Restaurant Business Financial Model with AI Projections

Tested prompts for restaurant financial model template compared across 5 leading AI models.

BEST BY JUDGE SCORE Claude Haiku 4.5 9/10

You're opening a restaurant, pitching investors, or trying to figure out if your concept actually pencils out. You need a financial model that projects revenue, food and labor costs, startup capital, and break-even timing. Generic spreadsheet templates give you empty cells and no guidance on what numbers to plug in. That's the gap this page fills.

Instead of downloading a static Excel template and guessing at cover counts, average ticket, prime cost ratios, and ramp-up curves, you can use a tested AI prompt to generate a full 3-year financial model tailored to your concept, location, seating capacity, and service style. The four model outputs below show how GPT-4, Claude, Gemini, and Llama handle the same restaurant modeling prompt so you can pick the one that matches your needs.

Below the outputs, you'll find when this AI approach beats a traditional template, when it doesn't, concrete examples across QSR, fine dining, and ghost kitchens, and the mistakes that sink most first-time restaurant pro formas.

When to use this

Use an AI-generated financial model when you need a defensible projection fast, don't have a CFO or accountant on retainer yet, and want to stress-test assumptions before committing capital. It works best when you can describe your concept in detail (cuisine, seats, check average, hours, labor mix) and want the AI to fill in industry-standard ratios and benchmarks.

Pitching a 3-year pro forma to investors or an SBA lender
Comparing two location options with different rents and foot traffic
Testing whether a second location is financially viable
Building a break-even analysis for a new menu concept or ghost kitchen
Quickly updating projections when COGS or wages shift

When this format breaks down

Filing taxes or audited financials: use a licensed CPA with actual books
Complex multi-entity operations with franchise royalties, equity waterfalls, or tax structuring
Replacing real POS data once you're operating: plug in actuals instead of AI estimates
Jurisdictions with unusual tax or labor rules the model won't know (tipped wage credits, VAT tiers, specific liquor tax schedules)

The prompt we tested

You are a restaurant finance expert building a comprehensive, investor-ready financial model for a restaurant business. Based on the restaurant details provided, generate a complete financial model template with monthly projections for Year 1 and annual projections for Years 2-5.

Instructions: Format the output as clean markdown tables with dollar figures rounded to whole numbers and percentages to one decimal. Use industry benchmark assumptions when specifics are missing (e.g., prime cost 55-65%, food cost 28-32%) and flag each assumption clearly. Keep the model comprehensive but readable, with brief notes explaining each section's logic.

Restaurant Details:
I'm opening a 60-seat fast-casual Mediterranean restaurant in Austin, TX with an average check of $18 for lunch and $28 for dinner. Projected buildout cost is $450K, monthly rent is $12K, and I plan to open 7 days a week with lunch and dinner service. I need a 5-year financial model to share with SBA lenders and potential investors.

Produce the financial model now, structured as clearly labeled tables with assumptions, calculations, and outputs. Include: (1) Key Assumptions (seats, turns, avg check, days open, food/beverage mix), (2) Revenue Build (covers × avg check, dine-in vs takeout/delivery), (3) COGS (food cost %, beverage cost %), (4) Labor (FOH, BOH, management with wages and benefits), (5) Operating Expenses (rent, utilities, marketing, supplies, insurance, R&M), (6) Pre-opening & CapEx (buildout, equipment, licenses), (7) P&L Statement, (8) Cash Flow Statement, (9) Break-even Analysis, (10) Key Metrics & Ratios (prime cost %, EBITDA margin, payback period). End with 3-5 sensitivity scenarios (base, downside, upside).

Model comparison

Model	Quality	Cost	Latency	Tokens
Claude Opus 4.7	9	$0.1633	26.5s	650 / 2048
Claude Haiku 4.5	9	$0.0107	18.2s	431 / 2048
GPT-5.4	Not run yet
Gemini 2.5 Pro	Not run yet
Grok 4.1 Fast Reasoning	9	$0.0011	31.9s	536 / 2048

Outputs

Claude Opus 4.7 9/10

# Mediterranean Fast-Casual Restaurant — 5-Year Financial Model
**Location:** Austin, TX | **Concept:** 60-seat Fast-Casual Mediterranean | **Service:** Lunch + Dinner, 7 days/week

