Fooodo Insights

Fooodo Insights is a separate product track from the Fooodo ordering platform. The website surfaces it because it is the strategic direction for the company; the working product is in active development and not yet shipped. This page covers what Insights is, what it explicitly isn't, and the architecture that sits behind both.

The one-line definition

Fooodo Insights is an AI-native decision intelligence layer for multi-location restaurant chains, designed to connect operational actions to EBIT impact and to keep humans firmly in the loop on every decision that affects employees.

It is not "another dashboard." A dashboard tells you what happened. Insights is a system of decision that:

1. Collects fragmented restaurant data.
2. Normalises it into a canonical restaurant operating model.
3. Explains what happened.
4. Estimates why it happened.
5. Predicts what will happen next.
6. Recommends actions.
7. Requires human approval before high-impact actions are executed.
8. Measures whether approved actions actually improved EBIT.

Who it's for

Multi-location chains in the 5–200 location range. The early reference deployment is a Čili Pizza-shaped chain, but the architecture is POS-agnostic, geography-flexible, and adaptable to different formats (QSR, casual dining, ghost kitchens, hybrids).

The personas Insights is built for are executive, not operator:

The Restaurant Manager and CHRO personas are why Article 22 GDPR matters, structurally.

The principle that makes this different

Every insight, recommendation, forecast, and scenario in Fooodo Insights connects to a measurable EBIT driver:

• Revenue
• Gross margin
• Food cost
• Labor cost
• Marketing ROI
• Table turnover
• Average check
• Order volume
• Waste
• Discounts
• Delivery platform economics

If a recommendation cannot be tied to one of these drivers, it does not enter the action queue. This is the rule that separates Insights from generic "AI optimises everything" copy.

Architecture (six layers)

The product is structured as six layers, each with a defined contract:

Layer 6 — Application
   Executive cockpit · location dashboards · campaign ROI · reverse P&L
   scenario simulator · approval queue · alerts · reports · admin · API
   ────────────────────────────────────────────────────────────────────
Layer 5 — AI and agents
   Virtual CFO · CMO · COO · CPO · CHRO · conversational AI ·
   insight generation · recommendation engine · explanation engine ·
   human-in-the-loop approval queue
   ────────────────────────────────────────────────────────────────────
Layer 4 — Metrics and modeling
   EBIT model · revenue · COGS · labor · marketing ROI · price elasticity ·
   forecasting · anomaly detection · attribution · scenario engine
   ────────────────────────────────────────────────────────────────────
Layer 3 — Canonical restaurant data model
   Tenant · brand · region · location · revenue centre · menu category ·
   menu item · order · order item · payment · discount · tax · campaign ·
   labor shift · employee role · supplier cost · recipe · forecast ·
   recommendation · approval · action · measured outcome
   ────────────────────────────────────────────────────────────────────
Layer 2 — Ingestion and importers
   Direct API connectors · CSV/Excel import · scheduled imports ·
   validation · mapping UI · data quality checks
   ────────────────────────────────────────────────────────────────────
Layer 1 — Data sources
   POS · labor scheduling · time tracking · accounting/P&L · delivery
   platforms · marketing campaigns · loyalty/CRM · weather · holidays ·
   manual CSV · food cost / recipe data · menu data

Two architectural rules matter most:

• You don't need a data warehouse. CSV/Excel upload is available from day one. Mid-market chains rarely have a working warehouse, and waiting on one was the failure mode of the previous generation of restaurant BI.
• POS connectors are pluggable. Each implements the same interface (fetchLocations, fetchMenus, fetchOrders, fetchPayments, etc.). Adding a connector is a Laravel package; it does not touch the metric or AI layers.

The five virtual agents

Insights ships with five specialised AI agents. Each monitors a specific domain and generates EBIT-tied recommendations. None of them executes — every recommendation enters the human approval queue.

The EBIT impact engine

The core analytical capability. It connects operational events to EBIT movement and explains the connection in plain language.

What it does:

• Breaks down EBIT change by location, time period, menu category, campaign, labor, food cost, delivery channel, and discount.
• Provides variance analysis: actual vs previous period, vs budget, vs forecast, vs reverse P&L target.
• Separates correlation from estimated causation from confirmed measured impact.
• Assigns a confidence level to every explanation.

