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Reflexive AI Intelligence hub replicating deep human investigations

Hacker News

Reflexive AI Intelligence hub replicating deep human investigations

We built Golden Owl because the data that matters to strategy, like leaks, emerging competitors, reputation hits, and regulatory shifts, is scattered and hard to catch in time. Golden Owl uses a network of AI agents to monitor and analyze data across the open, deep, and dark web. It pulls from non-indexed sources like court filings, breach dumps, sanctions lists, forum threads, and social media. Then it de-duplicates, merges, and ranks everything in real time. What it does: Discover: Finds hidden documents, domains, leaks, forum posts, and more from hundreds of OSINT sources. Analyze: Uses AI to extract entities, map relationships, detect anomalies, and identify trends. Monitor: Sends live alerts on brand mentions, competitor actions, geopolitical shifts, and supply chain risks. Compare: Provides side-by-side dashboards for companies, markets, or investments. Under the hood: ~200 AI agents act like digital investigators at scale. Custom scrapers + Tor relays for deep/dark web access. Vector graph store for relationships and clustering. API with 100+ endpoints offering both raw and enriched data. All data stays private—no public indexes, no shared queries. We're live at https://goldenowl.ai Would love feedback on: Usability and UI Data coverage and freshness Anything you'd expect from an OSINT tool but don't see Thanks!

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best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, open · Missing: mac, macos, cursor
77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · 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 · Missing: plus, platform, intuitive
37%37% 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, io · Missing: https docs, excited, just released
34%34% 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
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: real time · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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