---

## 1. Key Assumptions

| Category | Assumption | Value | Notes / Flag |
|---|---|---|---|
| **Capacity** | Seats | 60 | Given |
| | Days open/year | 361 | Closed ~4 holidays |
| | Lunch hours | 11am–3pm | Standard FC lunch |
| | Dinner hours | 5pm–9pm | Standard FC dinner |
| **Table Turns (mature, Y2+)** | Lunch turns/day | 1.8x | ⚠️ Benchmark: FC lunch 1.5–2.0x |
| | Dinner turns/day | 1.5x | ⚠️ Benchmark: FC dinner 1.2–1.8x |
| **Avg Check** | Lunch | $18 | Given |
| | Dinner | $28 | Given |
| **Seat Utilization** | % seats filled/turn | 70% | ⚠️ Assumption |
| **Channel Mix** | Dine-in | 60% | ⚠️ FC benchmark |
| | Takeout | 25% | ⚠️ Includes online pickup |
| | Delivery (3P) | 15% | ⚠️ Net of ~25% commission |
| **Revenue Mix** | Food | 88% | ⚠️ Mediterranean skews low-alcohol |
| | Beverage (incl. beer/wine) | 12% | ⚠️ Assumed beer/wine license |
| **COGS** | Food cost % | 30.0% | ⚠️ Benchmark 28–32% |
| | Beverage cost % | 22.0% | ⚠️ Benchmark 18–24% |
| **Ramp (Y1)** | Month 1 | 55% of mature | ⚠️ Standard ramp curve |
| | Month 6 | 85% | |
| | Month 12 | 100% | |

---

## 2. Revenue Build — Mature Run-Rate (Monthly)

**Formula:** Covers = Seats × Turns × Utilization × Days; Revenue = Covers × Avg Check

| Daypart | Seats | Turns | Util % | Covers/Day | Days/Mo | Covers/Mo | Avg Check | Revenue/Mo |
|---|---|---|---|---|---|---|---|---|
| Lunch | 60 | 1.8 | 70% | 76 | 30.1 | 2,287 | $18 | $41,166 |
| Dinner | 60 | 1.5 | 70% | 63 | 30.1 | 1,896 | $28 | $53,083 |
| **Total** | | | | **139** | | **4,183** | **$22.55** | **$94,249** |

**Mature Annual Revenue (Y2 run-rate):** ~$1,131,000

---

## 3. Year 1 Monthly P&L (Revenue Ramp)

| Month | Ramp % | Revenue | Food COGS | Bev COGS | Total COGS | Gross Profit |
|---|---|---|---|---|---|---|
| M1 | 55% | $51,837 | $13,685 | $1,368 | $15,053 | $36,784 |
| M2 | 62% | $58,434 | $15,427 | $1,542 | $16,969 | $41,465 |
| M3 | 68% | $64,089 | $16,920 | $1,692 | $18,612 | $45,477 |
| M4 | 73% | $68,802 | $18,164 | $1,816 | $19,980 | $48,822 |
| M5 | 79% | $74,457 | $19,657 | $1,966 | $21,623 | $52,834 |
| M6 | 85% | $80,112 | $21,150 | $2,115 | $23,265 | $56,847 |
| M7 | 88% | $82,939 | $21,896 | $2,190 | $24,086 | $58,853 |
| M8 | 91% | $85,766 | $22,642 | $2,264 | $24,906 | $60,860 |
| M9 | 94% | $88,594 | $23,389 | $2,339 | $25,728 | $62,866 |
| M10 | 97% | $91,421 | $24,135 | $2,414 | $26,549 | $64,872 |
| M11 | 99% | $93,307 | $24,633 | $2,463 | $27,096 | $66,211 |
| M12 | 100% | $94,249 | $24,882 | $2,488 | $27,370 | $66,879 |
| **Y1 Total** | **84%** | **$934,007** | **$246,580** | **$24,657** | **$271,237** | **$662,770** |

