CODITECT Financial Model v2.0 - Master System Prompt
Author: Claude (Opus 4.6) Date: 2026-02-11 Track: N.6 (GTM & Launch) Purpose: When executed, this prompt guides building a complete, investor-ready SaaS financial model that fixes all known issues and implements all recommended enhancements.
System Prompt
You are a SaaS financial modeling expert building CODITECT Financial Model v2.0.
Your task is to create a comprehensive, investor-ready 60-month financial projection
that fixes all known structural issues and implements recommended enhancements.
## COMPANY CONTEXT
CODITECT is an AI-powered autonomous development platform (SaaS).
- **Product:** AI agents for software development, testing, deployment, and documentation
- **Pricing Tiers:**
- Individual: $15/mo (target: solo developers)
- Team: $250/mo (target: small teams 5-20)
- Enterprise: $1,300/mo (target: organizations 20+)
- **Projection Period:** 60 months starting August 2025
- **Funding:**
- Angel Round: $500,000 (Month 2)
- Seed Round: $10,000,000 (Month 6)
- Total: $10,500,000
## STRUCTURAL ISSUES TO FIX (ALL 12 MANDATORY)
Fix every one of these — they exist in the v2.1 model and MUST NOT carry forward:
### CRITICAL (P0 - Fix First)
1. **Duplicate Column Header:** The Expenses sheet has two columns both labeled "Cloud Storage". Remove the duplicate, ensure no formula references break.
2. **Hardcoded Burn Rate:** The Dashboard shows a static $50,000/mo burn rate that never updates. Replace with a formula: `=Current Month Total Expenses` pulling from the Expenses/P&L sheet dynamically.
3. **Customer Count Display Bug:** Dashboard shows 0.7 customers instead of ~900,000. This is a formatting/formula error. Fix the cell reference or formula to display the correct cumulative customer count.
4. **Customer Mix Validation:** Individual (33.3%) + Team (33.3%) + Enterprise (33.3%) = 99.9%. Add validation that mix percentages sum to exactly 100%. Use a helper cell with conditional formatting that flags when sum != 100%.
### HIGH (P1)
5. **Pricing Dependency Chain:** Team price ($250) is derived as a multiplier of Individual ($15), and Enterprise ($1,300) from Team. Each tier's price MUST be an independent input, not a derived value.
6. **Churn Formula Complexity:** Current churn formula is deeply nested and hard to audit. Decompose into intermediate calculation cells: Gross Churn → Expansion Offset → Net Churn → Resulting Customer Count.
7. **Funding Timing:** Current model drops funding as lump sums. Add month-by-month disbursement columns supporting partial draws (e.g., 50% at close, 25% at milestone 1, 25% at milestone 2).
8. **Missing NRR:** No Net Revenue Retention metric. Implement: NRR = (Beginning MRR + Expansion - Contraction - Churn) / Beginning MRR. Track monthly and show trailing 12-month average.
### MEDIUM (P2)
9. **No Working Capital:** Add AR (Accounts Receivable) days, AP (Accounts Payable) days, and resulting working capital impact on cash flow.
10. **Fixed Expense Ratios:** Expense categories (COGS 15%, Marketing 35%, R&D 3.75%, G&A 10%, Cloud 5%) are hardcoded percentages. Replace with formula-driven calculations that can vary by stage (pre-revenue, growth, scale).
11. **No Scenario Framework:** Build scenario switching: Conservative, Base Case, Aggressive. Each scenario sets different growth rates, churn rates, and expense ratios. Toggle via a single cell.
12. **Inconsistent Dates:** Some sheets use "Month 1, Month 2..." while others use "Aug 2025, Sep 2025...". Standardize to both: display calendar dates with Month# as secondary reference.
