AdYield Vision

Predicts CTR and conversion performance of AI-generated video ads before spend using proprietary creative-to-outcome data; serves DTC and e-commerce media buyers. · AI video ad prediction

DEAD SLOW · CAUTIOUS

6.2 ShipScore / 10

Strong time-leverage; weak control — ad-platform APIs gate the outcome data.

Condition: You must secure a labeled creative-to-outcome dataset (real spend + results) big enough to beat a media buyer's gut on held-out ads — and prove it with a blind backtest before writing product code.

Part I — The Verdict what the instruments read

01 — The ShipScore breakdown

Control

C5 — creative-to-outcome data depends on Meta/TikTok ad API access.

Entry barriers

E5 — Laper widening +44%/mo, but prediction data moat is unbuilt.

Need

N6 — Laper's $40k verified, yet zero chatter on prediction tools specifically.

Time-freedom

T8 — inference-based SaaS scoring, no per-customer manual labor.

Scale

S7 — global DTC/e-com media buyers, but a buyer-side niche.

What the five commandments measure

C — Control	Do you own the customer, or does a gatekeeper stand between you and revenue?
E — Entry	How hard is this to copy once it works? A rival's existence isn't the question — the durability of their advantage is.
N — Need	Is demand proven with money rather than vibes?
T — Time	Can income detach from your hours, or did you buy yourself a job?
S — Scale	How far does it reach without linear cost?

Scored on the evidence below, against CENTS as we've adapted it plus a Buffett-style moat reading of every rival — the same referee for every idea, including the ones we generate ourselves. The verdict follows the arithmetic: we never relabel a score to make it read better, and the weakest commandment is always named. The exact rubric stays in-house so the scale can't be written for.

02 — The pace test — $1M/yr

At $199/mo (assumed category price), you need 419 subscribers for a $1M/yr pace.

Nearest tracked rivals on that curve:

Laper

$40k 49%

↑ moat widening · +44%/moX · quiet

A rival's moat trend is its verified-revenue trajectory, not its rating — a 4.9★ incumbent with eroding revenue is a different opponent than a widening one. How it's computed →

Part II — The Evidence the receipts, good and bad

03 — Rival scan — corpus tracks 1 similar

Laper

verified MRR $40k · CENTS 5.6 · revenue trend: widening (+44%/mo) · X-chatter: low (trustmrr)

↑ moat widening · +44%/mo X · quiet

Laper — Only tracked peer, $40k verified MRR growing +44%/mo — compounding into the lane fast.
Decode (Entropik) — X-mentioned pre-spend creative insight with heatmaps/emotion; already owns the 'predict before spend' pitch.
Motion-style creative analytics — Post-hoc creative reporting incumbents can bolt prediction on with data buyers already share.

04 — Demand evidence

Laper: verified $40k MRR, revenue trend widening +44%/mo — real money in adjacent creative tooling.
Reddit: zero signals found for these keywords — no organic pain evidence.
X: chatter on prediction SaaS 'essentially absent'; buyers do manual/AI-assisted iteration instead.
No funded companies tracked in/near the space — neither validation nor crowding.

05 — Risks

Cold start: no proprietary outcome data on day one, so predictions are guesses.
Prediction accuracy is falsifiable — one bad forecast and the buyer churns.
Media buyers may prefer cheap live testing over paying to predict.
Meta/TikTok API policy limits on pulling account-level creative performance data.

06 — Kill switches — what kills this with one decision

Meta Marketing API permission tiers — Restricting creative+results access starves both training data and the product.
Ad platforms shipping native pre-spend creative scores — Free, better-data incumbent feature makes a paid predictor redundant overnight.

Part III — The Plan if you insist on proceeding

07 — What this scan can't tell you

Cannot tell whether Laper actually does prediction or merely adjacent generation/analytics.
No visibility into achievable prediction accuracy versus a buyer's baseline judgment.
Unknown whether DTC buyers will share ad-account data with a third party.

Scores judge the market and business model — not your ability to execute. Verified MRR = independently tracked revenue; reviews/upvotes = platform-reported. Judged 2026-09-28.

08 — The wedge & the next step

Start as a free creative-results connector for DTC accounts; harvest the outcome dataset, then sell prediction once accuracy is provable.

Cheapest next step: Get 5 DTC buyers to hand over 90 days of past creatives and results; blind-predict the winners and show them the hit rate.

Not quite it? Spin the model

Business-model variations of this idea, engineered to beat 6.2 — different vertical, audience, model, or wedge. Same harsh scale. Pro feature.

One evidence-backed gap a day, free

Close — but conditional. Every morning the engine files one mined gap, scored on the same harsh scale as this one. Free at 7am AEST, unedited.

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