*COGS: Food = 88% × Revenue × 30%; Bev = 12% × Revenue × 22%*

---

## 4. Labor Plan

### 4a. Staffing Roster (Mature)

| Role | # FTEs | Avg Wage | Hrs/Wk | Monthly Cost |
|---|---|---|---|---|
| **Management** | | | | |
| General Manager | 1.0 | $70,000/yr | Salary | $5,833 |
| Asst. Manager / Shift Lead | 1.0 | $50,000/yr | Salary | $4,167 |
| **BOH** | | | | |
| Head Chef / Kitchen Mgr | 1.0 | $60,000/yr | Salary | $5,000 |
| Line Cooks | 4.0 | $20/hr | 35 | $12,133 |
| Prep Cook / Dishwasher | 2.0 | $16/hr | 35 | $4,853 |
| **FOH** | | | | |
| Counter / Cashier | 3.0 | $15/hr | 30 | $5,850 |
| Food Runner / Busser | 2.0 | $14/hr | 30 | $3,640

Claude Haiku 4.5 9/10

# Comprehensive 5-Year Financial Model
## Mediterranean Fast-Casual Restaurant | Austin, TX

---

## SECTION 1: KEY ASSUMPTIONS

### A. Restaurant Operating Parameters

| Metric | Value | Notes |
|--------|-------|-------|
| **Seating Capacity** | 60 seats | Fast-casual format with some table service |
| **Days Open Annually** | 365 days | Open 7 days/week, minimal closures |
| **Service Format** | Dine-in / Takeout / Delivery | Split modeled below |
| **Avg Check - Lunch** | $18 | 11am-2:30pm window |
| **Avg Check - Dinner** | $28 | 5pm-10pm window |
| **Blended Avg Check** | $23.00 | Weighted by revenue mix |

### B. Seat Turns & Covers (Year 1 Ramp)

| Period | Lunch Turns | Dinner Turns | Daily Lunch Covers | Daily Dinner Covers | Utilization % |
|--------|-------------|--------------|-------------------|-------------------|--------------|
| **Months 1-3** | 1.2 | 1.3 | 36 | 39 | 62.5% |
| **Months 4-6** | 1.4 | 1.5 | 42 | 45 | 72.5% |
| **Months 7-9** | 1.5 | 1.7 | 45 | 51 | 80.0% |
| **Months 10-12** | 1.6 | 1.8 | 48 | 54 | 85.0% |
| **Year 2+ (Mature)** | 1.8 | 2.0 | 54 | 60 | 95.0% |

**Assumptions:** Fast-casual lunch-driven model; dinner more variable; Year 1 ramps from conservative 62.5% to 85% utilization by Dec; Year 2 reaches mature 95% with 1.8 lunch turns and 2.0 dinner turns.

### C. Revenue Mix

| Channel | Year 1 % | Year 2+ % | Notes |
|---------|----------|-----------|-------|
| **Dine-in** | 65% | 70% | Core offering, higher margins |
| **Takeout** | 25% | 20% | Declining as dine-in grows |
| **Delivery (3rd party)** | 10% | 10% | 15-20% commission, lower margin |

**Avg Check Adjustment:** Delivery orders avg 5-10% lower; takeout slightly lower. Blended $23 accounts for this mix.

### D. Food & Beverage Cost Percentages

| Category | Cost % | Benchmark | Notes |
|----------|--------|-----------|-------|
| **Food Cost (% of food revenue)** | 30.0% | 28-32% FSR | Mediterranean menu with proteins, fresh veg |
| **Beverage Cost (% of bev revenue)** | 22.0% | 20-28% FSR | Wine, beer, non-alcoholic focus |
| **Beverage Mix** | 15% of total revenue | 10-20% industry | Moderate alcohol upsell potential |
| **Blended COGS %** | 28.9% | 28-35% FSR | Conservative for QSR-FSR hybrid |

**Beverage Calculation:** If 15% of $23 check = $3.45, with 22% cost = $0.76 cost; food $19.55 at 30% = $5.87 cost; total COGS $6.63 / $23 = 28.8%.

