aid: beesafe-ai accessModel: pricing: unknown onboarding: unknown trial: false try_now: false public: false label: Unknown confidence: low source: [] generated: '2026-07-22' method: derived image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/beesafe-ai.png name: Beesafe Ai description: >- Beesafe AI is a fraud-prevention and anti-scam platform whose tagline is "Stopping Scams Before They Reach Your Customers." Its AI-powered anti-scam agents proactively engage scammers to uncover the fraudulent accounts, money-mule networks, and criminal financial infrastructure behind trust-based scams such as romance scams, pig-butchering, and investment fraud, which bypass traditional detection by convincing victims to authorize transactions themselves. Beesafe serves financial-services firms, cryptocurrency companies, telecommunications providers, and government agencies. It was founded by AI and security PhDs from Carnegie Mellon and UC San Diego and is backed by Y Combinator, Obvious Ventures, and SBIR/STTR funding. The platform is currently in early access with no public self-service API, developer documentation, or pricing published. url: https://raw.githubusercontent.com/api-evangelist/beesafe-ai/refs/heads/main/apis.yml x-type: company x-source: vc-portfolio x-backed-by: - y-combinator - obvious-ventures - sbir-sttr x-tier: stub x-tier-reason: portfolio-lead specificationVersion: '0.20' created: '2026-07-17' modified: '2026-07-18' tags: - Company - Fraud Prevention - Anti-Scam - Fraud Detection - Security - Artificial Intelligence - Financial Services apis: [] common: - type: DomainSecurity url: security/beesafe-ai-domain-security.yml - type: Website url: https://beesafe.ai - type: PrivacyPolicy url: https://beesafe.ai/privacy - type: Support url: mailto:founders@beesafe.ai - type: LLMsTxt url: llms/beesafe-ai-llms.txt maintainers: - FN: Kin Lane email: kin@apievangelist.com - FN: APIs.json email: info@apis.io x-enrichment: date: '2026-07-19' status: backfilled pass: local-v1 note: backfilled from .gitignore signal + verified work evidence