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DataMotto – Proactive AI Data Analyst

Hacker News

DataMotto – Proactive AI Data Analyst

Hi HN! I am Ardalan, co-founder of DataMotto ( https://datamotto.com ), which is a proactive AI data analyst. It is an AI tool that enables people ask custom questions about their data and receive immediate data-driven answers. During my PhD in Data Science and years of experience as a data scientist and consultant, I've closely collaborated with individuals who lacked data expertise and relied on others when they need insights for their custom questions. This process was often time-consuming and frustrating. DataMotto was born out of a desire to bridge these gaps and alleviate their frustrations in awaiting data insights. The user journey with DataMotto involves answering data questions one at a time. Here's how it works (1-min video): https://youtu.be/1mnN6yGFj8w Recognizing users' pain points in the last few months, our goal is to continuously improve our AI to better address these three fundamental challenges over time: 1. Improve Data Quality: We found out many users have complex/messy datasets that are not immediately ready for analysis. Feeding such data to an AI can lead to various complications and inaccuracies. Using an AI service, we first scans your data and make a simplified analysis-ready version. 2. Ask Right Questions: Many users struggle to ask the right data questions, possibly because they lack data literacy or find it difficult to formulate their questions for the AI. Before the analysis begins, our AI attempts to understand the user's intentions and can even proactively suggest valuable questions that users may not have considered. 3. Advanced Data Analysis: Once a question is chosen, our AI performs the analysis. It is capable of generating code in Python, R, or SQL to solve the data-related question and subsequently presents the results and actionable insights to the users. While we're happy with DataMotto's current progress, we understand it's only the beginning. Like many early-stage startups, we're aware of gaps in our platform and many areas we need to enhance. Our goal is to transform DataMotto into a comprehensive tool that empowers teams to gain a deeper understanding of their data and move away from gut-based decision-making. As we move forward, we are seeking valuable feedback from this great community. Every piece of advice you provide is highly valuable to us.

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Actual performance

11points
Made the leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, using, code · Missing: mac, agents, macos
90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: video, month, answers · Missing: mobile apps, ios, personal
55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released
42%42% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
9%9% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: collaborate · Missing: web3, chat, crypto
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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