### E. Labor Assumptions (Year 1)

| Role | FTE Count | Annual Salary | Fringe (32%) | Notes |
|------|-----------|---------------|--------------|-------|
| **General Manager** | 1.0 | $50,000 | $16,000 | Lead P&L, hiring, operations |
| **Assistant Manager** | 1.0 | $40,000 | $12,800 | Shift coverage, scheduling |
| **Lead Line Cook** | 1.0 | $36,000 | $11,520 | BOH supervision, consistency |
| **Cooks (FTE)** | 1.5 | $32,000 each | $15,360 | Prep, line coverage |
| **Prep/Utility** | 1.0 | $28,000 | $8,960 | Prep, dish, cleanup |
| **Servers/Hosts (FTE)** | 2.5 | $24,000 each | $19,200 | Service staff, flex scheduling |
| **Barista/Expo (FTE)** | 1.5 | $26,000 each | $12,480 | Beverage, order expo, POS |
| **TOTAL BOH (FTE)** | 3.5 | — | — | 3.5 FTE |
| **TOTAL FOH (FTE)** | 4.0 | — | — | 4.0 FTE |
| **TOTAL PAYROLL FTE** | 8.5 | $279,360 | $96,320 | **Total Salary + Fringe: $375,680/yr** |

**Notes:** 
- Fringe includes FICA (7.65%), health insurance (est. $200/mo × 8.5 = $20.4K), workers' comp (2%), training (1%), uniforms (0.5%), payroll services (0.5%) = ~32% total.
- Year 1: Partial ramp (hire progressively months 1-3); modeled as avg 7.2 FTE for Year 1, reaching 8.5 by Q4.
- Year 2+: Add 1 FTE manager/lead (growth) = 9.5 FTE @ blended $39K salary + 32% = ~$468K annually.

### F. Labor Cost Evolution

| Year | Total FTE | Monthly Labor Cost | % of Revenue |
|------|-----------|-------------------|--------------|
| **Year 1** | 7.2 (avg) | $26,540 | 33.5% |
| **Year 2** | 9.5 | $36,875 | 31.2% |
| **Year 3** | 10.0 | $39,500 | 29.8% |
| **Year 4** | 10.5 | $41,580 | 28.5% |
| **Year 5** | 10.5 | $42,838 | 27.2% |

**Prime Cost Target:** COGS + Labor ≤ 62%; modeled to 60-62% range.

---

## SECTION 2: REVENUE BUILD

### A. Year 1 Monthly Covers & Revenue

| Month | Phase | Lunch Covers | Dinner Covers | Total Covers | Avg Check | Gross Revenue | Notes |
|-------|-------|--------------|---------------|--------------|-----------|-----------------|-------|
| **Jan** | Launch (Week 1 delayed) | 234 | 273 | 507 | $23.00 | $11,661 | Soft opening ramp |
| **Feb** | Growth | 504 | 585 | 1,089 | $23.00 | $25,047 | Full month, gaining traction |
| **Mar** | Stabilizing | 630 | 731 | 1,361 | $23.00 | $31,303 | Spring demand |
| **Apr** | 62.5% util | 1,080 | 1,170 | 2,250 | $23.00 | $51,750 | Stabilized ramp phase |
| **May** | Accelerating | 1,260 | 1,350 | 2,610 | $23.00 | $60,030 | Summer + graduation season |
| **Jun** | 72.5% util | 1,260 | 1,350 | 2,610 | $23.00 | $60,030 | Peak summer |
| **Jul** | 72.5% util | 1,260 | 1,350 | 2

Grok 4.1 Fast Reasoning 9/10

# Restaurant Financial Model: 60-Seat Fast-Casual Mediterranean in Austin, TX

## 1. Key Assumptions
*All figures flagged with benchmarks where specifics missing. Sources: Industry standards (NRA, Restaurant365 benchmarks for fast-casual). Ramp-up assumes typical new restaurant stabilization (50% Months 1-3, 75% 4-6, 100% 7-12). Annual growth: 5% (conservative post-COVID Austin market).*

