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Seeded intent and research
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INTENT.md
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INTENT.md
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# INTENT.md
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## Project
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`adaptive-pricing`
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## Purpose
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`adaptive-pricing` exists to provide a practical framework for defining, evaluating, adapting, and implementing pricing models for digital products, services, platforms, and marketplace offerings.
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The project starts from the assumption that pricing is not merely an administrative billing concern. Pricing is a central mechanism for discovering product-market fit, expressing value, managing risk, recovering cost, shaping customer behavior, and optimizing long-term business success.
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The project therefore treats pricing as an adaptive product capability.
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## Core Intent
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The intent of `adaptive-pricing` is to build an **auto-regulating market value exploring pricing engine**.
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The engine should help sellers define pricing models that can evolve across the product lifecycle while allowing customers to choose or tune pricing-model parameters within controlled boundaries.
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The system should support both:
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1. **Seller-side optimization**
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Helping vendors, builders, and product owners select pricing models that improve business success, customer lifetime value, contribution margin, adoption, retention, and sustainable growth.
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2. **Customer-side preference tuning**
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Allowing customers to adjust non-fixed pricing parameters to better match their own preferences, such as lower upfront cost, lower usage cost, stronger predictability, higher flexibility, or better volume economics.
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Customer tuning must not create arbitrary discounts. Instead, tunable parameters are solved against seller-defined business goals and boundary conditions.
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## Problem Space
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Pricing strategy requires understanding at least three economic dimensions:
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### Cost Floor
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The minimum economically viable price boundary derived from fixed costs, variable costs, operating cost, support cost, payment-provider fees, capital requirements, risk, and desired margin.
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The cost floor answers:
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> What must we charge to avoid building an unsustainable offering?
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### Value Range
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The range of prices that can reasonably be justified by the value created for different customer segments, use cases, maturity levels, and usage patterns.
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The value range answers:
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> What could this be worth to customers under different conditions?
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### Market Competition
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The externally visible pricing context created by competing products, substitutes, alternative workflows, internal build options, open-source alternatives, manual workarounds, and customer budget expectations.
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Market competition answers:
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> What price can we ask while still being attractive compared with available alternatives?
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`adaptive-pricing` should help sellers reason across these dimensions instead of relying on static guesses.
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## Product Lifecycle Awareness
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Pricing should adapt to the phase of the product lifecycle.
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The project should support pricing strategy across the following lifecycle phases:
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1. **Exploration**
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Pricing is used to learn about willingness to pay, customer value perception, cost structure, and viable business models.
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2. **Introduction**
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Pricing supports market entry, early adoption, pilot customers, controlled risk, and customer education.
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3. **Growth**
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Pricing supports scalable acquisition, segmentation, expansion revenue, repeatability, and business-model validation.
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4. **Maturity**
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Pricing supports margin optimization, packaging, predictable revenue, customer retention, and operational efficiency.
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5. **Saturation**
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Pricing supports differentiation, bundling, portfolio logic, retention, efficiency, and defense against competition.
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6. **Decline**
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Pricing supports harvesting, migration, transition offers, reduced investment, long-tail maintenance, or graceful shutdown.
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The engine should not assume that one pricing model is appropriate for all phases.
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## Pricing Model Framework
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The project should provide a structured way to define pricing models.
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A pricing model may include fixed and non-fixed parameters such as:
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* base price
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* subscription period
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* setup fee
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* included usage
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* usage unit
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* usage price
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* usage tiers
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* minimum commitment
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* contract duration
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* volume discounts
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* prepayment discount
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* risk premium
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* support level
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* service-level commitment
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* cancellation flexibility
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* onboarding effort
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* customer segment
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* feature package
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* payment terms
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* renewal conditions
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Some parameters may be fixed by the seller. Others may be tunable by the customer.
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The framework should distinguish clearly between:
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* fixed parameters
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* seller-controlled parameters
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* customer-tunable parameters
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* calculated parameters
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* constrained parameters
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* payment-provider implementation parameters
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## Customer-Tuned Pricing
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Customers should be able to tune non-fixed pricing-model parameters according to their preferences.
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Examples:
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* lower monthly base price in exchange for higher usage price
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* lower usage price in exchange for higher minimum commitment
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* lower risk for the seller through upfront payment
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* higher flexibility in exchange for a higher price
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* volume discounts in exchange for guaranteed minimum turnover
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* longer commitment in exchange for more favorable unit economics
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* lower onboarding fee in exchange for higher monthly minimum
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* stronger predictability through capped usage or prepaid packages
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When a customer adjusts one or more pricing parameters, the engine should solve the remaining non-fixed parameters so that the seller’s expected average comparable customer lifetime value improves by a required percentage compared with the most favorable predefined pricing model available to that customer.
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This means customer tuning is allowed, but only inside an economically valid solution space.
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## Comparable Customer Lifetime Value
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The engine should use a seller-side comparison metric tentatively called:
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`average_comparable_customer_lifetime_value`
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This metric estimates the expected lifetime value of a customer under a given pricing configuration, compared against similar customers, customer segments, usage expectations, risk classes, and contract conditions.
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Customer-tuned models should be evaluated against a reference model:
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`most_favorable_predefined_model`
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A tuned model is valid only if:
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```text
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average_comparable_customer_lifetime_value(tuned_model)
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>= average_comparable_customer_lifetime_value(most_favorable_predefined_model)
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× required_improvement_factor
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```
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The required improvement factor is seller-configurable.
