AI Data Cleanup for Restaurants: From Messy Inputs to Reviewable Drafts
How AI can organize recipes, invoices, menus, photos, and text without removing human approval.
Most food businesses do not fail to track costs because owners hate numbers. They fail because the data lives everywhere: invoices, WhatsApp photos, supplier portals, old spreadsheets, printed recipes, and memory.
The product should absorb the mess
A modern costing system should not demand perfect input before it gives value. Users should be able to upload what they already have and receive structured drafts.
That is the core Karu idea: AI does the first pass, the operator approves the truth.
Drafts beat silent automation
For business-critical data, full automation is less useful than reviewable automation. The user needs to see what was extracted, where it came from, and how confident the system is.
This makes the workflow safer and more sellable: less effort, but still trustworthy.
Schemas create discipline
LLM output should be parsed through strict schemas. If the data does not match the schema, the job should fail visibly or ask for review.
Karu stores prompt and schema versions so improvements can be tracked over time.
Operator checklist
Accept PDFs, photos, CSVs, text, and manual entry.
Create drafts instead of direct writes.
Validate AI output with schemas.
Store source and confidence for every AI draft.