Launch Intel
Apogee

Apogee

TrustMRR

Apogee lets Meta Ads media buyers, agencies and consultants bulk-upload hundreds of ad creatives in minutes, the painful workflow Meta's native tools never solved. Plus AI copywriting, campaign Autopi

Apogee lets Meta Ads media buyers, agencies and consultants bulk-upload hundreds of ad creatives in minutes, the painful workflow Meta's native tools never solved. Plus AI copywriting, campaign Autopilot, comment analysis and funnel analytics. A focused B2B SaaS for the fast-growing French paid-social market: recurring revenue, high gross margins, and a 30% lifetime affiliate program. Sticky, automated, and ready for its next operator to scale.

Visit productView on TrustMRRFeb 1, 2026MarketingSoftwareB2B
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Actual performance

42customers
$3,976MRR/mo
Made the leaderboard

Traction signals

Domain Rating5
MRR growth 30d+20.9%

Launch Intel predictions

Analyze your own launch →
TrustMRRFits verified-revenue profile · Strong signals: agencies, creatives, ai · Missing: api, 000, profitable
96%96% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, created, podcasting
81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: revenue, margin, saas · Missing: arr, mrr, profit
47%47% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus, b2b · Missing: reviews, intuitive, videos
33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, just released, open source
25%25% predicted probability of success on Hacker News, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: automated · Missing: web3, just like, small businesses
16%16% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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