| Assumption | Value | Notes/Benchmark |
|------------|-------|-----------------|
| Seats | 60 | Given |
| Days Open/Week | 7 | Given (365 days/year) |
| Lunch Turns/Day | 2.5 | Fast-casual benchmark (high turnover) |
| Dinner Turns/Day | 2.0 | Fast-casual benchmark |
| Daily Covers (Base) | 270 (150 lunch + 120 dinner) | Seats × turns |
| Avg Check: Lunch | $18 | Given |
| Avg Check: Dinner | $28 | Given |
| Weighted Avg Check | $22 | (55.6% lunch × $18) + (44.4% dinner × $28) |
| Dine-in % / Takeout-Delivery % | 70% / 30% | Benchmark; avg check same across |
| Food % of Rev / Bev % | 85% / 15% | Mediterranean benchmark (heavy plates) |
| Food Cost % | 30.0% | Benchmark 28-32% |
| Beverage Cost % | 20.0% | Benchmark 18-25% |
| Total COGS % | 28.5% | (85%×30%) + (15%×20%) |
| Labor % of Rev (Target) | 32.0% | Benchmark 30-35%; starts 35% Y1 (ramp), falls to 30% |
| Prime Cost Target | 60.5% | COGS + Labor benchmark 55-65% |
| Rent/Mo | $12,000 | Given ($144K/year) |
| Annual Rev Growth | 5.0% | Conservative Austin market |
| Depreciation | 7 years straight-line | FF&E benchmark |
| Initial CapEx | $720,000 | Buildout $450K (given) + Equip $200K + Licenses $20K + Pre-op $50K (benchmarks) |
| Startup Working Capital | $100,000 | 1-2 months OpEx benchmark |
| Total Startup Investment | $820,000 | CapEx + WC (assumes equity/debt funded) |

## 2. Revenue Build (Y1 Monthly; Annual Y2-5)
*Base monthly rev: 8,213 covers × $22 = $184K. Dine-in 70% ($129K), Takeout/Delivery 30% ($55K).*

### Y1 Monthly Revenue ($000s)
| Month | Ramp Factor | Total Covers | Dine-in (70%) | Takeout/Deliv (30%) | Total Revenue |
|-------|-------------|--------------|---------------|---------------------|---------------|
| 1 | 50% | 4,107 | $64 | $28 | $92 |
| 2 | 50% | 4,107 | $64 | $28 | $92 |
| 3 | 50% | 4,107 | $64 | $28 | $92 |
| 4 | 75% | 6,160 | $97 | $41 | $138 |
| 5 | 75% | 6,160 | $97 | $41 | $138 |
| 6 | 75% | 6,160 | $97 | $41 | $138 |
| 7 | 100% | 8,213 | $129 | $55 | $184 |
| 8 | 100% | 8,213 | $129 | $55 | $184 |
| 9 | 100% | 8,213 | $129 | $55 | $184 |
| 10 | 100% | 8,213 | $129 | $55 | $184 |
| 11 | 100% | 8,213 | $129 | $55 | $184 |
| 12 | 100% | 8,213 | $129 | $55 | $184 |
| **Y1 Total** | - | **73,919** | **1,157** | **497** | **1,794** |

### Annual Revenue Y1-Y5 ($000s)
| Year | Total Revenue | Growth |
|------|---------------|--------|
| 1 | 1,794 | - |
| 2 | 1,884 | 5.0% |
| 3 | 1,978 | 5.0% |
| 4 | 2,077 | 5.0% |
| 5 | 2,181 | 5.0% |

## 3. COGS ($000s)
*Applied monthly/annual as % of revenue.*

| Category | % of Rev | Y1 Total |
|----------|----------|----------|
| Food Cost | 25.5% | 457 |
| Beverage Cost | 3.0% | 54 |
| **Total COGS** | **28.5%** | **511** |

## 4. Labor ($000s)
*Fixed: Mgmt $12K/mo ($144K/yr), BOH $18K/mo ($216K/yr), FOH Base $12K/mo ($144K/yr) = $504K/yr fixed. Variable: 12% of rev (benefits/tips/overtime). Total starts 35% Y1, optimizes to 30%.*

| Category | Fixed (Annual) | Variable (% Rev) | Y1 Total |
|----------|----------------|-------------------|----------|
| Management | 144 | - | 144 |
| BOH (Kitchen) | 216 | 5% | 246 |
| FOH | 144 | 7% | 269 |
| **Total Labor** | **504** | **12%** | **659** (36.7% ramp-adjusted) |

## 5. Operating Expenses ($000s, Annual Y1 Base)
*Variable % of rev; fixed annual.*

| Category | Amount/Fixed or % Rev | Y1 Total | Benchmark |
|----------|-----------------------|----------|-----------|
| Rent | $144K | 144 | Given |
| Utilities | 4.0% | 72 | Benchmark |
| Marketing | 3.0% | 54 | New store ramp |
| Smallware/Supplies | 2.0% | 36 | Benchmark |
| Insurance | $40K | 40 | 2% rev benchmark |
| Repairs & Maintenance | 2.0% | 36 | Benchmark |
| **Total OpEx** | - | **382** | 21.3% of Y1 Rev |

