Startup Idea Validation

Systematic validation for testing ideas before building: define hypotheses, collect evidence, score the opportunity, and make a decision you can defend.

Operating Principles (2026)

• Prefer decisions over inventories: each dimension ends with GO / CONDITIONAL / PIVOT / NO-GO and a next action.
• Separate evidence quality from confidence: weak evidence cannot justify a high score.
• Pre-register thresholds and stop rules before running experiments (avoid moving goalposts).
• Validate willingness-to-pay and time-to-value early (price is part of the product).
• Calibrate thresholds to the target outcome (venture-scale vs cash-flow business) and business model (B2B SaaS, B2C, marketplace, services).
• Stay safe and ethical: no misrepresentation, respect ToS, and handle customer data with minimization and retention limits.

Intake Checklist (Ask First)

• One-sentence idea + target user + job-to-be-done
• Business model: B2B/B2C, SaaS/usage-based/marketplace/services, ACV/ARPU range
• Geography, constraints (regulated domain, procurement/security requirements, data access)
• Target outcome: venture-scale, profitable small business, or thesis-driven R&D
• Current evidence: interviews, pilots, pre-sales, traffic, competitor list, pricing assumptions

Choose the Right Output

If the user asks…	Produce…	Use…
“Validate this idea” / “Is this worth building?”	9-dimension scorecard + verdict	validation-scorecard.md, go-no-go-decision.md
“What’s the riskiest assumption?”	RAT + test plan	riskiest-assumption-test.md, validation-experiment-planner.md
“Test my hypothesis”	Hypothesis canvas + experiment design	hypothesis-canvas.md, hypothesis-testing-guide.md
“Market size for X”	TAM/SAM/SOM sizing + assumptions table	market-sizing-worksheet.md, market-sizing-patterns.md
“Can this be profitable / what’s my runway?”	Unit economics + runway + scenarios	financial-modeling-calculator.md
“Should I build X or Y?”	Comparative scorecard + decision memo	validation-scorecard.md, go-no-go-decision.md

Workflow

1. Clarify the target outcome and business model; set default thresholds accordingly.
2. Identify the RAT (the assumption that kills the business if wrong).
3. Plan the validation ladder: interviews -> smoke test -> concierge/WoZ -> paid pilot.
4. Run the cheapest falsifiable test first; pre-register PASS/FAIL thresholds and stop rules.
5. Score all 9 dimensions using evidence; downgrade scores when evidence is weak.
6. Produce a decision memo: verdict, why, what would change the decision, and the next smallest reversible step.

9-Dimension Scorecard

Dimension	Weight	What it measures
Problem severity	15%	Urgency, cost of inaction, current workarounds
Market size	12%	Sufficient demand for the target outcome
Market timing	10%	Clear “why now” and tailwinds
Competitive moat	12%	Defensibility over time
Unit economics	15%	Profit path (incl. payback and margins)
Founder-market fit	8%	Access, expertise, and execution capability
Technical feasibility	10%	Buildability, dependencies, constraints
GTM clarity	10%	ICP, channels, motion, first customers
Risk profile	8%	What can kill it and likelihood

Verdict thresholds (default):

• 80–100: GO
• 60–79: CONDITIONAL (validate RAT first)
• 40–59: PIVOT
• <40: NO-GO

Deep scoring rubrics and calibration live in validation-methodology.md.

Evidence Rules

• Strong evidence is behavioral commitment with cost (time, money, switching, access); weak evidence is opinions and hypotheticals.
• Triangulate important claims across at least two sources (especially market sizing and competitor state).
• Keep an evidence trail: link + capture month; separate “fact” vs “assumption”.

Validation Ladder (Default)

Step	Goal	Strong signal
Interviews	Validate the problem and context	Repeated pain with real workarounds and spend
Smoke test	Validate demand	Qualified conversion with price shown
Concierge/WoZ	Validate workflow value	Users complete the job and return
Paid pilot	Validate willingness-to-pay	Paid, renewed, or expanded

AI / Automation Notes (2026)

If the idea depends on AI (agents, copilots, automation), validate these explicitly:

• Data rights and access: can you legally and reliably access required data?
• Reliability: define success metrics, failure modes, and human fallback; validate on real workflows.
• Cost-to-serve: model inference + retrieval + human-in-the-loop costs in assets/financial-modeling-calculator.md.

See hypothesis-testing-guide.md for AI-specific experiment patterns.

Integration Points

Receives From

• startup-review-mining - Pain point evidence
• startup-trend-prediction - Market timing inputs
• startup-competitive-analysis - Competitor landscape

Feeds Into

• router-startup - Startup decision routing
• product-management - Validated requirements and roadmap inputs
• startup-business-models - Monetization and packaging decisions

Resources

Resource	Purpose
validation-methodology.md	Scoring rubrics and calibration
hypothesis-testing-guide.md	Experiment design and RAT workflows
market-sizing-patterns.md	TAM/SAM/SOM methods and pitfalls
moat-assessment-framework.md	Defensibility analysis

Templates

Template	Purpose
validation-scorecard.md	Full 9-dimension scoring
go-no-go-decision.md	Decision memo format
hypothesis-canvas.md	Hypothesis definition
validation-experiment-planner.md	Experiment planning + thresholds
riskiest-assumption-test.md	RAT identification and test design
market-sizing-worksheet.md	Sizing worksheet
financial-modeling-calculator.md	Runway + scenarios + unit economics

Data

File	Purpose
sources.json	Curated validation resources