How Cura helps
AI operations assistant for professional kitchens
One place where prep, receiving, shortages, costs and forecasts meet, so the shift starts with a clear read instead of a dozen checks.
The real kitchen problem
Before service, the information a chef needs sits in different places: a count sheet, an invoice, a text from a supplier, a forecast in someone's head. Problems get found when the line is already moving.
What Cura uses and helps you understand
Data Cura uses
- Inventory counts and par levels
- Recipes and prep items linked to menu items
- Expected deliveries and receiving results
- Covers forecast and service periods
- Open service issues and approved substitutions
What you can understand
- What changed since the last shift
- What needs attention before service, and why
- Which records Cura used, and what it could not see
- What the team may want to decide next
A concrete example
Sample: two hours before dinner
- Expected covers are 164, up from the usual midweek count.
- Salmon on hand may not cover that demand for the dishes that use it.
- The tomato delivery is marked late by the supplier.
- Cura lists these for the chef to review, with the records behind each.
Sample data Sample data. Fictional scenario for illustration, not a live or actual customer service.
Limits and human review
- Cura does not replace the chef's judgment or approvals.
- Output depends on data your team keeps current.
- Role-specific briefings and some connections between modules are still being built.