Too much research
Competitors, pricing, customer complaints, regulations, market signals and alternatives are scattered everywhere.
AI Pre-Build Decision Analyst
AI Startup Idea Validator.
Validate your startup idea with AI-powered market research, competitor analysis, evidence, and assumption testing before you spend months building.
No credit card required · Start with one idea
AI Development Planner
ReportIdea
Security Staff MarketplaceExample project
Overall Viability0 / 100
Evidence Confidence0 / 100
Problem Severity0 / 100
Demand Evidence0 / 100
Technical Feasibility0 / 100
GTM Feasibility0 / 100
Highest-risk assumption
Can venues reliably pay enough to support worker verification + marketplace costs?
Recommended test
Run a 7-day concierge marketplace pilot.
Sources 24Assumptions 8Tests 3
Instead of simply generating a business plan.
Investigate if your assumptions are supported by evidence.
From “I have an idea” to “I know what to test next.”
- 01Idea
- 02Research
- 03Evidence
- 04Risks
- 05Experiment
- 06Decision
- 07Build
- Pipeline: Idea, then Research, Evidence, Risks, Experiment, Decision, Build.
The real problem
Building is easy. Knowing what to build isn't.
AI can generate a business plan in seconds. That doesn’t mean the idea has been validated. Founders need evidence that changes decisions — not another polished document telling them their idea sounds great.
False confidence
A convincing AI answer can still be based on weak evidence, missing information or outdated assumptions.
No clear next step
Knowing there is a problem is different from knowing which experiment could actually disprove your biggest assumption.
AI Startup Idea Validator
Validate Your Startup Idea Before You Build.
Six steps take you from a raw idea to a decision you can defend — with an evidence-backed validation plan generated if it holds.
- 01
Describe your idea
Explain your idea in plain language. Add your budget, skills, team, geography and timeline.
- 02
Research the market
The system investigates competitors, substitutes, customer pain, pricing, trends, regulations and market signals.
- 03
Map the risks
Identify what must be true for the idea to work. Rank assumptions by impact and uncertainty.
- 04
Design the cheapest credible test
Generate 7-day, 30-day and 90-day experiments with measurable pass/fail thresholds.
- 05
Decide what to do next
Keep, Pivot or Drop — backed by evidence, confidence and explicit assumptions.
- 06
Build with a plan
Get your MVP scope, architecture, stack, budget, team requirements and roadmap.
Evidence ledger
Every important claim should have a trail.
AI Development Planner doesn't treat the web as truth. It collects evidence, records provenance and shows what supports a claim, what contradicts it, and how confident the system should be.
Claim under review
“Independent event venues frequently outsource security staffing.”
Evidence
- Industry publicationPublished Aug 12, 2026Supports claim
"Event organizers increasingly outsource overnight and weekend security shifts to staffing firms."
- Community discussionPublished Jul 29, 2026Partially supports
"We tried an agency once — vetting guards took longer than the contract itself."
- Local regulationPublished Jun 18, 2026Adds constraint
"Licensed guards must verify identity and certification before deployment on site."
Example evidence for a demo project. Every claim in a real report carries its source, date and confidence.
Source typeDateConfidenceSupports / refutesEvidence snippetsContradiction flags
The Difference
More than an AI
idea validator.
Most AI tools generate an answer from your idea. AI Development Planner builds an evidence graph first — then connects research, assumptions, experiments, decisions, and execution to the same underlying evidence.
Live graph
Evidence Graph
- 24
- Sources
- 8
- Claims
- 6
- Assumptions
- 3
- Experiments
One shared graph. Every module reads and writes here.
All six modules connect to the same evidence graph. Select a module to see how it feeds the system.
Research once.
Use the evidence everywhere.
Your market research shouldn’t disappear into a PDF. The same evidence can influence your score, assumptions, experiments, MVP, pricing, GTM strategy and future decisions.
Traditional AI validation workflow
- Idea
- AI Prompt
- AI Answer
- Score
- Generic Advice
AI Development Planner workflow
- Idea
- Research Questions
- Evidence
- Claims
- Assumptions
- Experiments
- Decision
- Build Plan
The second workflow isn’t longer because it’s slower — it’s longer because every step adds evidence you can defend.
One project.
One evidence graph.
Every decision connected.
Startup Research Engine
An AI Business Idea Validator Built on Evidence.
Four research modules feed one shared evidence graph, so every finding stays connected to the claims and assumptions it supports.
Market Intelligence
Market signals, demand evidence, geography, pricing, trends, substitutes and regulations.
Competitors
Competitors, substitutes, pricing, reviews, positioning, strengths, weaknesses and gaps.
Customer Insights
Customer pain, complaints, workflows, jobs-to-be-done and desired outcomes.
Trends & Opportunities
Emerging demand, underserved niches, market changes and opportunity themes.
Validation
Don't validate everything. Test what can kill the idea.
The system ranks assumptions by impact and uncertainty, then turns the riskiest assumption into a measurable experiment.
Assumption map
- 01Customers will payImpactHighUncertaintyHighEvidenceLow
- 02Supply can be acquiredImpactHighUncertaintyMediumEvidenceLow
- 03MVP can launch in 30 daysImpactMediumUncertaintyLowEvidenceMedium
Ranked by impact × uncertainty. The top assumption drives the next experiment — that’s the test most likely to change your decision.
Next experiment
Hypothesis
At least 3 venues will pay for a manually fulfilled security staffing service.
Duration
7 days
Budget
$150
Pass:3+ paid bookings
Fail:0 paid bookings
Example experiment for a demo project.
Scoring system
A score is only useful when you can explain it.
Ten separate dimensions instead of one opaque number — and Overall Viability is never presented as a probability of success.
