prebuilt.startup.ideation.models

Startup idea models for the Haive framework.

This module provides comprehensive data models for brainstorming, researching, and evaluating startup ideas using AI agents. These models integrate with the pitch deck models to create a complete startup development pipeline.

The models support: - Idea generation and brainstorming - Market research and validation - Competitive analysis - Problem-solution fit assessment - Business model exploration - Risk and opportunity analysis

Attributes

TResearch
TValidation

Classes

BusinessModelCanvas	Business model canvas for startup ideas.
CompetitorAnalysis	Analysis of a specific competitor.
IdeaBrainstorm	A brainstorming session for generating startup ideas.
IdeaCategory	Categories of startup ideas.
IdeaGenerationRequest	Request model for idea generation.
IdeaGenerationResponse	Response model for idea generation.
IdeaMetrics	Metrics for evaluating startup ideas.
IdeaPortfolio	Portfolio of startup ideas being developed.
IdeaStage	Stage of idea development.
MarketResearch	Market research data for a startup idea.
MarketSize	Market size categories.
ProblemStatement	Defines a problem that the startup idea aims to solve.
RiskAssessment	Risk assessment for a startup idea.
RiskLevel	Risk level assessment.
SolutionConcept	Defines a solution concept for a problem.
StartupIdea	Complete startup idea with all research and validation.
ValidationMethod	Methods for validating ideas.
ValidationResult	Results from idea validation activities.

Functions

create_basic_idea(→ StartupIdea)	Create a basic startup idea from minimal information.
create_problem_from_description(→ ProblemStatement)	Create a problem statement from a simple description.

Module Contents

class prebuilt.startup.ideation.models.BusinessModelCanvas(/, **data: Any)

Bases: pydantic.BaseModel

Business model canvas for startup ideas.

Based on the standard Business Model Canvas framework.

classmethod validate_required_lists(v, info)

Ensure critical lists are not empty.

channels: list[str] = None

cost_structure: list[str] = None

customer_relationships: list[str] = None

customer_segments: list[str] = None

key_activities: list[str] = None

key_partnerships: list[str] = None

key_resources: list[str] = None

metrics: list[str] = None

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

revenue_streams: list[str] = None

unfair_advantage: str | None = None

value_propositions: list[str] = None

class prebuilt.startup.ideation.models.CompetitorAnalysis(/, **data: Any)

Bases: pydantic.BaseModel

Analysis of a specific competitor.

Detailed competitive intelligence for strategic planning.

competitor_name: str = None

customer_satisfaction: float | None = None

description: str = None

employee_count: str | None = None

estimated_revenue: float | None = None

founded_year: int | None = None

funding_raised: float | None = None

key_features: list[str] = None

market_share: float | None = None

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

opportunities_to_differentiate: list[str] = None

potential_threats: list[str] = None

pricing_model: str | None = None

recent_developments: list[str] = None

strengths: list[str] = None

target_customers: list[str] = None

unique_selling_points: list[str] = None

weaknesses: list[str] = None

website: str | None = None

class prebuilt.startup.ideation.models.IdeaBrainstorm(/, **data: Any)

Bases: pydantic.BaseModel

A brainstorming session for generating startup ideas.

Tracks the ideation process and generated concepts.

add_raw_idea(idea: str, agent_id: str | None = None) → None

Add a raw idea to the session.

constraints: list[str] = None

focus_areas: list[str] = None

inspiration_sources: list[str] = None

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

participating_agents: list[str] = None

raw_ideas: list[str] = None

refined_ideas: list[StartupIdea] = None

session_date: datetime.datetime = None

session_id: str = None

session_notes: str = None

techniques_used: list[str] = None

class prebuilt.startup.ideation.models.IdeaCategory

Bases: str, enum.Enum

Categories of startup ideas.

