Decision Engine
Real-time decisions on live data in the critical path. Fraud signals, alert triage, intent routing, and customer signals at sub-50ms latency.
Four specialist models replace rule engines, cloud NLU platforms, and batch analytics pipelines. Each makes one critical decision in real time: extract fraud signals within the auth window, triage security alerts before analysts see them, route customer intent at conversation speed, and detect churn signals while the customer is still engaged.
4 specialist models
How It Works
One specialist model per critical decision,
running in the hot path at middleware speed
Semantic Intent Routing at Conversation Speed
Regex routing hits 70% accuracy. Cloud NLU platforms deliver better results at 500-900ms and six-figure annual costs. A specialist LFM classifies intent in 15ms with compound-intent handling. New intents deploy via LEAP in minutes, not vendor release cycles. A fraction of the cost for the quality of a managed platform.
Customer Signals Detected While They Are Still Engaged
Churn signals hide in support tickets, chat transcripts, and email threads. Batch analytics platforms detect risk 1-3 days later. A specialist LFM runs in the event stream at 25ms, detecting churn, upsell, and escalation signals in real time. By the time batch systems flag the risk, the model has already routed to retention.
Fraud Signals That Rule Engines Cannot See
Rule engines maintain thousands of regex patterns. Enterprise fraud platforms add hundreds of milliseconds and six-figure costs. Both miss novel attack vectors requiring semantic understanding: gift card splitting, P2P velocity anomalies, synthetic identity indicators. A specialist LFM extracts these signals within the authorization budget.
10,000 Alerts Triaged Before Analysts Start Their Shift
SOC teams receive 10,000+ alerts per day. 95% are noise, only 22% get investigated, each false positive wastes 30 minutes. A specialist LFM triages every alert at under 50ms. One GPU processes 10K alerts in 10 minutes. Analysts stop drowning in noise and start threat hunting.
Try each model
All Demos
Intent Classification
Sub-20ms semantic routing for contact centers and chatbots
15ms semantic routing replaces regex (70% accuracy) and expensive cloud NLU
Customer Signal Detection
Real-time churn, upsell, and escalation signals from every customer touchpoint
25ms signal detection turns every support ticket into a retention, upsell, or routing decision
Fraud Signal Extraction
Extract fraud indicators from transaction data
Semantic fraud detection within the 50ms payment-auth budget
SOC Alert Triage
Reduce alert fatigue with intelligent classification
95% of SOC alerts are noise — LFM filters them in real-time
Ready to deploy in your environment?