From 5 stores to 10,000 —
on the same screen.
Group · Company · Brand · Store — a 4-level org structure, 10 operating BI tabs, automated month-end close, and HQ operations control. Everything HQ checks daily, gathered in one place.
Beta stores
Franchisees (seed)
HQ BI tabs
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.
Same question, different answers.
Without attachments, general-purpose AI stays generic. RestoGenie is an HQ-only AI trained in real time on every store's revenue, procurement, org, and trade-area data.
“Which stores are underperforming this month, why, and what should we do?”
General-purpose AI (free)
e.g. ChatGPT Free · Gemini Free
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.
⚠ Analysis limited to attached files · no access to HQ standards, external events, AI Vision, or live DB
General-purpose AI (paid)
e.g. ChatGPT Plus · Gemini Pro
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.
⚠ Analysis limited to attached files · no access to HQ standards, external events, AI Vision, or live DB
RESTO GENIE AI
Restaurant-trained · live store DB
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 General AI tools can only reason over files you attach. They can’t reach your HQ playbook, local events, competitor activity, AI Vision, HRM, or your live database — so you have to re-upload everything every time it changes. RestoGenie continuously ingests POS, 5+ delivery platforms, ad accounts, HQ docs, HRM, and AI Vision (foot traffic, age, gender, conversion) via ontology-based RAG, and answers with tables, charts, insights, actions, and sources — every time.
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
HQ accounting
is freed from manual Excel work
Per-store month-end close and HQ procurement cost allocation, all in one screen. HQ closes in one shot; franchisees see the result.
- Per-store P&L close, automated
- HQ procurement costs auto-allocated by store
- HQ closes in one shot — franchisees view the results
HQ bulk ordering
HQ places one consolidated order (food + supplies)
Auto cost allocation
Allocated to each store by usage
Store P&L auto-built
Sales + settlements + labor + other costs rolled up
Close & confirm
HQ closes the books for every store in one click
Franchisee review
Franchisees see the finished close (read-only)
Every store's P&L compared on one board.
Revenue, food cost, labor, SG&A, operating profit — every line in a single matrix. Close status (Done / In progress / Missing / Disputed) and overall close rate update in real time, so HQ can finalize the period in one pass without month-end pressure.
10
Total
3
Done
3
In progress
2
Missing
2
Disputed
| Line item | Daehakro ST-001 | Seongsu ST-002 | Gangnam ST-003 | Hongdae ST-004 | Itaewon ST-005 | Busan Seomyeon ST-006 | Daejeon Dunsan ST-007 | Gwangju Sangmu ST-008 | Jeju ST-009 | Suwon ST-010 |
|---|---|---|---|---|---|---|---|---|---|---|
▶ 1. Revenue | 66,406 | 56,250 | 45,313 | 74,219 | 93,750 | 60,938 | 68,750 | 50,781 | 71,875 | 42,969 |
▶ 2. Food cost | 22,656 | 19,688 | 16,016 | 25,000 | 31,250 | 21,484 | 24,219 | 17,969 | 24,219 | 15,234 |
▶ 3. Labor (①+②+③) | 19,531 | 16,797 | 14,063 | 21,484 | 26,563 | 17,578 | 19,922 | 15,234 | 20,703 | 13,672 |
▶ 4. SG&A | 17,969 | 14,844 | 11,719 | 20,703 | 26,563 | 16,406 | 18,359 | 13,281 | 20,313 | 10,547 |
Other opex | 156 | 117 | 94 | 172 | 250 | 141 | 164 | 109 | 188 | 86 |
Discounts | 238 | 219 | 188 | 297 | 375 | 227 | 250 | 203 | 273 | 172 |
Depreciation (60 mo.) | 938 | 781 | 664 | 1,094 | 1,406 | 859 | 1,016 | 742 | 1,055 | 625 |
Non-op revenue | 1,133 | 859 | 719 | 1,289 | 1,641 | 977 | 1,172 | 820 | 1,250 | 688 |
Delivery tips | 1,250 | 1,094 | 898 | 1,641 | 2,188 | 1,172 | 1,367 | 977 | 1,523 | 820 |
5. Operating profit 1-2-3-4 | 6,250 | 4,922 | 3,516 | 7,031 | 9,375 | 5,469 | 6,250 | 4,297 | 6,641 | 3,516 |
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%
HQ operations control center —
we own ops end-to-end
AI pipeline status, store-platform integrations, scheduled data collection, execution history, delivery-app crawler status, and HQ manual learning — all unified in one operations control screen.
- AI pipeline status + execution history
- Scheduled data collection (toggleable · run now)
- Delivery-app crawler monitoring
- HQ manual AI learning management
01{02"query": "How are we doing on revenue today?",03"answer": "Today's running total is $342 (+12%)...",04"sources": [05{ "type": "pos", "id": "pos:store-3421", "weight": 0.42 },06{ "type": "delivery", "id": "doordash:order:d39204", "weight": 0.31 },07{ "type": "delivery", "id": "ubereats:order:u88102", "weight": 0.18 },08{ "type": "manual", "id": "rag:menu-pricing-v2.3", "weight": 0.09 }09],10"confidence": 0.94,11"model": "rg-chat-v1",12"rag_engine": "weaviate@7d3f"13}
The operating standards of restaurant HQs — built in by default.
Enterprise-grade security · Korean regulatory compliance · Battle-tested AI infrastructure
Korean Labor Standards Act §95
Korean labor guidelines — including the 10% cap on tardiness deductions — are built into the workforce module by default.
Korean PIPA §24 compliance
Sensitive PII (e.g., resident registration numbers) is encrypted at rest with AES-GCM 256-bit. Strict data isolation between franchisees and HQ.
Korean Franchise Act compliance
Every HQ↔franchisee order, settlement, ad-spend, and notice is disclosed through a full audit log. Franchisees see the same data, closing information asymmetry and supporting fair-trade obligations.
JWT auth + RBAC
Role-based access on a 4-level org tree (Group→Company→Brand→Store). Supervisor, manager, owner, and staff each see only what they should.
Spring AI + Weaviate RAG
OpenAI and Google GenAI operated safely through RAG grounded in your HQ manuals and recipes. Every answer is traceable to its source.
Oracle DB · daily backup · audit log
Enterprise-grade database with daily automated backups. Every data change is captured in a full audit log out of the box.
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
The questions we hear most before rollout
Request a demo
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5 min
Tailored demo
within 1 week
30-day PoC
free