Au

Automation-First Commerce OS for Enterprise Retailers

Acquire.com

An enterprise Commerce OS with the market’s fastest listing engine and a built‑in B2B/wholesale portal. Connects to major marketplaces, manages 500–50k SKUs, and automates inventory, orders, and pricing to boost speed and profit.

An enterprise Commerce OS with the market’s fastest listing engine and a built‑in B2B/wholesale portal. Connects to major marketplaces, manages 500–50k SKUs, and automates inventory, orders, and pricing to boost speed and profit. Backed by $2.18M+ R&D, it uses automation to remove manual work. With 0% churn, 100% recurring revenue, and 53% margins, it’s a plug‑and‑play asset ready for a sales‑led scale‑up. Key Highlights ✅ ARR $381k; TTM profit $67.7k; 12% growth ✅ 0% churn across sticky en...

Visit product View on Acquire.com SaaS

Actual performance

$31,750/yr

Made the leaderboard

Traction signals

Asking price$1,100,000

Annual revenue$381,000

Revenue multiple2.9x

Launch Intel predictions

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Acquire.comFits revenue-generating acquisition profile · Strong signals: arr, revenue, profit · Missing: mrr, saas, profitable

82%82% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

38%38% predicted probability of success on Hacker News, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

28%28% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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