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InsyteSage – AI data analysis from plain English

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InsyteSage – AI data analysis from plain English

I built InsyteSage ( https://insytesage.ai ) to solve a common problem I faced: business teams waiting on data analysts for every analysis request, and non-analysts struggling to start meaningful data exploration. How it works: - Upload data (or use provided public datasets like NSW property sales) - Describe your columns - Ask your business question in plain English (e.g. "compare user engagement in different regions") - Get a structured analysis plan with visualizations and statistical insights - Refine analysis through natural language prompts Technical details: - Uses LLMs to understand business context and generate analysis plans - Implements statistical methods for hypothesis testing - Generates visualizations based on data characteristics - Built with Python/FastAPI, supabase backend, React frontend You can try it immediately with public datasets. Just create an account for free and create an analysis using any of the public datasets. Looking for feedback especially on: 1. Analysis quality and relevance 2. Natural language interaction 3. Types of analyses you'd want automated This is my first substantial project after years of building data pipelines and analytics systems. Built it because I believe data analysis should be more accessible to business teams.

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: user, context, visual · Missing: mac, agents, macos
88%88% 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
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
30%30% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
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 · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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