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This prevents tuning from becoming unmanaged discounting.
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## Boundary Conditions
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Customer-tuned pricing must be evaluated against explicit boundary conditions.
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Boundary conditions may include:
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* cost floor
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* minimum margin
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* payment-provider fees
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* expected usage variance
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* support effort
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* onboarding effort
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* contract duration
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* churn risk
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* default risk
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* upfront investment risk
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* capital lockup
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* implementation effort
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* operational capacity
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* service-level risk
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* abuse risk
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* compliance constraints
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* tax and accounting constraints
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* customer segment eligibility
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* sales approval thresholds
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* minimum turnover commitment
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* maximum discount exposure
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The engine should make boundary conditions inspectable and testable.
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## Volume Discounts
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If a pricing model allows volume discounts, those discounts should not be based on optimistic usage assumptions alone.
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Volume discounts must be tied to enforceable or economically meaningful commitments, such as:
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* minimum monthly turnover
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* minimum annual turnover
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* prepaid usage packages
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* committed usage bands
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* longer contract duration
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* take-or-pay structures
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* reduced cancellation flexibility
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* customer-funded onboarding
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* guaranteed minimum platform fee
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The engine should reject discount structures that reduce seller economics without an offsetting commitment or risk reduction.
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## Automation Scope
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`adaptive-pricing` should not remain a theoretical pricing strategy document.
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The project should provide practical implementation components for:
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* pricing-model definition
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* pricing-model validation
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* pricing simulation
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* customer-specific price solving
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* price recommendation
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* lifecycle-aware pricing strategy
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* pricing health checks
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* payment-provider synchronization
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* customer-facing pricing configuration
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* seller-facing pricing governance
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* auditability of pricing decisions
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The engine should be usable by products and platforms that need to create, update, and manage pricing models automatically.
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## Payment Provider Integration
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A core implementation goal is automated integration with payment providers such as Stripe.
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The system should be able to translate internal pricing-model definitions into payment-provider artifacts without requiring manual configuration in the provider dashboard.
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For Stripe this may include automated creation and maintenance of:
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* products
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* prices
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* recurring subscriptions
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* metered usage prices
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* usage records
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* tiers
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* coupons or discounts where appropriate
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* promotion structures where appropriate
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* customer-specific subscription configurations
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* invoices
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* billing periods
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* tax-relevant metadata
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* product and price metadata
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* lifecycle states
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The internal pricing model should remain the source of truth.
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Payment providers are execution backends, not the primary modeling layer.
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## Design Principles
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### Pricing as Discovery
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The system should support learning. Especially in early product phases, pricing is a discovery mechanism, not merely a revenue mechanism.
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### Cost as Boundary, Not Strategy
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Cost floor matters, but pricing should not be reduced to cost-plus calculation. The engine should help explore market value above the cost floor.
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### Value Before Mechanics
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Pricing mechanics should serve value communication, adoption, sustainability, and customer success.
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### Customer Choice Within Seller Safety
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Customers may tune pricing parameters, but only within a bounded solution space that protects seller economics.
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### No Hidden Manual State
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Pricing models should be represented explicitly in code and data. Payment-provider state should be generated, synchronized, and auditable.
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### Lifecycle Awareness
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The system should expect pricing to change as the offering moves from exploration to introduction, growth, maturity, saturation, and decline.
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### Explainability
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The engine should explain why a pricing configuration is valid, invalid, recommended, risky, or strategically useful.
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### Composability
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Pricing models should be reusable across products, vendors, tenants, customer segments, and platforms.
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## Intended Users
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The primary users of `adaptive-pricing` are:
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* builders
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* vendors
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* product owners
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* SaaS operators
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* marketplace operators
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* platform owners
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* pricing strategists
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* finance operators
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* customer success teams
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* sales teams
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* AI agents assisting with product and pricing decisions
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The framework should support both human decision-making and automated agent-driven workflows.
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## Initial Deliverables
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The project should initially produce:
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1. A canonical pricing model definition format
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2. A pricing strategy vocabulary
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3. A pricing lifecycle model
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4. A model validator
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5. A customer-tuning solver concept
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6. A comparable customer lifetime value calculation model
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7. Boundary-condition definitions
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8. A simulation interface
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9. A Stripe integration design
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10. A minimal implementation path for automated price creation
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## Non-Goals
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The project is not intended to become only a theoretical pricing research repository.
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It is also not intended to hard-code one universal pricing model.
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It should not assume that all products are SaaS subscriptions, all services are usage-based, or all customers optimize for the same thing.
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It should not treat payment-provider configuration as the source of truth.
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## Success Criteria
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`adaptive-pricing` is successful when it enables a product team or builder to:
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* define pricing models in a structured format
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* reason about cost floor, value range, and market competition
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* adapt pricing to product lifecycle phase
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* expose safe pricing choices to customers
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* evaluate customer-tuned pricing against seller economics
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* enforce boundary conditions
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* connect pricing definitions to Stripe or similar payment providers
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* create and update payment-provider pricing artifacts automatically
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* simulate expected economic outcomes before activation
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* explain why a pricing model is valid, invalid, risky, or recommended
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## Strategic Direction
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`adaptive-pricing` should become the pricing capability layer for Coulomb offerings and related product platforms.
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It should allow pricing to become adaptive, inspectable, customer-aware, seller-safe, and operationally executable.
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The long-term ambition is to support pricing as a dynamic control system for product-market fit, customer value exploration, sustainable revenue, and market learning.
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