How the score is made
Rules & Weights
Scores are deterministic. A language model never sets a number. The same inputs always produce the same scores.
Last updated · September 28, 2026
The contract
Numbers come from the rule engine, not from AI
You describe the idea in one or two sentences and answer five structured questions. The engine starts each of seven dimensions at a baseline, applies every matching rule as a signed adjustment, then clamps each dimension to a 0–100 integer. Optional AI only polishes the prose around those numbers. It cannot raise a score, lower a score, or change the verdict.
Seven dimensions
7 dimensions, weighted on purpose
The overall startup score is a weighted sum of these seven. Validation carries the most weight because the engine is built on a simple opinion: the cheapest way to kill a bad idea is to check it. Build ease carries the least, because writing software got cheaper; finding out whether anyone wants it did not.
Validation
25%(0.25)How cheaply and specifically can this be tested before you build? A named audience and an idea that already includes numbers or a waitlist score higher. Hardware, clinical, or licensed proof scores lower because the first honest experiment is expensive — not because the idea is weak.
Baseline before rules apply: 50
Distribution
20%(0.20)Is there a realistic path to the first customers? Community and niche channels score higher for a solo founder. Paid ads, cold outreach, and arriving with no chosen channel score lower.
Baseline before rules apply: 40
Market demand
18%(0.18)Is the customer specific enough to find, and is the problem described concretely enough to check? Vague audiences — “everyone”, “users” — and one-line ideas lose points here because nothing in the input can be tested yet.
Baseline before rules apply: 45
Monetization
12%(0.12)Is there a revenue model you can test at a real price? Recurring subscription scores highest. Marketplace is treated as neutral because of the chicken-and-egg problem. Arriving without a chosen model is a heavy penalty.
Baseline before rules apply: 45
Competition
10%(0.10)Is the category already crowded with near-identical products, and is the audience narrow enough to defend? Ideas that match common saturated-category patterns lose points here.
Baseline before rules apply: 50
Founder fit
10%(0.10)Does your stated background match what shipping this would require? An experienced developer gains the most. A non-technical founder loses a small amount — hiring or learning is a real cost, not a disqualification.
Baseline before rules apply: 50
Build ease
5%(0.05)How much work stands between the idea and something a customer can react to? Technical background is the only input that moves this dimension, and it carries the smallest weight of the seven.
Baseline before rules apply: 55
Two summary numbers
Overall and opportunity are different formulas on purpose
Overall score
A weighted sum of the seven dimensions using the weights above. This number carries the engine’s priorities: validation first, then distribution, then market.
Opportunity score
The plain average of the same 7 dimensions, each counting equally. It ignores the engine’s weighting.
When the two diverge, the gap is the signal. Scoring higher on opportunity than overall usually means you are strong where the engine assigns little weight — often build and founder fit — and thin where it assigns a lot, most often validation and distribution.
Decision bands
How scores become GO, CAUTION, or NO-GO
The verdict is a threshold rule. Each half does separate work: the overall number has to clear a bar, and no single dimension is allowed to sit in a danger zone. An idea can average comfortably and still be rejected because one dimension is critically weak — the classic shape is a plausible product with no path to anyone.
GO
Overall score of at least 75, and no single dimension below 50.
CAUTION
Overall score of at least 55, and no single dimension below 30.
NO-GO
Everything else. Either the overall score is below the caution bar, or one dimension is in the danger zone.
The rule catalog
34 rules, grouped by what they read
Each rule has an id, a human reason, a signed adjustment, and the dimensions it touches. When a rule matches your inputs, that adjustment is added to each listed dimension. Zero is still a rule: it records that the engine looked and took no side.
Some rules also look at the free-text idea and audience fields for patterns — a named professional role, a waitlist, or keywords that often mark a crowded category. This page lists the reason and the signed adjustment, not the pattern source.
Monetization model(6)
mon.subscription+10Subscription is a proven, recurring model.
Affects: Monetization, Competition
mon.one_time+4One-time purchase is viable but limits LTV.
Affects: Monetization
mon.freemium+5Freemium works when paired with a strong acquisition channel.
Affects: Monetization, Distribution
mon.marketplace0Marketplace has chicken-and-egg risk; neutral here.
Affects: Monetization
mon.ads+2Ad-supported is hard to scale without massive traffic.
