Diagnose
Interview every team to map the repetitive manual work end to end
LIVE·Serving customer operationsBetter operating decisions.
Better operating decisions.
A shared view for stores and HQ.
HQ compares store performance. Owners ask questions about their own store. Connect purchasing, QSC and manual analysis to the data and workflows you need.
Store→
For franchisees
“What's today's revenue?”
Check your store’s sales and ask operational questions, with access based on your HQ rollout and role.
Explore the page
HQ→
For franchise headquarters
“Every store, one screen.”
Compare store performance and identify issues to review. Add purchasing, QSC and manual modules as your data is connected.
Explore the page
Built with operators. Exploring what comes next.
Current customers
Chipotle
Shabu All Day
AMOJE Food
cafe AMOJE
City Marché
Omuto tomato
mealions (Café)
A’BOUT COFFEE
ROOST PLACE
Adoption & collaboration discussions
Companies discussing potential adoption or collaboration. This does not indicate a signed contract or completed rollout.
THEBORN Korea
LOTTE GRS
Damga
Kkubeurakko
BRUNCH BEAN
HISBEANS
Euddeum Sushi
Noleoogae
ARC N BOOK
KICC EasyPOS
Payhere
ADT CAPS
NICE Payments
Product in action
HQ compares stores. Owners review today.
Illustrative screens for sales visibility and operating decisions. Purchasing, QSC and manual analysis depend on integrations and deployment scope.
For storesView full report ›
Hello, Miso Table Gangnam!
Tue, Aug 4 · sales/review widgets
Weekly revenue
₩12,480,000
156 orders · ₩80,000 avg
This week-14.2% vs last week
M
T
W
T
F
S
S
Key metrics
Orders
156
Avg ticket
₩80,000
Cancel rate
1.2%
Your store’s sales and operating metrics · AI questions within your access scope
See store features →For HQ
Revenue dashboard
Miso Table · 76 storesCompany revenue · today
₩1.24B
Attainment
103%
Target ₩1.2B
WTD103%
₩1.24B
MTD77%
₩3.68B
| Type | Today | /store | vs LW |
|---|---|---|---|
| Direct (6) | ₩150M | ₩2.5M | +0.8% |
| Franchise (54) | ₩950M | ₩1.76M | +25.9% |
| Consignment (16) | ₩140M | ₩0.88M | +19.2% |
Compare store sales · Find changes and issues to review
See HQ features →* Brand (Miso Table), store, and staff names are demo data.
TECHNOLOGY · ONTOLOGY GRAPHBeyond collecting data.
Beyond collecting data.
Connect what it means.
An ontology defines entities and relationships—stores, menus, time periods and operating rules. A graph makes those connections easier to explore. This example shows how context can help interpret restaurant operations data.
A question from the store
“Why did Wednesday lunch sales fall?”
Example evidence linked by store and time
Select evidence to explore its source and scope.
Linked by store and timeGangnam · Wednesday lunch
Selected evidence · POS sales
Example POS totals · Wednesday, 11 am–2 pm, versus the same weekday last week. The store and time window match.
Example AI response · Human review
Sales and foot traffic both declined. The nearby event may have contributed, but unchanged staffing does not rule out service issues. Check the event timing and store conditions before considering an advance group-menu promotion.
✓ 4 sources · Review the relationships
Illustrative demo · Store, figures and event are fictional. The connections explain the concept; they are not a reproduction of the live product’s answer interface.
01 · CONTEXT
Put numbers in context.
Compare different sources using a shared store and time period.
02 · TRACEABLE
Start with the evidence.
Review source data, reporting periods and registered operating rules.
03 · ACTIONABLE
Support the next decision.
Identify possible causes and checks for the person responsible.
Available data and features depend on integrations, uploaded materials and access permissions. Staff review the evidence before acting on AI suggestions.
Explore connections in your dataWe review data readiness and the scope of a tailored demo during your consultation.
CTRL-M · AX Consulting
Beyond the product — all the way to execution.
Extend the RestoGenie product around your workflows. CTRL-M supports system integration, process design, tailored implementation and adoption within an agreed scope.
Automate
Turn ordering, reviews, CS, and sales analysis into live systems on real data
Standardize
Roll out only what's proven to every store in the brand
TRUST · DATA & ADOPTION
Clear expectations before you start.
Review data, access and feature scope before planning your rollout.
Access for each role
Configure access around HQ, stores and user roles, then verify the required scope with actual user accounts.
Source and reporting period
Live collection, closed sales and purchasing data may cover different periods. Review source definitions and refresh cycles.
Core features and optional modules
Start with sales and AI questions. QSC, manuals and traffic analysis depend on the required data and integrations.
Connect existing systems
Review your POS, business systems, available data formats and integration access to define the scope.
Review AI suggestions
Distinguish available evidence from items that need checking. The responsible person reviews the evidence before deciding.
A staged rollout
Review your workflows in a demo, agree pilot scope, timing and support, then assess results before expanding.
FAQ · Frequently asked questions
The questions we hear most before rollout
Restaurant franchise HQ teams and their stores. HQ compares performance across locations while owners review their own store. For a standalone store, we first check the required workflow and available scope.
Inquiry · Talk to sales
Request a demo
Tell us about your workflows and current systems. We will review the available scope in a demo and discuss data readiness and pilot conditions.
01
Consultation
Workflows and store count
02
Tailored demo
Data and feature scope
03
Plan a pilot
Timing, fees and rollout criteria