Restaurant and cafe brands use catalog.xmap.ai to see exactly who they're competing with in any trade area, catch a competitor's new location the month it opens, and find the next site before someone else does.
catalog.xmap.ai is a self-serve database of 140M+ real business locations across 40+ countries. Every record is an actual, physically-located business (not an estimate) with up to 36 structured fields: name, full address, latitude/longitude, category (three levels deep), brand and chain affiliation, hours of operation, phone and website, star rating and review count, open/closed status, and more.
You filter the catalog by category, brand, or geography in a live web interface, watch the matching record count update as you narrow the search, and export the results as a clean CSV file, ready to open in a spreadsheet, load into GIS software, or join against your own data.
The dataset refreshes every 30 days, so an export reflects recent openings, closures, and changes rather than a static snapshot from years ago.
A delivery-only competitor can eat into your radius with zero storefront to notice.
Restaurant unit economics leave little room to recover from choosing a saturated or low-traffic corner.
The F&B landscape turns over quickly enough that a stale competitor map is worse than no map.
See every restaurant and cafe within any radius of a location, with ratings and review volume attached.
Re-run a saved filter monthly and catch a competing chain's new location as soon as it appears in the data.
Choose a delivery-only kitchen location based on demand density rather than storefront visibility.
Use venue hierarchy to see which food-court or mall you'd be joining, and who else is already there.
Spot categories (a cuisine or format) that are underrepresented in an otherwise dense trade area.
Check a proposed new unit against your own existing locations before approving it, to avoid cannibalization.
Unit count within a 2-mile trade area, by format.
Every export carries 36 columns; these are the ones F&B teams use most.
| Field | Type | Why it matters here |
|---|---|---|
| category_level_3 | string | Narrows to cuisine or format, cafe, QSR, fine dining, dark kitchen |
| brand_name / chain_flag | string / boolean | Separates national chain competitors from independent restaurants |
| dwell_time | numeric | Distinguishes a quick-service stop from a sit-down destination |
| rating / review_count | numeric | Signals which competitors are actually winning customers |
| hours_of_operation | string | Reveals late-night or breakfast gaps in a trade area |
| open_status | string | Flags recent openings and closures for monthly monitoring |
| venue_hierarchy | string | Ties a unit to its food court, mall, or shopping center |
| latitude / longitude | float | Powers delivery-radius and trade-area distance calculations |
A growing cafe chain is choosing between five submarkets for its next ten units. The team filters the catalog to competing cafes and QSR in each, exports dwell time, ratings, and open/closed status, and ranks the submarkets by whitespace, then sets a monthly alert on the same filter to catch new competitor openings before the lease is signed.
We'll pull a live sample for your category and market.