Detect account takeovers, credential abuse, and bot activity in real time by analyzing how users actually behave. On-device scoring, zero data egress, cross-platform baseline consistency.
ValorQ captures signals across timing, interaction, motion, and flow. The moment behavior shifts from a user's established pattern, the engine knows.
Time spent on each screen is measured against each user's personal baseline. Anomalously fast progression triggers a signal immediately.
Per-step · Per-user baselineHow credentials are entered reveals intent. Automated input patterns on sensitive fields are flagged with no warm-up period required.
Zero latency · No warm-upTotal flow completion time is measured against rolling baselines. Sessions completing flows at superhuman speed trigger high-confidence anomaly signals.
Flow-level · Rolling baselinePhysical device behavior is analyzed for signatures consistent with human use. Non-human environments exhibit characteristic patterns no legitimate user produces.
Device sensors · EnvironmentThe way a user navigates UI elements is compared against their own history. Legitimate users show natural consistency. Attacker-controlled sessions do not.
Touch · Navigation · RhythmScreen transition sequences and dwell times are compared to expected flow paths. Compromised sessions navigate with a mechanical linearity no real user exhibits.
Screen sequence · Flow pathA session deviating significantly from an established user profile is treated as high-risk. Account takeover is detected even when credentials are valid.
User lifetime · Profile deviationActive signals are combined into a single composite score from 0 to 100, updated continuously as the session progresses. One number drives one clear action.
Weighted · Confidence-adjustedValorQ sits alongside your existing security stack. It adds a behavioral signal layer that WAF, device fingerprinting, and transaction monitoring cannot produce on their own.
All signal layers score locally before any network call. Risk callbacks fire immediately, so friction is applied before the transaction reaches your backend.
With backend mode, baselines sync across device upgrades. A user's behavioral fingerprint persists even when they switch devices.
Your backend can query session risk via API before authorizing any transaction. Defense in depth, on your terms.
Four native packages. One unified scoring model. Baselines are platform-specific by design — behavioral patterns differ across surfaces, and ValorQ accounts for that automatically.
Kotlin-native with Jetpack Compose first-class support. Encrypted local storage, coroutines throughout, and a StateFlow interface for reactive UI integration.
Swift Concurrency throughout. Actor-isolated scoring engine, Keychain storage, and SwiftUI ViewModifiers for seamless integration into existing screens.
TypeScript package with React components. Local baseline persistence, automatic session tracking, and React hooks for direct risk state binding.
Extends the core client with native storage and AppState lifecycle management. Platform resolves automatically so behavioral baselines remain correctly separated.
Three integration depths. SDK-only for evaluation. Full deployment for enterprise-grade session security.
All scoring happens on-device. Zero network calls. Ideal for privacy-sensitive environments, initial evaluation, and teams who want frictionless integration before any backend work.
Events stream to your backend instance. Server rescores against full user history. Baselines persist across device upgrades. Your backend can query risk before authorizing transactions.
The complete observability layer for enterprise security teams. Session lifecycle tracking, live operations dashboard, fleet analytics, and webhook alerting in one platform.
ValorQ's risk engine is built on well-established statistical methods, not opaque ML models. Every verdict is statistically robust, auditable, and grounded in how real users actually behave.
Behavioral biometrics done the way regulated, privacy-conscious teams actually need it.
Layers on top of your existing security stack — device fingerprinting, WAF, transaction monitoring — adding a behavioral signal none of them can produce alone.
One scoring model across Android, iOS, Web, and React Native. Baselines carry across device upgrades within a platform.
Scoring happens locally in the SDK-only model. No behavioral data leaves the device, reducing your compliance scope from day one.
Risk is scored against each user's own established behavior, not a population average — sharper detection with fewer false positives.
Multi-tenant by design with strict tenant isolation, so behavioral data and baselines never cross customer boundaries.
ValorQ is architected for BFSI, fintech, wealth management, and insurance teams that answer to auditors. Privacy posture is a design constraint, not an afterthought.
The SDK-only model keeps behavioral data on-device, so it never enters your data-processing boundary.
ValorQ scores how users behave — no PII is transmitted for on-device scoring.
Every risk score traces to specific signals and thresholds — no opaque model to defend to a regulator.
ValorQ integrates in days, not quarters. It layers on top of what you already have: device fingerprinting, WAF, transaction monitoring. It adds the behavioral signal layer that reveals when an account has been taken over.
See ValorQ running against a live production flow. We will walk through signals, risk scoring, and integration paths for your specific platform mix.
Schedule with ValorQ →Start with Model 1. No backend required. Get SDK packages for your platform and see behavioral signals fire in your test environment before any infrastructure commitment.
Request SDK access →For enterprise teams evaluating ValorQ alongside existing fraud infrastructure. We map your current stack and show exactly where behavioral biometrics adds signal.
Talk to our team →Native SDKs for Android, iOS, Web, and React Native. Start with the SDK-only model — no backend required to see signals fire.
Request SDK accessWalk through signals, scoring, and compliance posture with our team, mapped to your existing fraud and identity infrastructure.
Book a demo