Overall ViabilityWeighted synthesis of every dimension below, shown with its assumptions — not a probability of success.
0
Evidence ConfidenceQuality, freshness, diversity and directness of the evidence behind this report.
0
Problem SeverityHow painful, frequent, expensive or urgent the target problem appears to be.
0
Demand EvidenceStrength of demand signals observed in real behaviour and public sources.
0
Competitive PressureHow difficult it is to win against incumbents and substitutes. Higher means harder.
0
Differentiation PotentialRoom for a meaningful, defensible product advantage.
0
Technical FeasibilityHow achievable the MVP is within your stated stack, budget and team.
0
GTM FeasibilityHow reachable the target customer is given channels, geography and CAC assumptions.
0
Economic FeasibilityWhether expected price, delivery cost and operating costs can support the model.
0
Regulatory / Trust RiskExposure to legal, privacy, identity, payments or operational risk. Higher means riskier.
0
Example scores for a demo project. Scores are explainable assessments of evidence — not predictions.
Founder constraints
The same idea can be a different business for a different founder.
A $2,000 solo founder does not have the same constraints as a $100,000 funded team. Your recommendations adapt to the reality you actually have.
Founder profile
- Budget
- $2,000 – $5,000
- Team
- Solo founder
- Technical skill
- Intermediate
- Time
- 15 hrs/week
- Geography
- Pakistan
- Launch target
- 90 days
Recommended approach
- MVP complexity
- Lean
- Architecture
- Next.js + Supabase
- AI
- Pay-as-you-go
- Validation
- Concierge test first
- Estimated MVP scope
- 6 core features
The same idea entered with a $100,000 funded team would return a larger MVP scope, a different architecture ceiling and paid-channel experiments instead of concierge tests.
Build planner
When the evidence says build, know exactly how.
Validation is half the product. The other half turns a 'Keep' decision into a concrete MVP scope, architecture, budget and roadmap.
Core features
01Authentication
02Idea Workspace
03Research Engine
04Evidence Ledger
05Validation Experiments
06Decision Report
Lean scope sized to your stated budget, skills and timeline.
Report
One research cycle. A complete decision brief.
Fifteen sections from verdict to sources — every claim traceable, every recommendation explainable, every version kept.
Decision Report · v3
KeepVerdict
Keep — narrow the wedge
Demand evidence is strong for venue-side pain, but willingness to pay is unproven. Run the concierge pilot before committing to marketplace automation[2]. Licensing requirements[3] add a hard constraint to worker onboarding.
Citations
[1] Industry publication — Aug 12, 2026
[2] Community discussion — Jul 29, 2026
[3] Local regulation — Jun 18, 2026
Decision history:v1 Pivotv2 Pivotv3 Keep
Export toPDFPRDNotionLinearJiraGitHubCursor-ready plan
The evidence graph
Every module reads from the same evidence graph.
Research modules don't produce isolated documents — they add nodes to one graph that the decision engine, experiments and build plan all share.
- 01Market
- 02Customers
- 03Competitors
- 04Evidence Graph
- 05Assumptions
- 06Experiments
- 07Decision
- 08Build Plan
- 09GTM
Designed for
Built for people who repeatedly decide what to build.
No customer logos, no testimonials — just the workflows this product is designed to serve.
Indie Hackers
Turn a backlog of ideas into evidence-backed experiments.
Technical Founders
Combine technical feasibility with actual market evidence.
Freelance Product Developers
Accelerate discovery, research and proposal preparation.
Agencies
Create structured research and product discovery packages.
Startup Studios
Evaluate ideas consistently across markets and constraints.
Accelerators
Create a repeatable research and validation workflow for cohorts.
FAQ
Questions founders actually ask.
An AI startup idea validator is a decision system that uses artificial intelligence to automatically gather market research, analyze competitors, and map out critical assumptions. It helps founders test what must be true for their startup to succeed before writing any code.
It accepts a plain-language description of your startup idea and immediately investigates market demand, pricing signals, and competitor gaps. It then generates evidence-backed reports and validation experiments you can run to prove or disprove your core business model.
To validate a startup idea, you must identify your riskiest assumptions and test them with real users. AI Development Planner automates the discovery of these risks and designs the cheapest, fastest experiments—like a concierge test or landing page—to gain real evidence.
Before building, you should research market severity, existing substitutes, customer pain points, technical feasibility, and go-to-market channels. Gathering real-world evidence around these topics prevents you from building something nobody wants.
AI cannot predict guaranteed success, but it can validate a business idea by structuring the necessary research. It automatically parses vast amounts of public data to find competitors, identify missing evidence, and build a measurable testing framework for human validation.
A business plan generator creates a persuasive document assuming your idea is already correct. A business idea validator operates explicitly on evidence and skepticism, mapping out exactly why your idea might fail and what you must test to prove it right.
No. The product is designed around evidence collection, assumption mapping and experiments rather than simply generating a score from an idea description.
No. It does not claim to predict future startup success. It produces a viability assessment and evidence-confidence measure based on available evidence and stated assumptions.
The research system collects information from relevant public sources and records provenance, dates and confidence.
Yes. Budget, team size, technical skills, geography, timeline and other constraints influence the analysis.
Yes. The platform can turn research into MVP scope, architecture, technology choices, budget, roles and roadmap.
Yes. The evidence graph and reports are designed to support research refreshes and version history.
The product is designed with tenant isolation, server-side provider credentials, access controls and project deletion/export capabilities. Specific security and compliance commitments depend on the deployed infrastructure and plan.
Don’t spend three months building a six-hour mistake.
Start with the idea. Find the evidence. Test the riskiest assumption. Then decide what to build.