Example outputs:

"Vilnius Akropolis EBIT fell by €1,240 yesterday. 62% of the decline is explained by labor hours exceeding target by 18%, combined with a 6% drop in average check."

"The lunch combo campaign increased revenue by €2,800, but after discounts and extra labor, EBIT impact was only +€340."

"The 5% price increase on pizza category likely reduced order volume by 2.1%, but increased contribution margin by €1,120."

These are not chatbot guesses — they are computed against the canonical data model with explicit confidence intervals.

Human-in-the-loop approval queue (GDPR Article 22)

This is the load-bearing constraint, not a feature.

GDPR Article 22 restricts solely-automated decisions with legal or similarly significant effects on individuals. In a restaurant context that means anything that affects employees — schedule changes, hour reductions, performance scoring, automated penalties — must be reviewed and approved by a human before execution.

Every recommendation that the agents produce enters an approval queue showing:

• The recommendation itself
• The reason
• Expected EBIT impact
• Confidence
• Risk
• Required approver role
• Data sources used
• Suggested implementation steps
• Expiry / urgency
• Approve / reject / modify / defer options

Approved actions become tracked experiments. Insights later measures whether the action achieved the expected result and feeds that signal back into recommendation ranking.

The CHRO agent in particular never autonomously reduces hours, fires staff, penalises employees, or changes schedules in ways that significantly affect employees. The PRD requires audit logs and explainability for every employee-related recommendation. This is a legal requirement, not a product preference.

Marketing ROI engine (statistical, not before/after)

Campaign measurement is one of the places restaurant BI tools fail most visibly. "Revenue went up during the campaign" is not causation.

Insights measures campaigns with:

• Welch's t-test for unequal-variance group comparison
• Cohen's d effect size for practical impact
• Financial materiality breakdown: revenue uplift, gross margin uplift, discount cost, extra labor cost, food cost impact, net EBIT impact
• Explicit labelling of "statistically significant but financially irrelevant" vs "financially material but statistically uncertain"

The recommendation can be: scale the campaign, repeat only in specific locations, stop, adjust discount, change timing, change product mix — with the EBIT-tied reasoning attached.

Reverse P&L planning

CEO/CFO inputs a target annual EBIT. The system works backward to generate:

• Monthly and daily revenue targets per location
• Allowable labor hours and labor cost %
• Theoretical food cost %
• Required average check, order volume, campaign uplift
• Required price/mix changes

Then it compares actual performance against the path and alerts when a location is drifting. Example output:

"Target EBIT €1.2M/year. Location A must average €4,850/day revenue, labor below 29%, food cost below 31.5%. Current forecast misses target by €210k unless average check increases 4.2% or labor reduces 6.8%."

What's in the MVP, what's later

MVP:

• CSV/Excel import
• Canonical data model
• Location dashboard + executive cockpit
• EBIT calculation and variance analysis
• Pre-generated AI insights at import time
• Manual campaign measurement with statistical testing
• Reverse P&L (basic)
• Human approval queue
• Virtual CFO + Virtual CMO agents
• Data quality checks
• Role-based access + audit logs

Deferred to later phases:

• Full POS API integrations beyond the first
• ChefBot Vision QA (camera-based dish quality)
• Advanced price elasticity
• Full CHRO automation
• Robotics
• Complex causal inference
• Fully automated execution (deliberately not on the roadmap; see Article 22)

Mandatory initial integration targets

Insights is designed to plug into the POS systems mid-market chains actually run:

• Western markets: Toast, Square, Lightspeed
• Eastern European markets: iiko, R-Keeper

Plus delivery platforms (Wolt, Bolt), accounting systems, labor scheduling tools, marketing platforms, weather/holiday data, payment providers, and Fooodo's own ordering and payment system.

Status

The Fooodo Insights working product is not yet shipped — the marketing surface at /insights describes the vision. Build is in progress against the PRD; phase 0 (internal Čili-style data validation) is the current focus. Expect substantive product news through 2026, not before.

If you are a multi-location chain interested in being a design partner, write to hello@fooodo.com. The fastest way for the team to prioritise a capability is to have a customer with the data and the appetite to validate it.