What problem do multi-location brands face in AI search?
AI answers change by place. Ask for the best firm near one town and you get a different list from the next town along. A brand can do well in one region and barely appear in another, and head office often has no idea which is which.
Details go wrong too. Engines name branches that closed years ago, give old opening hours or mix up which services each site offers. With ten or twenty locations, those small errors add up. Each one can send a buyer to the wrong place or put them off altogether.
Then there is the admin. Reviews spread across many Google profiles, directory listings for every branch and rankings that differ town by town. Most teams only ever look at the biggest two or three locations.
Why do AI answers differ by region?
Engines that search the web before answering pick up local sources. A regional news site, a local directory or a town-focused list article can all shape the answer in one area and not another. Your branch pages, reviews and listings in each area feed into that too. So a brand's standing in AI answers is really a set of local standings, and it helps to track them that way.
What do you get from Axiom GEO?
A view of every location, and the tools to fix what is wrong at each one.
- Regional AI visibility. Local AI visibility runs the same prompts per country or region and splits share of voice by geography.
- Town-level rankings. Rank tracking checks Google positions daily down to UK town level. You can copy a campaign to each location.
- Reviews for every branch. Google Business Profile reviews finds every location, pulls reviews daily and reports by source and location.
- Listings. Local citations tracks directory listings and whether each is live, claimed, pending or not claimed.
- Branch accuracy. Answer accuracy checks AI answers against a locations list and flags branches that do not exist.
- Location markup. The schema generator builds LocalBusiness JSON-LD with the address and rating from each profile.
How does the locations list work?
You keep a list of your locations in answer accuracy, with start and end dates. A setting on the list says whether it is complete. If it is, any branch an engine names that is not on the list gets flagged. If it is not, only details that can be proven wrong are flagged. For most brands with a fixed estate, marking it complete catches closed and made-up branches quickly.
What does a typical month look like?
Most multi-location teams work through a cycle like this.
- Compare regions. Look at share of voice by geography and spot the regions that lag.
- Check branch accuracy. Review flagged locations, hours and services, and trace each wrong detail to its source.
- Review the reviews. Look at new reviews by location, reply where needed and check which sources bring them in.
- Tidy the listings. Claim and correct directory listings for any branch that has changed.
- Plan local content. Send gaps for weak regions to Work as tasks.
- Report by location. Share the monthly report with head office and regional managers.
Which plan fits a multi-location brand?
Plans are counted in clients, and a client is usually one brand with its own workspace. So a single brand with many branches normally sits in one workspace. What grows with each region is credit use, since each prompt runs on each engine for every market you add.
| Plan | Price per month | Credits | What it suits |
|---|---|---|---|
| Professional | £799 | 50,000 | One brand in a few regions, with four engines and answer accuracy |
| Premium | £1,499 | 120,000 | Wider regional tracking, plus QA Finder and content rewrites |
| Enterprise | Custom | Custom | Groups running many brands |
A Perplexity query costs 20 credits and a ChatGPT query 40, so it is easy to work out what a prompt set across your regions will use. Top-up credits can be bought if you need more. Our guide to local SEO and AI search covers the wider picture.