AI chat that guests trust
Allergen and dietary answers come from verified data, never guesses.
Guests scan a QR, ask the AI about allergens, spice, or what's actually vegetarian, and order from the table. Every answer comes from verified menu data.
Verified menu data powers the chat, the ordering, the analytics, and the agents.
Allergen and dietary answers come from verified data, never guesses.
Guests order from the table; tickets land in the kitchen.
What guests view, ask about, and can't find.
An MCP server lets AI assistants browse menus and place orders.
Price accuracy and dietary-marker fidelity are verified per item, with confidence scoring and human review where the extractor is unsure.
From a photo of the menu to guests ordering at the table, in three steps.
{
"name": "Neer Dosa",
"price": { "plain": 120, "with_curry": 240 },
"diet": ["vegan", "gluten_free"],
"allergens": [],
"confidence": 0.97,
"review": "verified"
}The extraction engine, schema, and serving stack are AGPL-3.0.
Every layer is in the open: how menus are extracted, how items are verified, and how they are served to guests and agents.
Talk to us about embedding MenuHero: one verified schema behind your menus, ordering, and agents.
Answers to common questions about MenuHero. Anything else, write to hello@menuhero.ai.
From your verified menu data, never guesses. Allergen and dietary answers only say what the data supports.
Send a photo of the laminated card, a PDF, or a POS export. We structure every item, price variant, and dietary marker.
Price accuracy and dietary-marker fidelity are verified per item, with confidence scoring and human review where the extractor is unsure.
Yes. The extraction engine, schema, and serving stack are AGPL-3.0. The code is on GitHub.
Yes. An MCP server lets AI assistants browse menus and place orders.
Talk to us. Book a demo and we will walk through what embedding looks like for your stack.
Not ready for a call? Leave your email for the launch post.