A practical guide to how restaurant menus become structured data, what MenuClaw provides, and how to evaluate a data layer before building on it.
MenuClaw is restaurant data infrastructure that turns menus from PDFs, images and websites into normalized, connected data about restaurants, dishes, ingredients, nutrition and prices.
Developers access that data through the Restaurant Intelligence API, while AI teams can work with prepared food datasets. The goal is to provide one reusable data layer for products that need to understand what restaurants serve, where, at what price and with which food attributes.
A restaurant data API is a software interface for querying structured information about restaurants, menus, dishes, ingredients, nutrition and prices. Instead of collecting and cleaning menus itself, an application requests normalized data from a common source.
Every product builds its own crawlers, OCR, entity matching and change detection. The same restaurant may exist under several names, menus go stale, and food attributes remain trapped in unstructured text.
Teams query consistent entities and relationships through an API, then spend their engineering effort on product behavior, ranking, personalization and user experience.
Menu intelligence combines extraction, normalization, entity resolution and delivery. OCR is only one step.
Sources include menu PDFs, images, websites, restaurant pages and partner systems in multiple languages and formats.
Vision and language pipelines identify sections, dish names, descriptions, modifiers and prices from layouts designed for people.
Restaurants, dishes and ingredients are normalized and linked so the same real-world concepts can be queried consistently.
Developers retrieve menus, search dishes, inspect nutrition and traverse graph relationships through versioned endpoints.
The value is not only a list of records. It is the relationships that let software answer multi-step questions about food.
| Entity | Examples of attributes | Useful questions |
|---|---|---|
| Restaurant | Identity, cuisine, location, hours, price band | What does this restaurant serve, and when is it open? |
| Menu | Sections, availability, location, language, change history | Which current menu applies to this venue? |
| Dish | Canonical identity, description, category, modifiers | Where can this dish be found nearby? |
| Ingredient | Ingredient relationships and dish composition | Which dishes contain a specific ingredient? |
| Nutrition | Calories, macros, dietary attributes, confidence | Which dishes match a dietary constraint? |
| Price | Menu price, currency, location and update signals | How does a dish price compare across restaurants? |
Source: MenuClaw product definitions for the Restaurant Knowledge Graph and Restaurant Intelligence API.
Ground food recommendations, dish comparisons and ingredient questions in structured restaurant data rather than improvised answers.
Search by dish, cuisine, dietary attribute, ingredient or location, then return restaurant-level availability and pricing.
Normalize menu information across venues and markets while the ordering platform handles transactions and fulfillment.
Enrich operational records with canonical dish, ingredient and nutrition data without rebuilding a global menu graph.
Compare assortment, price bands and menu changes across locations, cuisines and time.
Use food intelligence datasets to train, evaluate and fine-tune models that need restaurant and dish understanding.
A large record count alone does not show whether data is usable for a specific AI or software workflow. Test a representative set of real restaurants.
The figures below reflect the product information published on MenuClaw's official website as of the update date.
https://menuclaw.com/answers
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