A weekly profitability analyst, built from the data you already have.

RestaurantIQ ingests what a restaurant already produces — point-of-sale exports, vendor invoices, payroll, recipes — and hands the owner a ranked, dollar-quantified list of profit leaks every week. The AI reads the messy inputs; deterministic code does every calculation; the owner stays the decision-maker.

43,501

Real orders from the pilot’s own point-of-sale,
analyzed in production

101,085

Line items normalized into one
canonical data model

~$1,900/mo

In pricing opportunities identified on the pilot’s real menu —
flagged and evidence-backed, with the system labeling its own confidence

1 phone

The whole product runs on the owner’s phone —
because that’s where a bakery owner actually works

The leaks are invisible — the data isn’t.

Restaurants run on thin margins, and the profit leaks hide in plain sight: vendor price creep buried in invoices, popular items priced below average margin, purchasing that quietly outruns what the recipes consume, labor scheduled against the wrong demand curve. The data to see all of it already exists — but it sits in silos no owner has time to cross-analyze.

Dashboards die of neglect. What an owner needs is an analyst: something that reads everything, ranks what matters, shows its math, and fits in a pocket.

/ THE APPROACH

Deterministic math. AI only where paper meets machine.

01

Everything into one model.

Point-of-sale exports, invoices (CSV or a photo), payroll punches, and recipe cards are normalized into a single canonical data model.

02

The owner confirms every extracted line.

When AI reads an invoice photo or a handwritten recipe card, nothing enters the books until the owner confirms it line by line.

03

Code does the math.

Vendor price drift, menu-item margins, usage variance, staffing vs. demand — all computed by deterministic code. The AI writes the plain-English weekly narrative; it never invents a number.

04

Every answer shows its work.

Ask anything, and the answer arrives with the exact query and rows behind it. Estimated figures carry visible “est.” labels, and low-confidence findings explicitly invite skepticism.

Findings in dollars, on real data — labeled honestly.

Running on the pilot’s own data — 43,501 orders, 101,085 line items — the system surfaced five high-volume menu items priced below average margin, roughly $1,900 a month in pricing opportunity. It labeled the finding medium-confidence on its own, because food costs were still owner-estimates at the time. That honesty is the product: an analyst you can trust is one that tells you what it doesn’t know.

No staff displaced. The owner stays the decision-maker on every price, every menu change, every schedule — the system just makes the case, with evidence.

Part of hospitality intelligence

RestaurantIQ is one of the two working products behind Blackfrog’s hospitality intelligence — built for restaurants, and for resorts running many outlets at once. See hospitality intelligence →

Your numbers are already talking. Start listening.

Bring what you already have — a point-of-sale export, a stack of invoices, your recipe cards. We’ll point the platform at your real data and show you what it finds, in dollars, with the evidence attached.