Store performance.
The issues to review. Together.
Compare store performance and identify issues to review. Add purchasing, QSC and manual modules as your data is connected.
Org tree · 4 levels
1 group / 1 company / 2 brands / 76 storesSame structure whether you run 5 stores or 10,000. RBAC isolates data per supervisor and per executive.
If this is your HQ's reality, RestoGenie is the answer.
- 01 ─No comparisonEach store reports differently — HQ can't compare
- 02 ─Weekly travelSupervisors travel weekly to onboard new franchisees
- 03 ─0% dataHQ decisions made on gut feel
- 04 ─5+ systemsScattered store data, no unified HQ BI
"Bottom 3 stores this month" — one line, that's all.
Data across every store, brand, and region — answered in one reply. Click a question on the left.
* The live product demo is shown during your sales call.
What changes when questions connect to data?
An illustrative comparison of different data contexts, not a benchmark of other AI services. Actual answers depend on connected data, registered materials and access permissions.
“Which stores are underperforming this month, why, and what should we do?”
Before connecting data
An illustrative general-knowledge answer
I can only tell which stores are down if you share per-store sales data. In general, franchise underperformance comes from trade-area shifts, competitor openings, or operational quality — and typical responses are more SV visits, local promos, or a menu refresh.
This example connects only a file. Answers change with the data and systems made available.
With an attached file
An example limited to attached data
Share each store's actuals vs target plus trade-area info and I can rank the laggards. But to tell whether the weakness is dine-in or delivery, external (a competitor opening) or internal (operations), I also need channel-level sales and foot-traffic data. Files alone can't separate those layers.
This example connects only a file. Answers change with the data and systems made available.
RESTO GENIE AI
Connected operations data · scope varies
Bottom 3 by target attainment this month. Suwon is weakest at -18%.
| Store | Rev (M) | vs target | Driver |
|---|---|---|---|
| Suwon | 55 | -18% | Competitor |
| Gangnam | 58 | -12% | Dine-in |
| Gwangju Sangmu | 65 | -9% | Delivery |
💡 Insight
All three keep normal delivery — only dine-in fell. Suwon hit by a competitor opening nearby 2 weeks ago; AI Vision foot traffic -21%.
⚡ Suggested actions
Sources
The difference Answers depend on connected data and access permissions. These figures and scenarios are illustrative, not a benchmark of other products. RestoGenie works within the sales, purchasing and uploaded materials connected for each customer. Not every illustrated data source or feature is included by default. Confirm supported scope and refresh cycles in a demo.
Start with sales visibility, comparison and AI questions. Purchasing, QSC and manual modules depend on connected data and rollout scope. Screens below are illustrative.
Ask across every store —
the HQ AI chat
The store app's AI chat has an HQ edition too. Ask "this month's underperformers," "cost ratio by brand," or "growth by region" and get answers against every store's data — with tables, charts, and sources attached.
- Live queries across stores, brands, and regions
- Auto-flag underperforming stores · root-cause analysis
- Pick your data source (network DB / web search)
- Sources cited · tables & charts inline
What would you like to see across the network?
Underperformers · Brand compare · Cost-ratio outliers · Regional growth
Ask across every store.
Group → Company → Brand → Store
4-level org tree
Multi-brand operations, naturally structured. The same structure scales from 5 stores to 10,000.
- Group · Company · Brand · Store — 4 levels
- Role-based access (data isolated per supervisor / exec / owner)
- Zero integration cost when onboarding a new franchisee
Org tree · 4 levels
1 group / 1 company / 2 brands / 76 storesSame structure whether you run 5 stores or 10,000. RBAC isolates data per supervisor and per executive.
Every store's revenue and 10 operating BI tabs —
gathered in one screen
The revenue dashboard HQ checks daily (direct / franchise / consignment overview, growth, trade share, by-store detail) and 10 operating BI tabs share a single entry. Auto-flag top/bottom stores, multi-period and multi-store comparison, one-click drill-down to individual stores.
