Global food and beverage manufacturers use catalog.xmap.ai to find every supermarket, restaurant, cafe, bakery, and hotel worth distributing to, and to measure demand by territory before committing sales and logistics resources.
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 single product line can move through supermarkets, restaurants, and hotels, each needing its own outlet list.
Every new territory or region needs a fresh, filtered list of outlets worth visiting.
A distribution list from last year misses new restaurants, cafes, and stores already open today.
Export every supermarket, restaurant, cafe, bakery, and hotel in a target territory in one filter.
Use outlet density and category mix to estimate relative demand across regions.
Hand field reps a ranked, filtered outlet list with contact fields instead of a cold map.
Catch newly opened restaurants and stores worth an introductory sales call.
Find regions with strong outlet density but weak current distribution coverage.
Compare retail, food-service, and hospitality channel weight across a market.
Reachable outlet count by channel in a target metro.
Every export carries 36 columns; these are the ones distribution teams use most.
| Field | Type | Why it matters here |
|---|---|---|
| category_level_1–3 | string | Separates supermarkets, restaurants, cafes, bakeries, and hotels into channel groups |
| brand_name / chain_flag | string / boolean | Distinguishes national chains worth a corporate deal from independent outlets |
| phone / website | string | Builds a contactable outreach list for field sales |
| address / city / zip | string | Organizes outlets into sales territories |
| open_status | string | Flags newly opened outlets worth a fresh sales visit |
| traffic_score / dwell_time | numeric | Signals which outlets likely see the highest customer volume |
| rating / review_count | numeric | Signals which outlets are thriving and worth prioritizing |
| poi_id | string (hash) | A stable join key for tracking the same outlet across sales cycles |
A food and beverage manufacturer is entering a new region. The team exports every supermarket, restaurant, cafe, and hotel kitchen in the territory, ranks them by outlet density and traffic score, and hands the regional sales team a prioritized call list on day one instead of a blank map.
We'll pull a live sample for your category and market.