## 6. Pre-opening & CapEx ($000s)
| Item | Amount | Notes |
|------|--------|-------|
| Buildout | 450 | Given |
| Equipment/FF&E | 200 | Benchmark (ovens, counters) |
| Licenses/Permits | 20 | TX benchmark |
| Pre-opening Labor/Training | 50 | 2 months benchmark |
| **Total CapEx** | **720** | Depreciated 7yrs ($103K/yr) |
| Startup WC (Cash Buffer) | 100 | 1 month OpEx |
| **Total Investment** | **820** | For payback calc |

## 7. P&L Statement
### Y1 Monthly Summary ($000s)
| Month | Revenue | COGS (28.5%) | Labor (~35%) | OpEx | EBITDA | Deprec (8.6/mo) | EBIT |
|-------|---------|--------------|--------------|------|--------|-----------------|------|
| 1 | 92 | 26 | 36 | 27 | 3 | 9 | -6 |
| 2 | 92 | 26 | 36 | 27 | 3 | 9 | -6 |
| 3 | 92 | 26 | 36 | 27 | 3 | 9 | -6 |
| 4 | 138 | 39 | 50 | 35 | 14 | 9 | 5 |
| 5 | 138 | 39 | 50 | 35 | 14 | 9 | 5 |
| 6 | 138 | 39 | 50 | 35 | 14 | 9 | 5 |
| 7 | 184 | 52 | 64 | 42 | 26 | 9 | 17 |
| 8 | 184 | 52 | 64 | 42 | 26 | 9 | 17 |
| 9 | 184 | 52 | 64 | 42 | 26 | 9 | 17 |
| 10 | 184 | 52 | 64 | 42 | 26 | 9 | 17 |
| 11 | 184 | 52 | 64 | 42 | 26 | 9 | 17 |
| 12 | 184 | 52 | 64 | 42 | 26 | 9 | 17 |
| **Y1 Total** | **1,794** | **511** | **659** | **382** | **242** | **103** | **139** |

### 5-Year Annual P&L ($000s)
| Year | Revenue | COGS | Labor | OpEx | EBITDA | Deprec | EBIT | Taxes (21%) | Net Income |
|------|---------|------|-------|

What makes these work

01

Feed it real local data
The AI can't guess your rent, wages, or foot traffic. Give it the exact $/sq ft, your market's average server wage, and realistic cover counts from comparable restaurants. Garbage assumptions produce garbage projections no matter how good the model is.
02

Ask for prime cost explicitly
Prime cost (COGS plus labor) is the number investors and operators actually care about. Tell the AI to target 55-65% prime cost and flag any month it exceeds 65%. That single constraint catches most unrealistic projections.
03

Request sensitivity tables
A point estimate is useless. Ask the AI to produce a table showing EBITDA at plus or minus 10% and 20% on ticket size and cover count. You'll see immediately whether your concept has margin to absorb a slow quarter.
04

Include a ramp curve
New restaurants don't hit mature revenue on day one. Specify a 6-9 month ramp from 40% to 85% of projected capacity. Models that show full revenue in month 1 get laughed out of investor meetings.

More example scenarios

#01 · Fast-casual taco shop, 40 seats, suburban strip mall

Input

Build a 3-year financial model for a fast-casual taco concept. 40 seats, 1,800 sq ft, $28/sq ft rent, $12 average ticket, 180 covers per day at maturity, 6-month ramp to 80% capacity. Labor: 2 cooks, 1 cashier per shift, $16/hr average. Target 30% food cost, 28% labor cost.

Expected output

A month-by-month P&L showing $1.18M Year 1 revenue ramping to $1.97M Year 3, prime cost at 58%, EBITDA of $142K Year 1 and $310K Year 3. Includes startup cost summary ($385K buildout + $45K working capital), break-even at month 8, and sensitivity table for ticket size and cover count.

#02 · Fine dining concept seeking investor capital

Input

60-seat contemporary American restaurant in downtown Austin. $85 average check, 2 turns on weekends, 1 turn weekdays. Full bar, 28% beverage mix. Executive chef at $95K, sous at $65K, full FOH team. Need investor-ready model with sources and uses, IRR, and 5-year exit.

