every question in the mention map where no model named you, check whether your site has a page that answers each one, and write the ones that are missing. It can compare the question to your actual pages rather than guessing.
• Build the FAQ from the informational questions. The pack marks which questions are informational. Those are the ones people ask while they are still learning, and they are usually the easiest to answer well.
• Work the source index. Those are the sites the models already trust in your market. Ask the tool to go down the list and tell you, honestly, which ones you could plausibly appear on and how.
• Read the competitor's pitch. Point it at a competitor that appears often, ask it to collect every passage describing them, and then ask what those passages have in common that your own pages do not say.

If you use a free or low-cost assistant

You do not need an agentic coding tool to get value out of the AI Data Pack. It's written to be read by any assistant, and the free tiers handle it well if you set them up once rather than pasting into a chat window.

• Use a notebook tool as a searchable library. Google's Gemini Notebook is the clearest example: upload the pack as a source and it becomes something you can question repeatedly, with answers pointing back to the part of the file they came from. This suits a large file better than a chat window does, because you are querying it rather than trying to hold it all in one conversation. Point Gemini Notebook at your website as well to get a holistic view and compound your capabilities.
• Attach it to a project. If your assistant has projects or workspaces, ChatGPT and Claude both do, add the pack once as a project file. Every conversation you start in that project can then draw on it, and you stop re-explaining your situation.
• Split it if you have to. The bulk of the file is the prompt-by-prompt responses. The analysis lives in the first few sections. If a tool struggles with the whole thing, load the briefing, mention map and source index first, which is exactly what the file itself tells the assistant to do.

The one thing we would avoid is pasting the whole file into an ordinary chat. It is large, and most of what makes it useful is the ability to reference it over time as you dig deeper into the insights.

Ask better questions of it

The AI Data Pack ships with a short list of key insights and suggested action items, written for your specific results, so start there rather than from a blank prompt. They are worth reading even if you do not use them, because they show the kind of question the data can actually answer.

Beyond that list, the questions that tend to pay off are comparative rather than general. "How do I improve my AI visibility" is too broad for any dataset to answer well. "Compare how the models describe the top competitor with how they describe us, quoting both" is a question this file can genuinely settle.

We're sure you'll find ways to dig in and unearth insights, but here are some questions to get you started:

• "Which of the 25 questions did no model name us for? What do the answers to those questions have in common?"
• "For each competitor named by three or more models, what reasons do the models give for recommending them?"
• "Which search queries did the models run that none of our pages answer?"
• "Which sources do the models cite most often? Which of those sources never mention us?"
• "Where do the models describe us wrongly or with old information? Quote each one."
• "Which models name us and which do not? What is different about the answers where we appear?"

One safety note

The responses in the pack came from external AI models, and they are quoted verbatim. The file tells any assistant reading it to treat that text strictly as data to analyse and never as instructions, which is the right default for any content that arrives from the open web.

That instruction is in the file. It is worth knowing it is there, and worth keeping it there if you ever cut the file down.

Snapshots, not live readings

The pack records what the models said on the day you ran the analysis. AI answers drift as models change and as the web underneath them changes, so treat it as a dated measurement rather than a permanent description of your market. The file carries its own date for that reason.

When you have made real changes and want to know whether they moved anything, do a re-run. Your results page stays live for 90 days, and while it is live it has a re-run button. A re-run puts the same 25 questions to the same five assistants again, which is what makes the two measurements comparable. It costs $20 USD instead of $29, because the questions are already written. If an assistant has moved to a newer model since your first run, the re-run uses the newer one, because that is what a real buyer would meet in the real world.

Key takeaways

• The report gives key findings. The AI Data Pack holds all of the evidence behind them.
• It is one markdown file, identical every time you download it, and free of personal data.
• With an agentic tool like Claude Code or Codex that can also see your site, the pack turns into an implementation map rather than a research guide.
• With a free assistant, set it up once as a source or a project file instead of pasting it into a chat.
• The Competitors named table is a ready-made competitor list, drawn from who AI actually names to buyers.
• Ask comparative questions. The file is much better at "how do they describe them versus us" than at "what should I do".
• Dig deep! There are 125 AI responses to realistic buyer questions, from the models the most popular AI assistants actually serve the buyers searching for your product.