idea-pricing-discovery

name

idea-pricing-discovery

description

Idea Machina Adaptive Pricing Discovery — resumable AI pipeline for optimal pricing of product ideas: 12-step state machine, two routes (strong/weak context), approval gates, tiered output. Use for developing/debugging the pricing pipeline, state-machine/step logic, PricingDiscoveryDialog/PricingResultView, pricing prompt templates (ai_prompt schema), the pricing-discovery edge function, PricingInputSchemaV1/PricingResult types, approval gates, run status/resumption, or AI model bindings for pricing.

Adaptive Pricing Discovery

Resumable AI pipeline: idea context → analyze strength → generate/approve hypotheses → compile schema → produce tiered pricing.

State Machine

Strong Route (context score = strong):
S0_NORMALIZE → S1_ANALYZE → S2A_SYNTHESIZE → S3_COMPILE → S4_IDEATION → COMPLETED

Weak/Medium Route (hypothesis-driven with user feedback):
S0 → S1 → S2B1_VALUE → U1 [user picks] → S2B2_BUYER → U2 [user picks] → S2B3_MARKET → U3 [user picks] → S3 → S4 → COMPLETED

Rejection fallback (at U1/U2):
→ ASK_MINIMUM [user provides text] → S3 → S4 → COMPLETED

Statuses: running | awaiting_user | completed | failed

Pause points: U1 (value hypotheses), U2 (buyer hypotheses), U3 (market anchors), ASK_MINIMUM

Key Files

Edge Function

File	Purpose
supabase/functions/pricing-discovery/index.ts	State machine, orchestrator calls, DB state, resumption

Types

File	Purpose
apps/idea-machina/src/types/pricing.ts	PricingInputSchemaV1, PricingResult, PricingRun, step types

Components

Component	File	Purpose
PricingDiscoveryDialog	components/PricingDiscoveryDialog.tsx	Main dialog: states (idle/loading/u_step/ask_minimum/completed/failed)
PricingOptionCard	components/pricing/PricingOptionCard.tsx	Choice card with checkbox/radio + confidence badge
PricingResultView	components/pricing/PricingResultView.tsx	Final result: tiers, sanity, stress test, warnings

Service Layer

File	Functions
lib/ideas.ts	startPricingDiscovery(ideaId), resumePricingDiscovery(runId, action, selections)
lib/aiContext.ts	pricing_discovery as AiPhaseType
lib/pipelineStatus.ts	"pricing" chip in status bar

Database

Migration	Purpose
20260211000000_pricing_discovery_run_table.sql	ai_prompt.pm_pricing_discovery_runs table + RLS
20260211000100_pricing_discovery_features.sql	8 AI features + quotas + operations
20260211000200_pricing_discovery_prompts.sql	All prompt templates (S0-S4, U1-U3)

DB Table: ai_prompt.pm_pricing_discovery_runs

Column	Type	Notes
id	uuid	PK
idea_id	uuid	FK pm_ideas
current_step	text	S0→S4, U1-U3, ASK_MINIMUM, COMPLETED
status	text	running, awaiting_user, completed, failed
step_results	jsonb	Intermediate outputs per step
approvals	jsonb	User decisions at approval points
pricing_schema	jsonb	PricingInputSchemaV1 (from S3)
pricing_result	jsonb	Final output (from S4)
route	text	strong or medium_weak
total_tokens_used	int
total_cost_usd	numeric
error_message	text	If failed
created_by	uuid	User

AI Features (8 steps)

Feature Key	Step	Model
pricing_s0_normalize	Ingest & normalize	Sonnet 4.5
pricing_s1_analyze	Context strength	Sonnet 4.5
pricing_s2a_synthesize	Full-context (strong)	Sonnet 4.5
pricing_s2b1_value	Value hypotheses	Sonnet 4.5
pricing_s2b2_buyer	Buyer hypotheses	Sonnet 4.5
pricing_s2b3_market	Market anchors	Sonnet 4.5
pricing_s3_compile	Schema compilation	Sonnet 4.5
pricing_s4_ideation	Final pricing	Sonnet 4.5

Edge Function API

Start: { mode: "start", idea_id } → runs S0→S1→(route)→pause or complete

Resume: { mode: "resume", run_id, action, selections? } where action = approve|reject|skip|minimum_answers

Response: { success, run_id, status, current_step, options?, result?, pricing_schema?, usage }

PricingInputSchemaV1 (key fields)

value_definition: { core_value_proposition, value_type, value_levers, perceived_uniqueness }
tier_intents: [{ tier_name, target_segment, positioning, key_features }]
buyer_context: { buyer_type, payer_types, budget_sensitivity, purchase_type }
market_anchors: { reference_products[], psychological_alternatives[] }
stress_test_inputs: { churn_scenario, price_elasticity_guess, volume_assumption }

Common Patterns

Add new step to pipeline

1. Add step constant to edge function step enum
2. Add prompt template migration with ai_prompt.prompts + ai_prompt.prompt_versions
3. Register AI feature + quota in bible_schema.ai_features + ai_plan_quotas
4. Add step logic in edge function state machine (after which step, pause or auto-continue)
5. Update progress percentage mapping in PricingDiscoveryDialog
6. Add types for step output in types/pricing.ts

Modify prompt template

1. Find prompt by feature_key in ai_prompt.prompts
2. Create new prompt_versions row (keep old version for rollback)
3. Update ai_prompt.ai_feature_bindings if changing model/vendor
4. Test via edge function (start a new pricing run)

Modify dialog UI at a U-step

1. PricingDiscoveryDialog.tsx — handles step rendering logic
2. PricingOptionCard.tsx — individual choice card
3. Step data comes from edge function options field in response
4. User selection sent back via resumePricingDiscovery(runId, action, selections)

Debug a stuck/failed run

1. Query: SELECT * FROM ai_prompt.pm_pricing_discovery_runs WHERE id = '<run_id>'
2. Check status, current_step, error_message
3. Check step_results jsonb for partial outputs
4. Edge function logs: check pricing-discovery in Supabase logs

References

• Full schema details: See references/schema.md (pricing runs table, AI features, prompts)
• Parent skill: idea-machina for general Idea Machina development