Solutions

Decision Support

Actionable intelligence output. The system delivers decisions, not dashboards.

We Do Not Deliver Dashboards. We Deliver Decisions.

The Decision Support layer surfaces anomalies, identifies risks, and recommends actions. It does not ask "what happened?" It answers "what must be done?"

Decisions emerge from the cognitive model continuously. The system monitors the kinetic graph, detects patterns, and surfaces actionable intelligence in real-time.

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Anomaly Detection
Surface deviations from expected patterns
Risk Identification
Identify potential threats before they materialize
Action Recommendations
Prescribe specific actions based on the model

Anticipatory Intelligence

The system anticipates insights before problems occur, not after.

Real-Time Anticipation

Events are processed as they occur, not after collection. Pattern matching happens on partial sequences, enabling predictions before outcomes.

Latency: <100ms

Graph RAG Intelligence

Natural language queries powered by Graph RAG. Ask "Why are users abandoning carts?" and receive insights with evidence nodes and confidence scores.

Powered by Inputless DB + LLM

Pattern-Based Forecasting

Recognize sequences that predict outcomes with 90%+ accuracy

Early Signal Detection

Identify leading indicators 30-60 seconds before outcomes occur

Predictive Scoring

Continuous probability calculation with real-time threshold alerts

Decision Types

The system generates multiple types of actionable decisions autonomously.

Anomaly Alerts

Real-time alerts when behavior deviates from expected patterns. Includes root cause analysis and suggested fixes.

Examples:
Unusual transaction patterns
Performance degradation
User frustration indicators

Risk Assessments

Proactive risk identification before problems materialize. Confidence scores and evidence-based recommendations.

Examples:
Cart abandonment risk
Churn probability
Security threat detection

Optimization Recommendations

Actionable suggestions to improve outcomes. Based on learned patterns and predictive models.

Examples:
A/B test recommendations
Pricing optimization
Content personalization

Intervention Triggers

Automatic intervention recommendations when user behavior indicates need for assistance.

Examples:
Support chat triggers
Discount offers
Checkout assistance