Affects: Monetization
mon.not_sure-8No chosen monetization signals weak business-model thinking.
Affects: Monetization
Acquisition channel(8)
acq.seo+8SEO compounds and is defensible.
Affects: Distribution, Competition
acq.community+7Community-led growth is defensible and high-trust.
Affects: Distribution
acq.reddit+6Reddit is great for niche, high-intent audiences.
Affects: Distribution
acq.product_hunt+5Product Hunt gives a spike but rarely sustained growth.
Affects: Distribution
acq.social_media+3Social media is high-effort, low-conversion by default.
Affects: Distribution
acq.paid_ads-4Paid ads are saturated and expensive for new products.
Affects: Distribution, Monetization
acq.cold_outreach-5Cold outreach does not scale and burns out quickly.
Affects: Distribution
acq.not_sure-6No chosen acquisition channel is a major red flag.
Affects: Distribution
Technical background(4)
bg.experienced+5An experienced developer can ship the MVP solo.
Affects: Founder fit, Build ease
bg.intermediate+2An intermediate developer can ship with some help.
Affects: Founder fit, Build ease
bg.beginner0Beginner background is fine; small build penalty.
Affects: Founder fit
bg.non_technical-2Non-technical founder will need to hire or learn.
Affects: Founder fit
Idea text(5)
idea.detailed+3Longer, more specific ideas tend to be better thought through.
Affects: Market demand
idea.vague-4Very short ideas signal shallow problem understanding.
Affects: Market demand
idea.ai_trend+3AI + a specific professional audience signals real demand in 2026.
Affects: Market demand
idea.saturated-6Common keywords suggest a saturated category.
Affects: Competition
idea.local_business-3Local business model has limited TAM and is hard to scale beyond a geography.
Affects: Distribution
Target audience(6)
audience.b2b+5Clear B2B or professional audience.
Affects: Market demand
audience.b2b_monetization+3B2B/professional audiences pay more reliably.
Affects: Monetization
audience.vague-5Audience is too generic.
Affects: Market demand
audience.vague_competition-4Generic audience faces heavy competition.
Affects: Competition
audience.icp_clear+4Target audience is specific (named role, industry, revenue, or job-to-be-done).
Affects: Market demand, Validation
audience.icp_vague-3Target audience is too broad to validate cheaply.
Affects: Market demand, Validation
Validation cost(2)
val.cost_high-5Validation requires regulated or physical validation (hardware, FDA, healthcare).
Affects: Validation, Competition
val.cost_low+3Validation can be done cheaply via a landing page or waitlist.
Affects: Validation
Distribution difficulty(1)
distribution.difficulty_high-4Distribution requires enterprise sales, regulated channels, or offline relationships.
Affects: Distribution
Evidence in the idea(2)
evidence.dense+3Idea includes specific numbers, data, or pilot evidence.
Affects: Market demand, Validation
evidence.sparse-3Idea is short or generic with no concrete evidence.
Affects: Market demand, Validation
Common questions
Rules & weights FAQ
Can AI change my score?
No. Optional AI only rewrites the explanation. Scores, weights, fired rules, and the GO / CAUTION / NO-GO verdict all come from the deterministic engine. Turn the model off and the numbers stay the same.
Why is validation weighted highest?
Because the engine is built to punish untested ideas more than unbuilt ones. Validation is 25% of the overall score — the largest share. Build ease is 5%. That ordering is an opinion, not a law of nature. Every dimension is still shown separately, so you can read the breakdown on your own terms.
Why isn’t this a success prediction?
The engine has never met your customers. A score measures how completely and specifically your inputs address a dimension. It is not a probability that the startup will work. A high score means the engine found no stated reason not to proceed, given what you typed. A low score names the cheapest thing to go test.
Why do overall and opportunity differ?
They are different formulas. Overall is a weighted sum, so validation and distribution pull harder. Opportunity is a plain average of the seven dimensions. The gap tells you whether you are strong on the cheap-to-build side and weak on the must-validate side, or the other way around.
Do the same inputs always produce the same scores?
Yes. The engine is a pure function: same idea, audience, monetization, channel, and background → same seven dimensions, same overall, same opportunity, same verdict. That is the point of not letting a language model pick the numbers.
Run the same engine on your idea
One or two sentences plus five questions. The numbers on this page are the numbers you will get.