What problem do in-house teams face with AI search?
Someone senior has asked whether the brand shows up in ChatGPT. It might be the chief executive, the sales director or the board. It is a fair question, and it is hard to answer with the tools most teams have.
Google Analytics shows a trickle of visits from chatgpt.com, but not what was said. Your rank tracker shows Google positions, but not AI answers. Checking by hand gives a different answer each time. So the team is left with a gut feeling and a few screenshots.
There is a second worry too. AI engines sometimes get the facts wrong. An old price, a service you stopped offering or a branch that closed. Buyers read that before they ever reach your site.
What do you get from Axiom GEO?
A clear, repeatable view of how AI engines see your brand, and a way to act on it.
- Visibility. AI visibility tracking runs your buyers' questions across ChatGPT, Perplexity, Gemini and Claude on a schedule.
- Competitors. Competitor share of voice shows which brands get named and recommended instead of you, and why.
- Accuracy. Answer accuracy checks each answer against an approved fact sheet and shows where wrong details come from.
- Content. Content opportunities shows questions where AI engines cite others and not you, with an outline for the fix.
- Traffic. AI traffic analytics pulls AI referral visits out of GA4, page by page.
- Reporting. Client reports produce a monthly PDF you can take straight to leadership.
Rankings, reviews, local citations and Search Console data sit in the same login. That means one place to look, rather than five.
Why not just check ChatGPT by hand?
You can, and it is a good way to get a feel for the problem. The trouble is that one answer on one day tells you very little. The same question can give a different answer an hour later. Running a fixed set of prompts on a schedule, across several engines, turns those one-off answers into a trend you can report and act on.
What does a typical month look like?
Most in-house teams work to a simple monthly cycle.
- Check the headline numbers. Look at visibility and share of voice against your main competitors.
- Review accuracy issues. See what the engines got wrong and trace each wrong detail to its source.
- Look at content gaps. Pick the questions where AI cites others and you have something useful to say.
- Plan the work. Send the chosen items to Work as tasks, with the evidence attached.
- Do the fixes. Update pages, profiles and listings, and publish new content.
- Report upward. Send the monthly report, with your own recommendations for the next month.
Tasks linked to a prompt, page or accuracy issue get a baseline. The platform later measures whether the issue cleared, visibility rose, the page got cited or clicks rose. That is the part leadership usually cares about most.
How do you explain AI visibility to leadership?
Keep it simple and honest. AI answers vary from run to run, and tracking is a sample of the questions buyers ask. No one can guarantee a mention. What you can show is the trend, how you compare with named competitors and whether the engines describe you correctly.
Our guide on how to measure AI visibility covers which numbers are worth reporting and how to explain them.
Which plan fits an in-house team?
Most in-house teams start with one brand. The choice comes down to which engines you need and how much content work you plan.
| Plan | Price per month | What it suits |
|---|---|---|
| Starter | £500 | A first look at one brand, tracked on Gemini |
| Professional | £799 | Four engines and answer accuracy, for up to 3 brands |
| Premium | £1,499 | Adds QA Finder and content rewrites for content-heavy teams |
For most teams, Professional is the sensible starting point, since it adds ChatGPT and Perplexity, which both cite web pages in their answers. The pricing page has the full detail, including credit prices for each action.