MoverisLive analyzes streaming video from customers' own webcams and produces a Human Likelihood Score in a matter of seconds.
Once customers turn on their webcam, MoverisLive works in the background while customers create an account or complete an application.
Integrates seamlessly into existing KYC and AML workflows without being slowed down by clunky, time-consuming checks.
The foundation of MoverisLive is Human Signal Intelligence - the science of capturing the body's natural physiological signals that deepfakes can't imitate. It's psychophysiology, not biometrics. While AI can fully defeat voiceprint and other biometrics, it cannot reliably imitate the psychophysiology captured by Human Signal Intelligence.
All it takes is a single metric, trained on thousands of human and AI-generated models, to know whether a customer responds in a way that only a real human can.
From KYC (Know Your Customer) and AML (Anti-Money Laundering), to wire transactions and account security, MoverisLive reduces the threat of AI-generated fraud at points in the workflow that work best for you.
How does Moveris ensure privacy and compliance (GDPR, CCPA, etc.)?
Privacy is fundamental to our architecture. We employ data minimization principles—our system extracts anonymized physiological signal data that cannot be reconstructed into personally identifiable information, without storing raw video or audio by default. All processing can be configured for on-premise deployment or region-specific servers to meet GDPR, CCPA, HIPAA, and other regulatory requirements. We maintain SOC 2 Type II compliance and can provide detailed compliance documentation upon request.
How do you address bias in measurement?
We proactively test our models across diverse demographic groups, age ranges, and environmental conditions to minimize bias. Because our approach analyzes dynamic psychophysiological signals—real-time indicators of human presence—rather than static features like facial structure, it is inherently less vulnerable to the racial and gender biases common in traditional facial recognition systems. We conduct ongoing bias audits with third-party validation and continuously update our models to maintain fairness across all populations.
Does Moveris store or share user data?
We never share, sell, or monetize personal data. Clients maintain full control over data retention policies and can configure the system for zero data storage if preferred. When retention is required for compliance purposes, all data is encrypted end-to-end with AES-256 encryption, and only derived, de-identified metrics are used for analysis. Our zero-trust architecture ensures data remains within your designated geographic boundaries.
How easy is integration with our existing systems?
Integration is designed for minimal disruption. Our RESTful API can be implemented into existing workflows—including onboarding, KYC, identity verification, or access control—typically within 2-4 weeks without requiring new hardware or significant infrastructure changes. Many clients deploy Moveris as a drop-in replacement for existing verification screens. We provide comprehensive documentation, SDKs for major platforms, and dedicated integration support throughout the process.
How does the system handle scale and high-volume processing?
Our cloud-native architecture scales elastically to handle anywhere from dozens to millions of daily verifications. The system auto-scales based on demand with sub-100ms response times maintained even under peak loads. For enterprise deployments, we offer dedicated processing clusters and can guarantee specific SLA performance levels. Our distributed processing approach ensures no single point of failure while maintaining consistent accuracy at any scale.
What are the technical requirements and limitations?
Moveris works with standard webcams and mobile device cameras (minimum 720p resolution recommended). The system performs optimally with stable internet connections and processing requires 2-5 seconds per verification. Our lightweight client-side components have minimal impact on device performance and work across all major browsers and mobile platforms.
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