AI_ML = 'ai_ml'

B2B_SAAS = 'b2b_saas'

B2C_APP = 'b2c_app'

BIOTECH = 'biotech'

BLOCKCHAIN = 'blockchain'

DEEPTECH = 'deeptech'

ECOMMERCE = 'ecommerce'

EDTECH = 'edtech'

ENTERTAINMENT = 'entertainment'

FINTECH = 'fintech'

HARDWARE = 'hardware'

HEALTHTECH = 'healthtech'

MARKETPLACE = 'marketplace'

OTHER = 'other'

SOCIAL = 'social'

SUSTAINABILITY = 'sustainability'

class prebuilt.startup.ideation.models.IdeaGenerationRequest(/, **data: Any)

Bases: pydantic.BaseModel

Request model for idea generation.

constraints: list[str] = None

focus_area: str | None = None

inspiration_keywords: list[str] = None

num_ideas: int = None

target_audience: str | None = None

class prebuilt.startup.ideation.models.IdeaGenerationResponse(/, **data: Any)

Bases: pydantic.BaseModel

Response model for idea generation.

ideas: list[dict[str, Any]] = None

market_trends: list[str] = None

rationale: str = None

class prebuilt.startup.ideation.models.IdeaMetrics(/, **data: Any)

Bases: pydantic.BaseModel

Metrics for evaluating startup ideas.

Comprehensive scoring across multiple dimensions.

calculate_scores()

Calculate overall scores.

feasibility: float = None

founder_fit: float = None

investment_readiness: float | None = None

market_opportunity: float = None

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

overall_score: float | None = None

problem_severity: float = None

resource_requirements: float = None

scalability: float = None

solution_uniqueness: float = None

timing: float = None

class prebuilt.startup.ideation.models.IdeaPortfolio(/, **data: Any)

Bases: pydantic.BaseModel

Portfolio of startup ideas being developed.

Manages multiple ideas and tracks their progress.

add_idea(idea: StartupIdea) → bool

Add an idea to the portfolio.

get_ideas_by_stage(stage: IdeaStage) → list[StartupIdea]

Get all ideas at a specific stage.

get_portfolio_summary() → dict[str, Any]

Get summary statistics of the portfolio.

get_top_ideas(n: int = 3) → list[StartupIdea]

Get top N ideas by score.

created_at: datetime.datetime = None

description: str = None

focus_categories: list[IdeaCategory] = None

ideas: list[StartupIdea] = None

max_ideas: int = None

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str = None

portfolio_id: str = None

target_market_size: MarketSize | None = None

updated_at: datetime.datetime = None

class prebuilt.startup.ideation.models.IdeaStage

Bases: str, enum.Enum

Stage of idea development.

LAUNCHED = 'launched'

MVP_READY = 'mvp_ready'

PROTOTYPED = 'prototyped'

RAW_CONCEPT = 'raw_concept'

RESEARCHED = 'researched'

VALIDATED = 'validated'

class prebuilt.startup.ideation.models.MarketResearch(/, **data: Any)

Bases: pydantic.BaseModel

Market research data for a startup idea.

Contains comprehensive market analysis and research findings.

validate_market_sizes()

Ensure market sizes are logical.

confidence_level: float = None

data_sources: list[str] = None

direct_competitors: list[dict[str, Any]] = None

early_adopters: list[str] = None

growth_rate: float | None = None

indirect_competitors: list[dict[str, Any]] = None

market_barriers: list[str] = None

market_drivers: list[str] = None

market_gaps: list[str] = None

market_size_category: MarketSize = None

market_trends: list[str] = None

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

primary_customers: list[dict[str, Any]] = None

research_date: datetime.datetime = None

secondary_customers: list[dict[str, Any]] = None

serviceable_addressable_market: float | None = None

serviceable_obtainable_market: float | None = None

total_addressable_market: float | None = None

class prebuilt.startup.ideation.models.MarketSize

Bases: str, enum.Enum

Market size categories.

LARGE = 'large'

MASSIVE = 'massive'

MEDIUM = 'medium'

NICHE = 'niche'

SMALL = 'small'

class prebuilt.startup.ideation.models.ProblemStatement(/, **data: Any)

Bases: pydantic.BaseModel

Defines a problem that the startup idea aims to solve.

This is the foundation of any good startup idea - a clear problem definition.

affected_users: list[str] = None

current_solutions: list[str] = None

description: str = None

emotional_impact: str | None = None

evidence: list[str] = None

financial_impact: str | None = None

frequency: Literal['daily', 'weekly', 'monthly', 'occasionally', 'rarely'] = None

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

pain_points: list[str] = None

problem_id: str = None

property problem_score: float

Calculate a score for how good this problem is to solve.

quotes: list[str] = None

research_sources: list[str] = None

severity: Literal['critical', 'high', 'medium', 'low'] = None

validation_score: float = None

class prebuilt.startup.ideation.models.RiskAssessment(/, **data: Any)

Bases: pydantic.BaseModel

Risk assessment for a startup idea.

