Restaurant B2B research guide
Choose a restaurant data API around the records your workflow needs
Evaluate restaurant data APIs by record type, geography, source dates and access. Test company and location data without assuming menu, transaction or POS coverage.
Who this helps: Revenue operations, data and engineering teams connecting restaurant research to sales workflows.
Choose the record type before choosing an endpoint
Start with the downstream action. Territory discovery needs locations and company relationships. Contact enrichment needs the correct organization and person association. Menu analysis needs item and channel detail. Transaction analysis needs an explicitly authorized operational source. A general restaurant label does not make those records interchangeable.
Write down one row's meaning and its stable identifier. Ask how a location relates to a brand and operating company, and how the provider handles several venues sharing one domain. Validate that the API's entity model matches your CRM or research workflow before moving a large dataset.
Test filter scope and coverage with concrete examples
For a request such as restaurants in Chicago with a given ordering channel, verify that the geography and provider evidence apply to the same venue. A company with one Chicago location and a different listed venue elsewhere may not satisfy the business question.
Define how unknown values, incomplete location coverage and duplicate records appear. Keep a company's full recorded footprint separate from the locations matching your territory. Request representative matches, nonmatches and missing-data cases so the integration can handle each intentionally.
Require interpretable sources and update behavior
Overture's Place schema illustrates useful distinctions: a place has identity and location fields, optional source information, an operating-status field and a confidence value about existence. Its documentation explicitly separates operating status from current opening hours. Read those definitions before mapping a provider's field into a sales workflow.
For your chosen API, establish what each timestamp means and how changes, deletions and merged records are communicated. Confirm pagination and retry behavior, the treatment of missing values, and which evidence remains inspectable after a record changes. A recently retrieved response can still contain older source observations.
Primary sources for this section
Verify access and product scope before integration
Square's Catalog API is an example of access to a seller's catalog with documented permissions. That contract is different from discovering public restaurant companies across a market. Choose the appropriate source for the job, and confirm the permitted use and account access required for each data type.
Use Lumitra's public company records to inspect location, provider and contact evidence and assess whether the research model fits your work. For a production integration, verify the available API documentation, fields and access arrangements directly. This guide does not promise menu, transaction, installed-POS or opening-event endpoints.
Primary sources for this section
Inspect the company query definition
Example reviewed
The company example uses domain_record rows for published profiles. The public schema also describes location, menu_item and offer rows for different research questions. Choose the unit before counting records or comparing providers.
For this Chicago cohort, company membership and the included location preview reuse the same country, foodservice, state and city conditions. An included preview alone does not qualify the company; both parts must keep the location scope aligned.
POST https://api.lumitra.io/v1/public/intelligence/query
{
"grain": "domain_record",
"select": [
"domain",
"name",
"materially_changed_at",
"location_count",
"locations.coverage",
"contacts.summary"
],
"where": {
"and": [
{
"field": "location_count",
"op": "gte",
"value": 1
}
]
},
"having": {
"locations": {
"and": [
{
"field": "address.country",
"op": "in",
"value": [
"us",
"usa",
"u.s.",
"u.s.a.",
"united states",
"united states of america"
]
},
{
"field": "declared_type",
"op": "in",
"value": [
"Restaurant",
"FastFoodRestaurant",
"CafeOrCoffeeShop",
"BarOrPub",
"Bakery",
"IceCreamShop"
]
},
{
"field": "address.region",
"op": "in",
"value": [
"il",
"illinois"
]
},
{
"field": "address.city",
"op": "in",
"value": [
"chicago"
]
}
]
}
},
"include": {
"locations": {
"select": [
"id",
"name",
"address.street",
"address.city",
"address.region",
"address.country",
"cuisines",
"identifier.providers"
],
"where": {
"and": [
{
"field": "address.country",
"op": "in",
"value": [
"us",
"usa",
"u.s.",
"u.s.a.",
"united states",
"united states of america"
]
},
{
"field": "declared_type",
"op": "in",
"value": [
"Restaurant",
"FastFoodRestaurant",
"CafeOrCoffeeShop",
"BarOrPub",
"Bakery",
"IceCreamShop"
]
},
{
"field": "address.region",
"op": "in",
"value": [
"il",
"illinois"
]
},
{
"field": "address.city",
"op": "in",
"value": [
"chicago"
]
}
]
},
"limit": 3
}
},
"order_by": [
{
"field": "domain",
"dir": "asc"
}
],
"limit": 50
}This request definition is generated from the same recipe used by the linked market. It reads the stored public corpus; it does not collect missing records or refresh sources. Availability and performance depend on the deployed query service.
The first response is bounded to 50 company records and up to 3 matching location previews per company. Follow meta.next_cursor with an unchanged query for more company rows, preserving stable identities and source references.
Completing pagination and establishing collection coverage are separate checks. A complete traversal is still a view of the stored corpus. This example includes no live execution or latency result.
Write a five-case API acceptance brief
For a sales-operations integration, define the account-discovery cases you need the provider to demonstrate before implementation.
- Select a single-location company, a multi-location company and a company with provider evidence from the public research links.
- Add a case with missing contact information and a case where the venue is outside your target geography.
- Specify the expected record scope, relationship evidence and treatment of missing values for each case. Do not invent endpoint names or response fields.
- Ask the provider to demonstrate its documented access, pagination, source dates and update behavior against those cases.
The research output
An acceptance brief linking real example accounts to required data behavior, with API access and unsupported fields explicitly unresolved until verified.
Start with inspectable company evidence
Start with recorded US restaurant locations and inspect each company.
Browse companies with multiple recorded locationsInspect the stored footprint; size alone does not establish ownership or franchise status.
Inspect a published company profile: SmoqueReview a real record's fields and sources; it is not presented as a franchise or new opening.
Frequently asked questions
Does a public JSON record mean bulk API access is available?
No. A public representation helps inspect a record. Bulk access, rate limits, authentication, fields and permitted use need a documented provider contract before you build a production integration.
How should an integration handle missing contacts?
Preserve the provider's documented missing-value state and avoid creating a guessed email or role. Keep company, venue and person contact scopes distinct, and review source dates before treating a field as current.
Can one restaurant API provide company, menu and POS data?
Possibly, but each data type needs its own verified coverage and access definition. Do not infer operational access or installed-system evidence from a company's presence in a location or contact dataset.
Sources and methodology
Descriptions of other products come from their published sites. Coverage, accuracy, integration, pricing and customer outcomes must be verified for the buyer's actual territory and use case. Lumitra publishes this guide and has a commercial interest in being evaluated.
Related restaurant-sales research
Put restaurant intelligence to work
Tell Lumitra which restaurant operators fit your business. Research their organizations and buyer paths, qualify opportunities, and let AI agents prepare the next step.