Cairos provides a secure synthetic data pipeline that generates adversarial, policy‑aligned content to stress‑test AI models for child‑safety risks across text, voice, and vision modalities.
Funding
Funding not disclosed
Founders
Product
Problem
AI systems increasingly expose children to harmful content such as grooming, coercion, and exploitation, yet existing testing methods are inconsistent, often require human reviewers to view disturbing material, and lack comprehensive coverage across text, voice, and vision modalities.
Solution
Cairos offers a secure synthetic data pipeline that creates adversarial, policy‑aligned content to stress‑test AI models for child‑safety risks. By using controlled agentic systems, the platform simulates grooming, coercion, and exploitation scenarios without exposing humans to real harmful material. Generated datasets span text, audio, and visual inputs, enabling detection, classification, and prevention of child‑safety harms throughout the model lifecycle. The service returns model stress‑test results, policy‑mapped findings, and audit‑ready reports that align with safety frameworks and support compliance and governance efforts.
Target Audience
Primary customers are AI product teams, safety engineers, and compliance officers at large technology firms and AI labs that need systematic, low‑risk testing of child‑safety safeguards.
Features
- Agentic adversarial generation of synthetic child‑safety threat scenarios across text, voice, and vision modalities
- Policy‑aligned data output mapped to established safety frameworks for easy interpretation
- Comprehensive failure‑mode detection and stress‑testing without human exposure to real harmful content
- Tailored evaluation datasets that reflect specific deployment contexts and interaction types
- Audit‑ready reporting package covering safety findings, compliance evidence, and governance documentation