Launch Intel
Cu

Custom LMS

Acquire.com

A custom-built corporate LMS with over 11 years of operating history, currently serving three sticky enterprise clients across multiple industries and generating $83,195 in annual recurring revenue. The platform supports SCORM 1.2, video, PowerPoint, and document-based training, along with real-time completion tracking, analytics, and automated assignment workflows.

A custom-built corporate LMS with over 11 years of operating history, currently serving three sticky enterprise clients across multiple industries and generating $83,195 in annual recurring revenue. The platform supports SCORM 1.2, video, PowerPoint, and document-based training, along with real-time completion tracking, analytics, and automated assignment workflows. All three clients have remained on the platform for 6-8 years with zero churn, and day-to-day operations are handled by a part-t...

Visit productView on Acquire.comSaaS
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Actual performance

$6,916/yr
Made the leaderboard

Traction signals

Asking price$75,000
Annual revenue$83,000
Revenue multiple0.9x

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created
91%91% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Acquire.comFits revenue-generating acquisition profile · Strong signals: revenue, recurring, training · Missing: arr, mrr, profit
69%69% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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