Comprehensive risk analysis across multiple dimensions.

calculate_overall_risk()

Calculate overall risk from individual components.

competitive_risk: RiskLevel = None

financial_risk: RiskLevel = None

identified_risks: list[dict[str, Any]] = None

market_risk: RiskLevel = None

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

overall_risk_level: RiskLevel = None

regulatory_risk: RiskLevel = None

risk_mitigation_strategies: list[str] = None

team_risk: RiskLevel = None

technical_risk: RiskLevel = None

class prebuilt.startup.ideation.models.RiskLevel

Bases: str, enum.Enum

Risk level assessment.

HIGH = 'high'

LOW = 'low'

MEDIUM = 'medium'

VERY_HIGH = 'very_high'

class prebuilt.startup.ideation.models.SolutionConcept(/, **data: Any)

Bases: pydantic.BaseModel

Defines a solution concept for a problem.

This represents how the startup plans to solve the identified problem.

classmethod validate_features(v)

Ensure we have at least 3 key features.

description: str = None

implementation_complexity: RiskLevel = None

key_features: list[str] = None

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str = None

required_technologies: list[str] = None

solution_id: str = None

technical_feasibility: float = None

unique_aspects: list[str] = None

user_journey: list[str] = None

value_proposition: str = None

wow_factor: str | None = None

class prebuilt.startup.ideation.models.StartupIdea(/, **data: Any)

Bases: pydantic.BaseModel

Complete startup idea with all research and validation.

This is the main model that brings together all components of a startup idea.

add_research(research_type: str, data: Any) → None

Add research data to the idea.

add_validation(validation: ValidationResult) → None

Add validation results.

calculate_readiness() → dict[str, Any]

Calculate how ready this idea is for next steps.

get_research_gaps() → list[str]

Identify what research is still needed.

to_pitch_deck_brief() → dict[str, Any]

Convert idea to a brief for pitch deck generation.

This creates a structured brief that can be used by pitch deck agents.

update_timestamp()

Update timestamp on changes.

business_model: BusinessModelCanvas | None = None

category: IdeaCategory = None

competitor_analyses: list[CompetitorAnalysis] = None

contributing_agents: list[str] = None

created_at: datetime.datetime = None

idea_id: str = None

market_research: MarketResearch | None = None

metrics: IdeaMetrics | None = None

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str = None

problem: ProblemStatement = None

research_sources: list[str] = None

risk_assessment: RiskAssessment | None = None

solution: SolutionConcept = None

stage: IdeaStage = None

tagline: str = None

updated_at: datetime.datetime = None

validation_results: list[ValidationResult] = None

class prebuilt.startup.ideation.models.ValidationMethod

Bases: str, enum.Enum

Methods for validating ideas.

COMPETITOR_ANALYSIS = 'competitor_analysis'

CUSTOMER_INTERVIEWS = 'customer_interviews'

EXPERT_CONSULTATION = 'expert_consultation'

LANDING_PAGE = 'landing_page'

MARKET_RESEARCH = 'market_research'

PILOT_PROGRAM = 'pilot_program'

PROTOTYPE_TESTING = 'prototype_testing'

SURVEYS = 'surveys'

class prebuilt.startup.ideation.models.ValidationResult(/, **data: Any)

Bases: pydantic.BaseModel

Results from idea validation activities.

Tracks validation efforts and findings.

confidence_score: float = None

date_conducted: datetime.datetime = None

key_insights: list[str] = None

method: ValidationMethod = None

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

next_steps: list[str] = None

pivot_recommendations: list[str] = None

response_rate: float | None = None

sample_size: int | None = None

summary: str = None

supporting_data: dict[str, Any] = None

validates_hypothesis: bool | None = None

validation_id: str = None

prebuilt.startup.ideation.models.create_basic_idea(name: str, problem: str, solution: str, category: IdeaCategory) → StartupIdea

Create a basic startup idea from minimal information.

prebuilt.startup.ideation.models.create_problem_from_description(description: str, severity: str = 'high') → ProblemStatement

Create a problem statement from a simple description.

prebuilt.startup.ideation.models.TResearch

prebuilt.startup.ideation.models.TValidation