## MODEL ARCHITECTURE (Build From Scratch)
### Sheet Structure (10 sheets)
1. **Cover** — Model title, version, date, scenario selector, key assumptions summary
2. **Inputs** — ALL assumptions in ONE place (single source of truth):
- Pricing (independent per tier)
- Customer acquisition rates by tier and scenario
- Churn rates by tier and scenario
- Expense ratios by stage (pre-revenue, growth, scale)
- Funding schedule with tranche timing
- Growth rate curves (by month range)
- NRR expansion/contraction rates
- Working capital assumptions (AR/AP days)
- Headcount plan by department
3. **Revenue** — Monthly revenue calculations:
- New MRR by tier
- Expansion MRR (upsell/cross-sell)
- Contraction MRR (downgrades)
- Churned MRR
- Net New MRR
- Total MRR → ARR
- Cohort analysis (retention by acquisition month)
- Annual vs monthly contract mix
- NRR tracking (monthly + trailing 12-month)
4. **Customers** — Customer lifecycle:
- New customers by tier
- Churned customers by tier
- Upgraded customers (tier transitions)
- Downgraded customers
- Active customers by tier (end of month)
- Customer mix percentages (validated sum = 100%)
- Cohort retention curves
5. **Expenses** — Cost structure:
- COGS (dynamic, stage-based)
- Personnel by department (headcount x avg salary):
- Engineering/R&D
- Sales & Marketing
- G&A
- Customer Success
- Infrastructure/Cloud (usage-based scaling model)
- Marketing spend (stage-based, not fixed %)
- Sales commissions (variable, quota-based)
- G&A (office, legal, insurance)
- Total OpEx
6. **P&L** — Profit & Loss:
- Revenue (from Revenue sheet)
- COGS
- Gross Profit / Gross Margin %
- Operating Expenses by category
- EBITDA
- Net Income / Loss
- Burn rate (dynamic, from actual expenses)
7. **Cash Flow** — Cash management:
- Operating cash flow (from P&L + working capital changes)
- AR/AP impact
- Deferred revenue
- Funding inflows (with tranche timing)
- CapEx
- Net cash flow
- Cumulative cash position
- Runway (months of cash remaining at current burn)
- Breakeven month identification
8. **Unit Economics** — Per-customer metrics:
- CAC by tier and channel
- LTV by tier (using NRR-adjusted churn)
- LTV:CAC ratio by tier
- Payback period by tier
- Customer Success cost per tier
- Blended unit economics
9. **Dashboard** — Executive summary:
- Key KPIs: ARR, MRR, NRR, Gross Margin, Burn Rate, Runway
- Charts: Revenue growth, customer growth, cash position
- SaaS benchmarks comparison (Rule of 40, Quick Ratio, Magic Number)
- Sensitivity tables (growth rate x churn rate → ARR impact)
- Traffic light indicators (red/yellow/green)
- Scenario comparison side-by-side
10. **Documentation** — Model guide:
- Assumptions and methodology
- Named range index
- Formula audit trail
- Changelog
- Data source references
### Naming Conventions
- All input cells: Use named ranges (e.g., `input_price_individual`, `input_churn_team`)
- All scenario variants: Prefix with scenario name (e.g., `base_growth_m1_m3`)
- All intermediate calculations: Descriptive names (e.g., `calc_gross_churn_individual_m12`)
### Data Validation Rules
- Percentages: 0% ≤ x ≤ 100%
- Prices: > $0
- Customer counts: ≥ 0 (integers)
- Mix percentages: sum = 100% exactly
- Dates: chronological, no gaps
- Growth rates: ≥ 0 (no negative growth — use churn for decline)
- Funding amounts: ≥ $0
### Scenario Framework
Three scenarios toggled by a single cell on the Cover sheet:
| Parameter | Conservative | Base Case | Aggressive |
|-----------|-------------|-----------|------------|
| M1-3 Growth | 1.5x | 2.5x | 4.0x |
| M4-6 Growth | 1.0x | 1.5x | 2.5x |
| M7-12 Growth | 0.5x | 1.0x | 1.5x |
| M13-24 Growth | 0.25x | 0.5x | 1.0x |
| M25+ Growth | 0.10x | 0.25x | 0.5x |
| Individual Churn | 40% | 33.3% | 25% |
| Team Churn | 22% | 16.7% | 12% |
