Restaurant Tech · Document AI · OCR + LLM · Menu Workflow Automation
PDFs, images, and Word docs with multi-column layouts, decorative fonts, and non-standard formatting.
The ordering platform demands IDs, tax flags, and relationship links — none of which appear on a physical menu.
Each restaurant's menu looks different — no template or consistent structure to parse mechanically.
Manual input leads to import failures, missing relationships, and inconsistent tax and modifier data.
OCR + layout understanding parses categories, items, prices, sizes, modifiers, toppings, and hours in parallel AI calls.
11 transformation steps: auto ID generation, size-to-ID mapping, option grouping, topping price fan-out, and normalization.
200+ system fields auto-populated with validated, neutral defaults — output is always valid and immediately importable.
Next.js interface with live progress tracking and structured preview; delivers .menu JSON + .rmf files on completion.
Per menu — parse, discover, extract in parallel AI passes.
Post-processing — ID gen, normalization, merging, fan-out.
Fields auto-filled — safe defaults for all non-extractable fields.
Manual data entry — output imports directly with no intervention.