BrandRetina – screenshot similarity API for spear-phish detection

Hacker News

Hi HN folks, I’m building BrandRetina (brandretina.ai), an API-first service to catch high-fidelity spear-phishing that looks like your real internal portals.. Most scanners are great at "bad code" (malware/reputation), but lots of modern phishing is just a clean site that visually impersonates Okta/SSO/Workday/customer portals. SOC teams often end up doing the manual step: open urlscan screenshot -> eyeball it -> decide. What BrandRetina does: - You onboard a "Golden Registry" (verified screenshots of your real portals) - When a suspicious link comes in, your SOAR detonates it (e.g., urlscan.io) and gets a screenshot UUID - BrandRetina compares the screenshot against the Golden Registry (visual embeddings / similarity) and returns: 1) verdict (CLEAN/SUSPICIOUS/MALICIOUS) 2) similarity score 3) target portal + evidence flags (logo/layout/color/form signals) Why it’s useful: - Flags lookalikes even when the HTML/code is completely different - Removes the repetitive screenshot-review step - Fits existing workflows (SOAR/SIEM) instead of replacing them I'd love feedback from SOC/IR/SecOps folks: - Are screenshot-based verdicts something you’d trust in an automated playbook? - What evidence signals would make this actionable for you? - What sources besides urlscan would you want supported? Docs: https://brandretina.ai/docs API base: https://api.brandretina.ai

Visit product View on Hacker NewsDec 26, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: visual, code, open · Missing: mac, agents, macos

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Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

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Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

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AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: scanner · Missing: mobile apps, ios, personal

25%25% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

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