Every verdict on this site comes out of the same pipeline, on the same harsh scale, with the evidence printed underneath. This page is the full provenance — models, data, calibration, and where we've been wrong.
Your idea or URL is parsed into a product description, a category, and up to four competitor-search keywords. Cheap, mechanical, fast.
The keywords are matched against the App Radar corpus — a living database our collectors refresh daily: 1,400+ tracked products with verified MRR time series, AppSumo trajectories, funding events, marketplace listings (what similar businesses actually sell for), and Reddit pain threads. Word-level matching with a ≥2-hit gate so "AI tracker" doesn't match every tracker.
The evidence pack is scored on MJ DeMarco's CENTS framework (Control, Entry, Need, Time, Scale) by Claude Opus 5 — the same referee for every idea, including the ones we generate ourselves. The rubric is harsh by design: no market evidence caps Need at 5; a well-loved incumbent caps Entry low; the weakest commandment is always named. Inspired by MJ DeMarco's CENTS framework.
The score becomes a stamp — FULL AHEAD, DEAD SLOW, or STOP — with kill switches, the pace test, and the cheapest next step. Re-spins (business-model variations engineered to beat the original) are generated by Sonnet, chosen in calibration tests specifically because it doesn't inflate its own scores.
Every idea is scored on five commandments, 0–10 each, by the same referee with the same evidence standard. The composite is the plain mean — no weighting games — and the weakest commandment is always named in the verdict, because a business fails at its weakest link, not its average.
| C — Control | Ownership of the customer relationship. Gatekeepers (app stores, search algorithms, one marketplace, one ad platform) that can tax or kill you score this down. Direct relationships score it up. |
| E — Entry | Barriers to imitation. High is good once you're in. A well-loved incumbent (4.5★+ with loyal users) is a barrier working against you, and caps this low — ratings alone can only soften that cap when the incumbent's revenue is provably collapsing (see §2). |
| N — Need | Demand proven with money: verified rival revenue, funding flowing into the space, exit multiples, complaint volume. Ideas with no market evidence cap at 5 — "people should want this" is not demand. |
| T — Time | Detachment of income from hours. Software that sells while you sleep scores high; services, marketplaces needing constant operation, and content treadmills score low. |
| S — Scale | Reach without linear cost. Marginal cost per customer near zero, a reachable market big enough to matter, no geographic or regulatory ceiling that stops growth at a wall. |
Inspired by MJ DeMarco's CENTS framework (attribution only — the rubric, caps and wording here are ours).
A rating tells you how loved a rival is. It doesn't tell you whether that love is durable. For every product in the corpus with a verified revenue series, we compute a moat trend — the direction of its economic moat, from its own numbers:
| ↑ widening | Revenue compounding ≥ +10%/mo (monthlyized). The incumbent is getting stronger — attacking it gets harder every month you wait. |
| → stable | Holding between −5% and +10%/mo. A fortress, but a static one. |
| ↓ narrowing | Declining −5% to −10%/mo. Users still love it; the money is quietly leaving. Possible opening. |
| ↓↓ eroding | Collapsing faster than −10%/mo. A well-loved product whose economics are dying — the classic disruption seam. |
The rules that keep us honest:
Where you'll see it: trend badges on rival evidence in every report. Where it works behind the glass: our daily-idea engine closes lanes led by widening incumbents and discounts lanes whose leader is eroding.
| Verified MRR | Independently tracked revenue (TrustMRR-class sources) — the strongest signal we have. Not self-reported. |
| Ratings / upvotes / reviews | Platform-reported (App Store, Product Hunt, AppSumo). Useful, gameable — weighted accordingly. |
| Funding events | Public announcements cross-checked across feeds; company-level dedup. |
| Marketplace multiples | Asking prices and sold outcomes from Acquire.com — what buyers actually pay for profit. |
| Reddit pain | Complaint threads as demand signal, not proof. Volume over sentiment. |
Scores anchor to a calibration ladder of real products (the "6.6 attractor" = real revenue, crowded market). New models and prompt changes run a blind-recheck protocol — flag at Δ ≤ −1.5 — before they touch production. A 7-model public bake-off (2026-08-01) decided the current architecture; the results are published in the repo.
A collector once mis-paired sponsor names to favicons, manufacturing products that didn't exist with phantom revenue. Caught when a name didn't match its logo. Fix: sponsor filter + duplicate-favicon guard + an audit comparing raw source data to stored names. The wrong rows were deleted publicly.
Apple's review feed returns empty results at random. An early version cached the empty result and concluded "no complaints exist" — a demand-killing false negative. Fix: empty is treated as an error, never an answer; dry apps retry on the next cycle.
Models asked to invent opportunities score them ~1 point higher than adversarial verification allows. Every generated candidate — including our own daily ideas — now goes through the unchanged harsh referee. When nothing clears 7.0, we publish "closest, and here's what kills it." The misses stay in the archive.