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AI Factor Model Stock Screener

Hacker News

AI Factor Model Stock Screener

I built Sophistia because traditional stock screeners only filter financial ratios, and none of them let you screen companies by deeper business characteristics or narrative factors. Sophistia lets you type any factor in plain English - e.g., “AI datacenter exposure”, “high switching costs”, “mission-critical supplier”, “tariff sensitivity”, “recurring-revenue strength”, “rare earths value chain”, “Trump trade war beneficiaries”, “AI-disruption risk”, etc. You can describe in detail what the factor should evaluate and choose the scoring scale. The system scores thousands of companies from 0–10 on each factor using LLMs with structured context. Companies with the highest total scores rise to the top of your custom thematic watchlist. It’s essentially a factor-model stock screener for retail investors: you define the narrative, factors, characteristics, or thesis, and the tool finds the companies that actually match it. I built this after realizing how difficult it is for retail investors to identify companies that benefit (or suffer) from a trend without already knowing the entire universe of stocks. Hedge funds solve this by having the resources to track all the stocks; retail investors don't. This is my attempt to bridge that gap. This is not a tool for deep, company-specific analysis but rather a way to surface the companies that exhibit the characteristics you’re looking for and may deserve a closer look. This is very much a v1: right now it only covers SEC companies and the contextual data is still limited. Feedback from anyone familiar with active trading, screening tools, or factor models would be extremely helpful. This is a completely solo-founded project that I built myself.

Visit productView on Hacker NewsNov 23, 2025
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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting
90%90% 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.
TrustMRRFits verified-revenue profile · Strong signals: ios, trading, way · Missing: mobile apps, personal, entrepreneurs
67%67% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, context · Missing: mac, agents, macos
66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: lua, ide, io · Missing: https docs, excited, just released
50%50% 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 · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr, revenue, recurring · Missing: mrr, profit, saas
20%20% 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.

Incorrect prediction on native model

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