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QSR & Multi-Unit Restaurant Intelligence

The Operating Intelligence Platform Built for QSR and Multi-unit Restaurant Brands.

A single operating narrative across the restaurant business
The Retail Score connects POS, BOH (Back of House), inventory, transactions, marketing, and finance data into one restaurant intelligence model — so operations, finance, field leadership, and brand teams work from the same numbers, definitions, and daily realities.
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See How It Works
HQ Insights
Enterprise view
Labor %
27.4%
Network average
Food Variance
1.8%
Period to date
Discount Use
<5.2%
Promo mix controlled
Overtime %
4.75%
Period to date
Labor spike: 14 locations trending above target by daypart
BOH / Labor
Discount usage rising in 22 stores with margin pressure emerging
Promo
Food cost risk: 9 stores showing unusual variance on core items
COGS
Regional Manager Insights
Area performance
Sales vs Plan
+3.1%
Region this week
Labor %
29.2%
Above target in 6 stores
Drive-Thru Time
248s
2 regions off pace
Throughput
31.6
Orders per labor hour
6 stores need coaching on labor deployment during dinner daypart
BOH / Labor
3 stores showing weak throughput despite higher transaction volume from local promo
Demand
Service time drift is concentrated in two higher-volume trade areas
Speed
Store Insights
Manager focus
Transactions
412
Today so far
Labor Hours
86.5
Shift to date
Avg Ticket
$18.40
Above yesterday
Waste %
1.9%
On core items
Lunch throughput is below expected levels given transaction volume and needs immediate attention
Sales
Product over-usage detected on key menu items exceeding expected consumption rates
BOH / Labor
Waste variance tied to two high-velocity menu items
COGS

Two problems define QSR performance.

Every QSR operator is solving two fundamentally different problems — and they require two different lenses.

Store inefficiency compounds. Portfolio complexity hides it.

THREAD 1
Store Execution
This is where margin is won or lost — inside the four walls of each restaurant.
  • Labor drift and overtime
  • Service time and throughput
  • Product waste and over-usage
  • Coupon misuse and discount leakage
  • Upsell and order quality

Problem: teams know the numbers, but not what to fix today

THREAD 2
Franchise & Multi-Brand Visibility
As operators scale across brands, the problem shifts from execution to clarity across the portfolio.
  • Multiple POS and data sources
  • Inconsistent KPI definitions
  • No cross-brand comparison
  • Manual consolidation by finance
  • No portfolio prioritization
Problem: Leadership cannot see where to focus across brands
POS BOH / Labor Inventory Transactions Marketing Finance Delivery

How the platform works

The simplest way to understand tRS is as a connected operating layer for QSR: systems feed a restaurant model, and that model powers reporting, AI, and action.
Source systems
Restaurant systems generate the raw data
tRS pulls data from the systems different teams already use to run the business — including POS, BOH (Back of House systems covering labor, scheduling, and cost control), inventory, transactions, marketing, finance, and delivery platforms.
Restaurant model
The data is structured into one restaurant intelligence model
Stores, dayparts, products, promotions, labor, transactions, and financial outcomes are linked into one consistent operating view.
Reporting + AI
Reporting and AI work from the same model
Teams can build automated reporting from the connected operating model and use AI on top of the same definitions, calendars, and logic. That means no constant rebuilding, reconciling, or debating which number is right.
Execution
Teams can act faster at the store and network level
Operations, finance, and field teams can see the same issues earlier, prioritize action, and improve execution across every location.
Systems → Restaurant model → Reporting + AI → Execution

Two Distinct Capabilities — Built for Two Different Problems

The platform solves both store execution and portfolio visibility —
but these are fundamentally different use cases and should be understood separately.

AI for QSR & Multi-unit Restaurant Operations

Because AI runs on the same connected restaurant model as reporting, it analyzes the business the same way your teams do — across stores, labor, products, transactions, promotions, and financial outcomes.
Surface restaurant issues automatically
The platform continuously monitors the model and flags issues common in QSR operations — labor spikes, food variance, transactions throughput gaps, promotion underperformance, and store execution changes — without teams needing to build reports first.
Answer operational questions instantly
Instead of manually pulling POS, labor, and finance reports, teams can ask questions about stores, dayparts, menu items, promotions, and labor efficiency and receive simple explanations based on the connected operating data.
Solve multi-step operational problems
Because the model connects transactions, transactions, labor, promotions, and financials, AI can trace problems across the business.
Example: a promotion drives higher-than-expected lunch transactions in a region. The platform can identify which stores were understaffed, where throughput slipped, how labor % moved, and what the net sales impact was — without anyone manually stitching the analysis together.
LIVE AI QUESTIONS

Bring operations, finance, and field leadership onto the same operating truth.

The Retail Score helps QSR and multi-unit restaurant brands connect store, labor, transactions, inventory, marketing, and finance data into one intelligence layer for faster analysis and better execution.
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Our Clients

We focus on delivering a tangible return on investment be it through improved productivity, finding growth or aiding smarter decision-making. Collectively, we have over 50 years of experience in retailing, retail analytics, data management and insights.

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