- Revenue dashboard, 6 tabs (Overview · Growth · Monthly · Trade share · Order cost · By store)
- 10 BI tabs (Summary · Ranking · Trends · Day · Channel · Hour · Menu · P&L · Compare · Store detail)
- Auto-flag top/bottom stores + instant store-level drill-down
Revenue dashboard
Miso Table · 76 storesCompany revenue · today
₩1.24B
Attainment
103%
Target ₩1.2B
₩1.24B
₩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% |
HQ · Unified BI
76 stores · 2026-05-18Network-wide KPIs
Net revenue
$920K
+8.2%
Transactions
84,231
+3.4%
Avg ticket
$10.92
+4.6%
Revenue mix by channel
Why the cost ratio is high —
pinpointed by store and item
Shows each store's order-to-revenue ratio (cost ratio) against a target (e.g. 34%), alongside weekly revenue, order, and cost-ratio trends. The screen pinpoints the stores and items where the cost ratio spikes.
- Order cost ratio vs target, tracked
- Weekly revenue · order · cost-ratio trends
- Per-store, per-item cost-ratio anomaly detection
Order-cost analysis
Miso Table · 76 stores · last 6 weeksTotal sales
₩34.4B
76 stores
Total orders
₩11.4B
supply price
Cost ratio
33.2%
vs. 34% target, -0.8%p
AI order analysis
AI: Store A's 50.7% cost ratio is 12pp above Store B (36.8%). Friday beef over-ordering (+₩24.1M) — rebalance recommended.
HQ announcements, manuals, recommended content —
one click → every franchisee, instantly
Auto-published to the 'News' tab in the franchisee app. Read receipts tracked, and HQ manuals are learned by AI so franchisee chatbots can use them immediately.
- Publish announcements, help docs, and recommended content
- Auto-track read receipts + auto-remind unread
- HQ manuals learned by AI → instantly available to franchisee chatbots
HQ content → every location, instantly
200 / 200 reachedPublished at
2:02:08 PM
Avg reach time
2.4 s
Acknowledged
94.3%
Self-checks + SV visits,
every store to one standard
Owner self-check lists and SV visit inspections in one dashboard. Compare per-store results at a glance, auto-surface the stores that need attention, and raise every store's operating quality to a standard level.
- SV visit — one item per screen, reason & photo enforced on fail
- Compare owner self-check vs SV visit per store
- Immediate action on legal non-compliance · overdue-visit alerts
- Auto-surface stores needing attention → standardize quality
Itaewon
12 / 61 · 20%Expiry & FIFO compliance
ⓘ Verify open-date labels
Reason (required on fail)
📷 Photo required (1)
QSC report
LIVESelf-check rate
88.3%
SV visit score
78
Self−visit diff
+10.3p
Overdue visits
2
| # | Store | Self | SV | Diff | Last |
|---|---|---|---|---|---|
| 1 | Suwon | 92.0% | 74 | +18.0p | 41d |
| 2 | Daejeon | 88.0% | 79 | +9.0p | 12d |
| 3 | Gangnam | 85.0% | 81 | +4.0p | 6d |
| – | Seongsu | — | — | — | none |
Prioritize stores where self-check and visit results diverge — bringing every store up to standard. Metrics update live and re-sort automatically.
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.
12+ items in progress
Released by HQ and franchisee-requested priority. Hover to pause.
HQ ERP integration
Bi-directional cost · inventory · logistics integration
Brand strategy support
New menu simulation · promo impact · new site recommendation
AI auto-decisioning
Auto-pause low-ROAS ads + auto-suggested actions for struggling stores
Recipe module
Per-brand recipes + standard portion/cost management
Mobile BI for HQ executives
Replace Excel decks · key KPIs on mobile · push alerts
Multilingual HQ dashboard
EN/JA UI for HQ + multi-country group operations
Data warehouse integration
Two-way sync with major DWHs (BigQuery · Snowflake · Databricks)
AI new-site recommendation
Trade area + competition + foot traffic → site scoring
Franchisee evaluation auto
HQ-standard scoring · supervisor reviews · auto-aggregated
Promo impact simulation
Forecast revenue/margin impact before HQ-wide promotions
HQ KPI auto-alerts
KakaoTalk / Slack / Email · KPI thresholds · auto-alerts
Franchisee satisfaction
Owner NPS · supervisor response analysis · churn prediction
All your restaurant data, managed in one place.
Connected to 12+ partners across POS, payments, delivery, ads, AI Vision, and messaging — so stores and HQ see all the data on a single screen.
Delivery platforms
POS · Payments
Advertising
AI Vision · Analytics
Messaging · Other
+ More integrations rolling out during beta
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
The questions we hear most before rollout
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.
Consultation
Workflows and store count
Tailored demo
Data and feature scope
Plan a pilot
Timing, fees and rollout criteria