Price → Demand → Usage → AI cost → Margin → Experiment → Decision
PricingOS is a product-management portfolio project for answering one practical question:
Given customers, usage, costs and a proposed price change, what should we actually charge — and what happens if we change it?
- Pricing strategy across subscription, usage and hybrid models
- Revenue, ARPU, ARR and gross-margin simulation
- AI cost-to-serve and margin-compression analysis
- Customer segmentation by willingness-to-pay and usage
- Pricing experiments with explicit guardrails
- Executive-ready decision memos rather than calculator-only output
AI products make pricing harder because customer value and delivery cost can both vary with usage. PricingOS treats monetization as a product decision: model the economics, expose the tradeoffs, then validate the recommendation with a controlled experiment.
- Pricing Studio — configure price, customer base, AI allowance and cost assumptions.
- Revenue Simulator — translate demand assumptions into revenue and margin outcomes.
- Segments — identify where willingness-to-pay and cost-to-serve diverge.
- Experiment Lab — define primary metrics and guardrails before launch.
- Decision Memo — turn the model into an executive recommendation.
The synthetic workspace models a move from $49 → $59/month + 1,000 AI credits. The UI intentionally treats this as a scenario, not a financial forecast. The recommendation is to test before rollout.
HTML • CSS • Vanilla JavaScript • Render
- Scenario comparison and saved cases
- Cohort-level elasticity modeling
- Monte Carlo uncertainty ranges
- CSV import for real pricing data
- Pricing recommendation explainability
- AI-assisted decision memo generation
All numbers in the default workspace are synthetic and intended to demonstrate product reasoning, not represent a real company's financial forecast.