Solutions · Local & multi-location

Get recommended in every city you serve.

Find out what AI recommends in every city you serve, then fix what engines miss about each store or service area.

Accounts open from the waitlist. The website check is free, with no signup.

A map of US cities: AI names Kestrel in 41% of discovery answers asked from Denver and 38% from Seattle, but only 9% from Minneapolis, a store city

Places

Measure each place you serve on its own

A national score hides the store cities where you lose. Measure each place on its own, with its range and change, against your national share.

Every place measured: 11 of 12 places and 1,564 answers, and Minneapolis, a store city, where AI names Kestrel clearly less than across the US

Cause

Open a place to find what engines are missing

Open one city to find who engines name there, then choose the fix: a store page engines can cite, matching listings or pages for the right market.

Minneapolis opened: Norrland is named 7 times as often as Kestrel, 62% against 9%, with how each engine reached the city

Regions

Repeat what works, region by region

Once a fix moves one city, repeat the work place by place, and send each region its own scheduled report.

Every scheduled send 30 days back and 30 ahead, from weekly performance to the EU regional digest posted to a Notion page every Thursday, with the Q3 leadership review waiting on approval

Find the city missing from local answers.

When buyers in a store city are sent to a competitor, find what engines miss about that location, fix the page and listings, and check again.

The US map: in Minneapolis, a store city, AI names Kestrel in 9% of discovery answers, beside Denver at 41%, Seattle at 38% and Colorado measured as a region at 31%
The UK measured as a country, then products recommended by city: the Summit Shell at 34% in Denver and 6% in Minneapolis, and the Ridge jacket strong only in Seattle and Portland
Four places need a move: a competitor named instead in Minneapolis, filed and in progress; the Boston store’s old address; UK prices quoted to Austin; and Bozeman waiting to be confirmed

1Compare how often AI names you in each city against your national share, and find the store cities that fall behind.

2Check whether the same products win in other cities, which tells you the problem is the location, not the product.

3File the work for each location and keep the next one queued, such as a city where answers still give an old address.

Keep exploring.

Questions, answered.

Something else on your mind? Contact us.

No. Praised asks your questions from each place you set up and reads the answers engines give there; a buyer’s own history and settings are not reproduced. Each place shows how every engine reached the place: sampled there, or told the place.

Yes. Measure a region on its own when single cities have too few answers, and keep store cities separate. Store cities can also be pooled and compared with the other cities you track.

It checks a public page’s readiness to be found, understood and cited. Ongoing tracking and work belong in the platform.

Fix the place where you lose.

Measure each place on its own, find what engines are missing about the store, and repeat the fix city by city. Check a store page free, or explore how places and competitors are tracked.