A universal AI infrastructure layer that ingests behavioral signals, infers real-time psychographic state, and delivers adaptive interventions — across web, mobile, game, and enterprise environments.
Four-stage pipeline from raw behavioral exhaust to closed-loop adaptive intelligence.
Continuously updated runtime state layer representing real-time cognitive and behavioral trajectory per user.
Queried at decision time by downstream engagement systems.
Every feature is built to serve the inference-to-intervention pipeline.
Multi-layer AI that processes behavioral event streams in real time to produce probabilistic trait vectors covering motivation, risk, emotion, and cognition.
Universal SDK accepts behavioral signals from web, mobile, game engines, IoT, and CRM systems. Structured via a unified event schema for immediate processing.
Performs real-time state queries against the Trait Store to evaluate decision thresholds and behavioral trajectory, mapping inferred state to downstream engagement interventions with precision timing.
Native A/B and multivariate testing with contextual bandit optimization. Each intervention generates outcome data used to update engagement policy via reinforcement feedback.
Run deep psychographic clustering, churn prediction, cohort comparisons, and behavioral trend analysis across your entire user population on demand.
Updates behavioral policy based on observed response to deployed interventions — without requiring offline retraining or manual model updates.
Dynamically sequences user journeys based on live psychographic state, adapting content, timing, and channel selection to each user's inferred cognitive profile.
Native connectors for HubSpot, Salesforce, Segment, GA4, Meta CAPI, Google Ads, AWS S3/EventBridge, Azure Blob, Shopify, Magento, Pipedrive, and Zoho CRM.
Full audit logging, GDPR-aligned data request handling (export + deletion), consent management, and role-based access controls for enterprise data governance.
Built for teams that need to understand the psychology behind behavior — not just the behavior itself.
Optimize conversion funnels using real-time psychographic state instead of demographic proxies.
Adapt game narrative, difficulty, and reward systems to player cognitive and emotional state.
Personalize pricing display, urgency signals, and product sequencing by user risk and loss aversion profiles.
Drive feature adoption and reduce churn by detecting cognitive overload and motivation decay early.
Improve ROAS by targeting based on real-time inferred intent rather than static audience segments.
Integrate psychographic trait vectors as input features into downstream recommendation and personalization models.
Understand behavioral intent behind product interactions without surveys or explicit feedback collection.
Apply operator psychographic state to adaptive safety systems and dynamic interface complexity management.
Psychographic intelligence applied across industries and product categories.
Detect loss-aversion and price-sensitivity in real time. Adapt urgency messaging, social proof placement, and checkout flow to match the user's inferred psychological state.
Adapt difficulty curves, narrative pacing, and reward cadence based on player engagement volatility and novelty-seeking scores. Reduce churn by detecting frustration before drop-off.
Replace static audience segments with live psychographic state vectors. Bid on users when their decision_velocity_index peaks and trust_acquisition_threshold is lowest.
Identify cognitive overload and motivation decay signals during onboarding. Trigger adaptive tooltips, simplified flows, or human outreach at the precise moment of risk.
Sequence push notifications and in-app moments using inferred energy level and engagement volatility. Reach users when receptivity is highest, not just when a timer fires.
Modulate AI assistant tone, depth, and pacing based on cognitive load tolerance and narrative receptivity. Reduce abandonment and increase task completion in conversational interfaces.
Measured outcomes from psychographic-driven interventions vs. traditional demographic targeting.
| Metric | Baseline | With knXw | Lift |
|---|---|---|---|
| ROAS | 1.8× | 4.2× | +133% |
| CPA | $48 | $29 | -40% |
| CVR | 2.1% | 3.9% | +86% |
| CTR | 1.4% | 2.9% | +107% |
| Impression-to-Intent | 8% | 19% | +138% |
| Metric | Baseline | With knXw | Lift |
|---|---|---|---|
| D30 Retention | 24% | 41% | +71% |
| Churn Rate | 8.2% | 5.9% | -28% |
| Feature Adoption | 31% | 54% | +74% |
| Session Length | 4.2 min | 7.8 min | +86% |
| LTV | $180 | $440 | +144% |
Continuous behavioral policy optimization via reinforcement feedback — no offline retraining required.
Each intervention generates outcome data used to update engagement policy via reinforcement feedback. The system continuously optimizes behavioral policy with every user interaction.
Each component maps directly to an implemented system artifact.
Structured representation of behavioral interactions (clicks, purchases, feature usage). Normalizes heterogeneous telemetry into a unified signal format for downstream inference.
Processes behavioral data streams to estimate latent psychological and decision-making traits. Produces probabilistic trait vectors per user updated continuously in real time.
Persistent database containing inferred user psychographic runtime state. Serves as the low-latency runtime state interface queried at decision time by Trigger Engine and downstream engagement systems for intervention selection.
Performs real-time state queries against the Trait Store to evaluate decision thresholds and behavioral trajectory. Determines when and how engagement interventions are deployed.
Evaluates intervention effectiveness across psychographic cohorts using multivariate testing and adaptive bandit algorithms to continuously surface optimal engagement policies.
Records behavioral changes following system-driven interventions. Provides outcome data that feeds the online adaptation loop for continuous behavioral policy optimization.
Adaptive algorithm that selects optimal intervention actions based on observed user context and reinforcement feedback signals. Balances exploration of new strategies with exploitation of proven ones.
Cross-session and cross-device user identity resolution layer that unifies behavioral signals into a coherent per-user psychographic runtime state across all touchpoints.
Enterprise-grade security, privacy, and reliability built into the core architecture.
Real-time event ingestion with ultra-low latency for immediate psychographic state updates.
Encrypted at rest and in transit. HMAC-signed event payloads. Role-based access controls throughout.
Data export and deletion request workflows. Consent management layer. Full audit log trail.
JavaScript, REST API, and mobile SDKs. Game engine integrations. IoT event endpoints.
Continuous behavioral policy optimization via reinforcement feedback. No offline retraining cycles required.
Native AWS S3, Azure Blob, EventBridge, and BI export connectors for enterprise data pipelines.
Live dashboard with continuous trait and event stream updates. No manual refresh required.
Webhooks, REST API v1, and native CRM/ad platform connectors for seamless ecosystem embedding.