Cleaning Service Locations
12,885 US cleaning service businesses, from national chains to single independent shops, with address, phone, website and map coordinates.
Buy the whole list — 12,885 rows · $66.68
Over 10,000 rows, we build the file after you pay and email you the link, about 2 minutes later.
Or ask for the part you need: an area, a distance, a condition. Counts and previews are free, and each answer shows its exact price.
Sample data
| name | address | city | state | zip | phone | website | overture id | brand | brand id | wikidata | category | category path | basic category | social | operating status | confidence | source dataset | source record id | source ids | source updated | data license | location type | location type confidence | location type basis | latitude | longitude | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Astro Pak Corporation | 5325 US Highway 1 | Mims | FL | 32754 | (321) 269-4573 | http://www.astropak.com/ | 543bfdb0-79fa-4dc9-9c1b-045a6c0bbaef | industrial_cleaning_service | |services_and_business|professional_service|cleaning_service|industrial_cleaning_service| | professional_service | open | 0.92 | Microsoft | 844424931880474 | Microsoft:844424931880474|Overture-signals:c1c9c0e3-4404-4f7c-84e8-5d540331f788 | 2026-08-10 | Overture Maps Foundation, CDLA-Permissive-2.0 | unknown | name: no premises word; website: none in its path; category: industrial_cleaning_service names a business, not premises | 28.748829 | -80.871326 | ||||||
| Molly Maid of Central Portland | 6645 NE 78th Court, C-1 | Portland | OR | 97218 | (503) 755-5499 | https://www.mollymaid.com/locations/oregon/central-portland/?cid=LSTL_MLY000212 | 0b6bc629-f606-4ee2-aed4-54acef5be2f4 | office_cleaning | |services_and_business|professional_service|cleaning_service|office_cleaning| | professional_service | open | 0.99 | Microsoft | 562949964771240 | Microsoft:562949964771240|Overture-signals:d7cc49ca-72d3-4813-b3ed-e90952910f43 | 2026-08-10 | Overture Maps Foundation, CDLA-Permissive-2.0 | unknown | name: no premises word; website: none in its path; category: office_cleaning names a business, not premises | 45.570225 | -122.581863 | ||||||
| H&B Janitorial & Window Cleaning | 5179 Cortland Dr | Grand Blanc | MI | 48507 | (810) 695-0555 | https://hbwindowcleaners.com/ | 1ea1b77b-02e6-4f1d-9a7a-d7b2e63d1b8a | office_cleaning | |services_and_business|professional_service|cleaning_service|office_cleaning| | professional_service | open | 0.92 | Microsoft | 562949962620202 | Microsoft:562949962620202|Overture-signals:c8539953-1d75-41ff-bb07-24158b56d242 | 2026-08-10 | Overture Maps Foundation, CDLA-Permissive-2.0 | unknown | name: no premises word; website: none in its path; category: office_cleaning names a business, not premises | 42.957218 | -83.635277 |
What's in each row
| Field | What it is |
|---|---|
| overture_ | Map data id for the place, stable across monthly releases |
| name | Business name |
| brand | Brand or chain name, when the place belongs to one |
| brand_ | Our id for that brand (its Wikidata id where one exists) |
| wikidata | Wikidata id of the brand |
| category | Kind of place, from the map data |
| category_ | The category with every broader category above it |
| basic_ | A broader, simpler category |
| address | Street address |
| city | City |
| state | State (two-letter code) |
| zip | ZIP code |
| phone | Phone number |
| website | Website |
| Email address | |
| social | Social media page |
| latitude | Latitude |
| longitude | Longitude |
| operating_ | Whether the place is open, where the source says; closed places are left out of the list |
| confidence | How sure the map data is that the place exists (0 to 1) |
| source_ | Which source contributed the record |
| source_ | The record's id in that source |
| source_ | Every source record merged into this row |
| source_ | When the source last updated the record |
| data_ | The license the row is published under |
| location_ | Kind of premises: store, office, branch, warehouse and so on, read from the name, website and category |
| location_ | How sure the location type is (0 to 1) |
| location_ | What the location type was read from |
Source & license
Open map data from the Overture Maps Foundation, release of August 2026, refreshed monthly. Closed and low-confidence places are removed and duplicates within 50 meters merged. Contains data from Overture Maps Foundation (https://overturemaps.org), Places theme, licensed CDLA-Permissive-2.0 (https://cdla.dev/permissive-2-0/); some sources are Apache-2.0. See https://docs.overturemaps.org/attribution/.
Using ChatGPT, Claude or an API?
Ask this list a question with a link
Using ChatGPT or Claude? Tell it to answer from locationlists.com/find — no account, no connector.
How assistants ask
No connector needed. Any AI assistant can answer from this site with a web link, no connector, no account: open https://locationlists.com/find with the question as query parameters.
If a question needs filtering, distance, drive time, combining lists or a price, open a /find link: either put the question itself in q=, or build the structured link. The answer does not need an API, a connector or an account.
Simplest, the question itself: https://locationlists.com/find?q=<the question in plain words>
Or set the parts yourself: https://locationlists.com/find?dataset=<slug>&state=<ST>&city=<city>&county=<county>&zip=<zip>&near=<place or zip>&radius=<miles>&drive=<minutes>&where=<column>:<op>:<value>&total=<rows>
Leave out what the question does not need. Use radius for straight-line miles or drive for minutes of driving (5 to 60), not both; near can name several places separated by | to reach from any of them. To ask about several lists at once, use datasets with two or more names, or category with a kind of business. To compare two lists, add relate=nearest and the other list with b. in front of its parameters. To count by area, use areas=county (or zip, state, metro). To keep only places whose county, ZIP code, metro area or state meets a Census figure, add area_where (for example county:population>1000000); with areas, per=100000 gives counts per 100,000 residents.
Open one of these to see how many places match, a few preview rows and the price for just those rows:
- The whole list: add a city, state, ZIP code or distance:
https://locationlists.com/find?dataset=cleaning-service-locations
Ask in ChatGPT or Claude
Copy this and paste it in:
Every answer shows its exact price before you pay, by card, no account; the whole list is $66.68, we email the file when it's built (about 2 minutes). Counts, previews and comparisons with other lists are free.
For developers
Dataset slug cleaning-service-locations. Machine-readable record: dataset.json. Without a connector, every one of these answers comes from a link: https://locationlists.com/find?dataset=cleaning-service-locations. Works in every MCP tool and the x402 query API like any other dataset (count_locations, get_sample, query_locations, relate_locations, count_by_area, cotenancy).