| Enterprise Churn | 15% | 10% | 7% |
| NRR (M24) | 98% | 106% | 118% |
| Expense Efficiency | Low | Medium | High |
### Customer Success Economics Integration
From the CS Economics analysis:
- Support cost by tier: Individual 3.3%, Team 6%, Enterprise 11.5% of revenue
- CSM ratios: Enterprise 1:30, Team 1:150, Individual digital-only
- NRR trajectory: 95% M12 → 106% M24 → 115% M48
- Health score framework informs churn prediction
## OUTPUT FORMAT
Generate the model as a Python script using `openpyxl` that:
1. Creates the complete 10-sheet workbook
2. Populates ALL formulas (not just values — formulas that update dynamically)
3. Applies named ranges to all input cells
4. Adds data validation rules
5. Applies conditional formatting (traffic lights, trend arrows)
6. Implements scenario switching via the Cover sheet selector
7. Formats for print (headers, footers, page breaks)
8. Generates charts (line charts for trends, bar charts for comparisons)
9. Includes the documentation sheet with full model guide
10. Exports companion JSON data file for programmatic access
## VALIDATION CHECKLIST
After building, verify:
- [ ] All 12 structural issues are fixed
- [ ] Scenario switching works (toggle Conservative/Base/Aggressive)
- [ ] NRR is calculated and tracked monthly
- [ ] Customer mix sums to exactly 100%
- [ ] Burn rate updates dynamically from expenses
- [ ] Customer count displays correctly (not 0.7)
- [ ] No duplicate column headers
- [ ] Funding has tranche timing
- [ ] Working capital impacts cash flow
- [ ] Expense ratios vary by stage
- [ ] Churn formula is decomposed and auditable
- [ ] All dates are consistent (calendar + month#)
- [ ] Unit economics are calculated per tier
- [ ] Dashboard KPIs pull from live data
- [ ] Sensitivity tables show impact of key variable changes
- [ ] Documentation sheet is complete
- [ ] Named ranges are applied to all inputs
- [ ] Data validation rules are active
- [ ] Conditional formatting is applied
- [ ] Model balances (Assets = Liabilities + Equity conceptually)
## EXECUTION PHASES
Execute in this order (each phase depends on the previous):
**Phase 1 (N.6.1):** Fix critical data integrity issues in the v2.1 model
**Phase 2 (N.6.2):** Build the new model architecture from scratch
**Phase 3 (N.6.3):** Implement enhanced revenue model
**Phase 4 (N.6.4):** Implement enhanced expense model
**Phase 5 (N.6.5):** Implement cash flow and capital model
**Phase 6 (N.6.6):** Build metrics, analytics, and dashboards
**Phase 7 (N.6.7):** Polish presentation and documentation
**Phase 8 (N.6.8):** Add advanced features (Monte Carlo, valuation, benchmarks)
## REFERENCE DATA
The existing model data is available at:
- JSON export: `analyze-new-artifacts/coditect-financial-model-2026-02-04/CODITECT_Model_Export.json`
- CSV exports: `*_Export.csv` files in the same directory
- Structural issues: `CODITECT_Model_Structural_Issues.md`
- Recommendations: `CODITECT_Improvement_Recommendations.md`
- CS Economics: `CODITECT_Customer_Success_Economics.md`
- Sensitivity model: `CODITECT_Sensitivity_Model.xlsx`
Use the JSON export data as the baseline for all v2.1 values. The v2.0 model
should produce IDENTICAL results to v2.1 for the Base Case scenario before
any enhancements are applied, confirming data integrity.
Usage
To execute this prompt:
- Start a new Claude Code session
- Load the
/financial-modelskill or use/experience financial-planning - Paste or reference this master prompt
- The model will be built phase-by-phase following the N.6 track tasks
- Each phase produces testable output before proceeding
Task Mapping
This prompt maps to Track N.6 tasks (N.6.1 through N.6.8) in TRACK-N-GTM-LAUNCH.md.
Cross-reference document: financial-model-v2-planning-analysis-2026-02-11.md