Expected output

A detailed sources and uses showing $1.4M total raise, 5-year P&L with Year 1 revenue of $2.8M growing to $3.6M, 12% EBITDA margin by Year 3. Investor returns: 2.4x MOIC, 22% IRR on a Year 5 sale at 4x EBITDA. Monthly cash flow shows 4-month burn before positive operating cash.

#03 · Ghost kitchen expansion from existing brand

Input

Existing pizza brand adding a ghost kitchen location. $8K monthly rent shared space, delivery-only, 3 third-party platforms at 25-30% commission. Projected 80 orders/day at $32 average. Food cost 26%, packaging 4%, labor 18%.

Expected output

Monthly model showing $77K gross revenue, $54K net after platform fees, contribution margin of 22% after COGS, packaging, and labor. Payback on $85K buildout in 14 months. Includes breakdown of platform economics and a scenario where commissions drop to 20% via direct ordering.

#04 · Coffee shop with roastery side revenue

Input

Specialty coffee shop, 25 seats, $6.50 average ticket, 350 transactions/day. Adding wholesale roasting: target 500 lbs/week at $14/lb wholesale margin. $45K espresso setup, $28K roaster. Opening budget $240K.

Expected output

Dual-revenue model: retail at $830K Year 1, wholesale ramping from $0 to $320K by Year 2. Blended gross margin 68%, net margin 11% Year 2. Break-even at month 10 for retail only, month 14 including wholesale ramp costs.

Common mistakes to avoid

Ignoring working capital
Founders budget buildout but forget they need 2-3 months of operating cash to cover payroll and inventory before revenue stabilizes. The AI will skip this unless you ask for it explicitly. Running out of cash in month 3 kills otherwise viable concepts.
Unrealistic labor assumptions
Plugging in 22% labor cost for full-service dining is fantasy. Full-service typically runs 30-35% after payroll taxes, benefits, and overtime. Tell the AI to include employer taxes (roughly 10% on top of wages) and a realistic schedule.
Forgetting credit card and platform fees
2.5-3% on card transactions and 25-30% on third-party delivery is real money. On $1.5M revenue that's $40K+ in card fees alone. Models that skip this overstate EBITDA by a full percentage point.
No seasonality
January and February are brutal for most restaurants. Summer patios and holiday catering swing revenue 20-30% month to month. A flat monthly projection hides cash crunches the operator will actually face.
Treating AI output as final
The model is a starting draft. Have a restaurant accountant or operator review the assumptions before you show it to a lender. Investors spot AI-generic numbers (exactly 30% food cost every month) instantly.

Related queries

Frequently asked questions

Is an AI-generated restaurant financial model good enough for an SBA loan?

It's a strong first draft but most SBA lenders want a CPA or advisor to sign off on the assumptions. Use the AI output to build the structure, then have a restaurant accountant review the labor math, tax treatment, and debt service coverage before submission.

What assumptions should I include in the prompt?

Seats, average check, covers per day at maturity, ramp period, rent per square foot, total square footage, labor mix and wage rates, food cost target, beverage mix, and your opening budget. The more specific, the more useful the output. Vague prompts produce generic models.

How is this different from a downloadable Excel template?

An Excel template gives you empty cells and formulas. The AI generates populated projections tailored to your concept, explains the assumptions, and produces sensitivity scenarios on request. You can also iterate in seconds by asking it to change a variable, which would take 20 minutes in a static template.

What's a realistic prime cost for a new restaurant?

55-65% is the healthy range. Quick-service concepts often hit 55-60%, full-service typically 60-65%, and fine dining can push higher due to ingredient costs. If your model shows prime cost under 50%, something is wrong with your labor or COGS assumptions.

Can the AI model franchise fees and royalties?

Yes, if you specify them. Include the franchise fee amortization, ongoing royalty percentage (typically 4-8% of gross), marketing fund contribution (2-4%), and any required tech stack fees. Without these inputs the model will underestimate your expenses significantly.

How long should my projections cover?

Three years is standard for most operators and SBA lenders. Investor pitches for larger concepts or multi-unit rollouts usually want 5 years with a terminal value or exit assumption. Month-by-month detail for Year 1, quarterly for Years 2-3, and annual thereafter is the typical format.