User Verification & ID Checks
Strengthen trust and security with advanced document verification, fraud flagging, and compliance checks to ensure every user is authentic and meets platform standards.

Spam & Bot Detection
Identifying fake accounts, automated bots, and suspicious posting patterns that disrupt user experiences and damage credibility.

Brand Safety & Ad Quality Review
Review ads and placements to guarantee alignment with brand rules, audience expectations, and industry standards.

Fraud Auditing & Verification
Mitigate financial and reputational risks through transaction reviews, account-level analysis, and proactive fraud prevention strategies.

Low-Severity Content Moderation
Filtering out profanity, harassment, and straightforward policy violations, keeping online spaces safe and respectful.

Bias Detection in Data
Label training datasets to identify and reduce gender, racial, and cultural bias in AI systems.

Toxic Content Tagging
Detect and annotate harmful language, hate speech, or inappropriate content to strengthen AI safety.

Context-Aware Annotation
Apply nuanced labels that help AI distinguish between context-driven safe and unsafe usage.

Vulnerability Testing
Design and execute prompts to expose weaknesses in generative AI systems.

Harmful Output Simulation
Stress-test models with adversarial prompts to detect unsafe, biased, or harmful responses.

Red Team Evaluation
Perform systematic red teaming exercises to uncover risks before deployment.

Hallucination Reduction
Validate AI-generated outputs against trusted knowledge sources to reduce factual inaccuracies.

Policy Compliance Checking
Ensure AI outputs meet ethical, legal, and organizational standards.

Human-in-the-Loop Review
Combine automated checks with expert human validation for high-stakes content.

Discrimination Detection
Identify bias in model predictions across demographic groups.

Fairness Assurance
Run audits to ensure equitable treatment across use cases and applications.

Ethical AI Review
Evaluate models against fairness and inclusivity benchmarks to promote responsible AI adoption.

Performance Against Safety Standards
Evaluate AI models against predefined safety benchmarks to ensure reliability.

Risk Assessment Frameworks
Assess potential misuse scenarios and unintended consequences of deployment.

Ongoing Safety Audits
Continuously monitor and audit models post-deployment to detect new risks.