Build 'RadarEdge'—a privacy-preserving, edge-AI radicalization detection engine for mid-market platforms. The platform would: 1) Deploy SLMs (Small Language Models) on-device to analyze conversational context (e.g., 'User A is grooming minors via Discord DMs') without storing raw data; 2) Use federated learning to improve detection across instances (e.g., 'Mastodon.social flags 80% of grooming patterns'); 3) Integrate with FBI’s IC3 API to auto-generate SARs (Suspicious Activity Reports); 4) Provide a 'Radicalization Risk Score' for users (e.g., 'User B: 9/10—immediate review'); 5) Offer white-label compliance dashboards for platforms (e.g., 'Reddit Moderator View').

Mid-market social platforms (e.g., Mastodon instances, niche forums) cannot afford enterprise-grade radicalization detection tools (e.g., Thorn’s Safer, $250K/year). Congress is mandating real-time grooming detection, but smaller players lack multimodal AI (text + images + voice), federated learning for privacy, or automated reporting for law enforcement. Current solutions are either too invasive (e.g., 'scan all DMs') or ineffective (e.g., 'keyword filtering'), leading to fines ($50K per violation under KOSA Act) and reputational damage. Radicalization content on decentralized platforms surged 240% in 2025 (ADL).