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Notle.ai: AI CDSS for Psychologists/Psychiatrists

Hacker News

Notle.ai: AI CDSS for Psychologists/Psychiatrists

Hello all, been working on this for almost two years now. Notle is a workspace for psychologists to do things like note documentation, intake assessments, psychometric assessments and insurance reviews. We also offer features such as therapist feedback, deployable AI chatbots, session recaps for patients and psychometric forecasting. Using the psychometrics generated each session Notle uses predictive capabilities to notice compounding issues before they exacerbate. To be honest, when building this I did so in a way that would probably be considered a red flag for most - I didn't talk to many users, I initially built it just to explore how stable and accurate AI could generate psychometrics for things like depression, anxiety, etc. I built it more in the name of "science" than what the market is actually demanding. So the process has been up and down as far as finding our market fit. That being said, I still believe we are on the forefront of the future with what we are doing and AI will be able to aid in the treatment and diagnosis of patients in the psych field. I'd love any feedback, thanks.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
84%84% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, using, apis · Missing: mac, agents, macos
58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users, way · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: reviews, users · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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